Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (103)

Search Parameters:
Keywords = pore-water electrical conductivity

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 6283 KiB  
Article
Assessing the Impact of Graphene Nanoplatelets Aggregates on the Performance Characteristics of Cement-Based Materials
by Ahmed A. Ahmed, Mahmoud Shakouri and Ojo Friday Abraham
Sustainability 2025, 17(6), 2349; https://doi.org/10.3390/su17062349 - 7 Mar 2025
Viewed by 88
Abstract
Graphene nanoplatelet aggregates (GNAs) are a low-cost, low-quality alternative to graphene nanoplatelets (GNPs), characterized by their three-dimensional stacked structure and porous surface morphology. Despite their affordability, limited research has been conducted on the effects of GNAs in cementitious systems. This study investigates the [...] Read more.
Graphene nanoplatelet aggregates (GNAs) are a low-cost, low-quality alternative to graphene nanoplatelets (GNPs), characterized by their three-dimensional stacked structure and porous surface morphology. Despite their affordability, limited research has been conducted on the effects of GNAs in cementitious systems. This study investigates the impact of GNAs on hydration kinetics, phase assemblage, mortar consistency, mechanical strength, bulk electrical resistivity, water absorption, and pore solution pH. Mortar mixtures with 0%, 0.05%, and 1% GNAs by cement weight were prepared using a water-to-cement ratio of 0.42 and cured for 28 days. The results showed that GNAs had minimal influence on hydration kinetics, with no significant changes in hydration products detected by XRD and TGA analyses. Mortar consistency consistently decreased with increasing GNA content. At 0.05%, GNAs had no significant effect on compressive strength or bulk electrical resistivity, whereas 1% GNAs reduced compressive strength by 10%. Water absorption was significantly lower in specimens with 1% GNAs as well, while pore solution pH increased at this dosage. The findings of this study indicate that the incorporation of GNAs at a 0.05% replacement level does not inherently enhance cementitious properties but can influence specific behaviors, such as workability and water absorption, when used at 1% dosages. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Schematics of in situ pH measurement test; (<b>b</b>) experimental setup for pH measurement.</p>
Full article ">Figure 2
<p>SEM images of GNAs at different magnifications: (<b>a</b>) 370×; (<b>b</b>) 700×; (<b>c</b>) 1600×; and (<b>d</b>) 8500×.</p>
Full article ">Figure 3
<p>Particle size distribution (black line) and rug plot (blue vertical lines) of GNAs.</p>
Full article ">Figure 4
<p>(<b>a</b>) SEM image of GNAs. X and Y indicate locations for EDX analysis; (<b>b</b>) EDX spectrum corresponding to X in (<b>a</b>); (<b>c</b>) EDX spectrum corresponding to Y in (<b>a</b>).</p>
Full article ">Figure 5
<p>Influence of GNAs on cement hydration: (<b>a</b>) normalized heat flow for the first 72 h of the hydration process; (<b>b</b>) 10-day normalized cumulative heat flow.</p>
Full article ">Figure 6
<p>XRD patterns of 10-day paste samples made with GNA replacements of 0, 0.05, and 1 percent (E: ettringite, P: portlandite, C: calcite, CS: calcium silicate hydrates).</p>
Full article ">Figure 7
<p>TGA (solid lines) and DTG (dashed lines) of 10-day GNA-blended pastes (AFt: Aluminate Ferrite Tri-sulfate, P: portlandite, C: calcite).</p>
Full article ">Figure 8
<p>SEM images of hydrated pastes: (<b>a</b>) 0G; (<b>b</b>) 0.05G; and (<b>c</b>) 1G.</p>
Full article ">Figure 9
<p>Twenty-eight-day compressive strength of test specimens (mean ± 1.96SD).</p>
Full article ">Figure 10
<p>Electrical bulk resistivity of test samples (mean ± 1.96SD).</p>
Full article ">Figure 11
<p>Water absorption of the test samples per ASTM C1403 [<a href="#B46-sustainability-17-02349" class="html-bibr">46</a>] (mean ± 1.96SD).</p>
Full article ">Figure 12
<p>pH measurements of specimens 0G, 0.05G, and 1G over a 14-day period.</p>
Full article ">
19 pages, 18754 KiB  
Article
The Design and Characterization of an Artificial Soil Substrate Made from Sand-Washing Slurry
by Biqin Dong, Xu Wu, Penghui Wang, Rongxin Peng and Yanshuai Wang
J. Compos. Sci. 2025, 9(2), 88; https://doi.org/10.3390/jcs9020088 - 13 Feb 2025
Viewed by 532
Abstract
The global reserve of sand has significantly decreased, and sand washing is predominantly favored due to its simplicity and low operational costs, but this method poses significant environmental risks like landslides, making its reuse essential for sustainability. In view of this challenge, based [...] Read more.
The global reserve of sand has significantly decreased, and sand washing is predominantly favored due to its simplicity and low operational costs, but this method poses significant environmental risks like landslides, making its reuse essential for sustainability. In view of this challenge, based on the composite preparation method, an innovative approach was proposed to prepare an artificial soil substrate from sand-washing slurry. The physical and vegetative feasibility performance, including strength, density, water absorption, retention, electrical conductivity (EC), and pH; and microstructural characteristics, including X-ray diffraction (XRD), scanning electron microscopy (SEM), and nuclear magnetic resonance (NMR) of the artificial soil substrate with different proportions of cement and foaming agent were measured. Increasing the cement content to 30% of un-crushed artificial soil substrate specimens improved strength, whereas 40% reduced it due to the diminished pore-filling effect. Water absorption rates ranged from 29.22% to 36.68%, increasing with more foaming agent and decreasing with more cement, while the water retention time was 12–14 days, and incorporating foaming agent significantly increased water absorption. Leachate pH ranged from 11.99 to 12.18, and reduced to 7.82–8.28 with 5% phosphoric acid. The EC of the artificial soil substrate decreased by 88.64% to 93.59% after 10 wet–dry cycles, aligning with the standard. Artificial-soil-substrate-predominant products include calcite, quartz, and dolomite, with a pronounced silica content and soil substrate porosity ranging from 27.96% to 51.80%. From the microstructural test, calcium silicate hydrate gel, produced by cement hydration, effectively bound the sand-washing slurry, thereby improving strength. Full article
Show Figures

Figure 1

Figure 1
<p>Raw material morphology: (<b>a</b>) sand-washing slurry; (<b>b</b>) pre-treated sand-washing slurry; (<b>c</b>) cement; (<b>d</b>) foaming agent.</p>
Full article ">Figure 2
<p>Particle size distribution, XRD patterns, and SEM image of sand-washing slurry. (<b>a</b>) Particle size distribution. (<b>b</b>) XRD patterns. (<b>c</b>) SEM image.</p>
Full article ">Figure 3
<p>The particle size distribution, XRD patterns, and SEM image of sand-washing slurry.</p>
Full article ">Figure 4
<p>Compressive strength and average density of different specimens. (<b>a</b>) Compressive strength. (<b>b</b>) Density.</p>
Full article ">Figure 5
<p>Percentage of different particle sizes after crushing of artificial soil substrate.</p>
Full article ">Figure 6
<p>Water absorption rates of artificial soil substrate particles.</p>
Full article ">Figure 7
<p>Time-varying water retention rate of artificial soil substrate particles.</p>
Full article ">Figure 8
<p>Effect of different phosphoric acid concentrations on the pH of artificial soil substrate. (<b>a</b>) Change in pH after treatment. (<b>b</b>) Effect of phosphoric acid on pH of S3C7-2.</p>
Full article ">Figure 9
<p>EC values of artificial soil substrate with different proportions.</p>
Full article ">Figure 10
<p>EC values of artificial soil substrate after 10 wet–dry cycles.</p>
Full article ">Figure 11
<p>Percolation rate of the artificial soil substrate with different proportions.</p>
Full article ">Figure 12
<p>XRD results of artificial soil substrate particles with different proportions: (<b>a</b>) 80% sand-washing slurry; (<b>b</b>) 70% sand-washing slurry; (<b>c</b>) 60% sand-washing slurry.</p>
Full article ">Figure 13
<p>SEM images of artificial soil substrate particles at different level magnifications: (<b>a</b>) 100×; (<b>b</b>) 200×; (<b>c</b>) 500×; (<b>d</b>) 2000×; (<b>e</b>) 2000×; (<b>f</b>) 4000×; (<b>g</b>) 10,000×.</p>
Full article ">Figure 14
<p>The porosity of the artificial soil substrate with different proportions.</p>
Full article ">Figure 15
<p>Pore size distribution of artificial soil substrate with different proportions: (<b>a</b>) sample S8C2; (<b>b</b>) sample S7C3; (<b>c</b>) sample S6C4.</p>
Full article ">Figure 16
<p>Small-scale planting experiments.</p>
Full article ">
22 pages, 2514 KiB  
Article
Phytotoxicity and Metals Mobility Assessment in Mining Wastes Amended with Various Biochars
by Yassine Chafik, Marta Sena-Velez, Hugo Henaut, Mohammed Oujdi, Alex Ceriani, Sabine Carpin, Domenico Morabito and Sylvain Bourgerie
Land 2025, 14(2), 372; https://doi.org/10.3390/land14020372 - 11 Feb 2025
Viewed by 417
Abstract
Mining activities often contaminate soils with heavy metals, generating environmental and health risks. This study investigates the ecotoxicity of muddy (Mw) and sandy (Sw) mining wastes on Phaseolus vulgaris and assesses the impact of five locally sourced biochar amendments on plant growth and [...] Read more.
Mining activities often contaminate soils with heavy metals, generating environmental and health risks. This study investigates the ecotoxicity of muddy (Mw) and sandy (Sw) mining wastes on Phaseolus vulgaris and assesses the impact of five locally sourced biochar amendments on plant growth and soil pore water (SPW) properties. Most biochars improved water retention, except for argan nut shells (An) biochar, highlighting the importance of feedstock type. Sw supported better plant growth than Mw regardless of biochar addition, due to textural differences. Palm fronds (Pf) biochar significantly enhanced surface leaf area in Sw. SPW analysis revealed that biochar affected pH and electrical conductivity (EC) differently across soil types. Mw consistently increased pH, while Sw’s pH was biochar-dependent. A significant 5.1-fold EC increase was recorded in Sw amended with Pf. All biochars reduced Pb availability in Mw at planting, while Cu availability decreased in Sw at harvest. In Mw, Pb, Zn, and Cu, uptake and accumulation were unaffected by biochar, while a slight reduction was observed in Sw roots. A germination test with Lepidium sativum confirmed these findings, particularly the inhibition observed with An. This dual approach highlights the toxicity of mining soils and biochars’ potential as amendments for soil remediation programs. Full article
(This article belongs to the Special Issue Ecosystem Disturbances and Soil Properties (Second Edition))
Show Figures

Figure 1

Figure 1
<p>Water holding capacity of Mw (<b>A</b>) and Sw (<b>B</b>) amended or not with the different biochar types. Letters (a, b, c) refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (<span class="html-italic">n</span> = 5 ± SE). Significant difference between amended and unamended Mw and Sw is presented in (<b>C</b>) by * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01) and *** (<span class="html-italic">p</span> &lt; 0.001). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar.</p>
Full article ">Figure 2
<p>Stems height curves of <span class="html-italic">Phaseolus vulgaris</span> grown on Mw (<b>A</b>) and Sw (<b>B</b>) amended or not with the different biochar types. Letters (a, b, c) refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (n = 5 ± SE). Significant difference between amended and unamended Mw and Sw at Day (12) is presented in (<b>C</b>) by * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01) and *** (<span class="html-italic">p</span> &lt; 0.001). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar.</p>
Full article ">Figure 3
<p>Dry masses of leaves, stems, and roots of <span class="html-italic">Phaseolus vulgaris</span> grown on Mw (<b>A</b>) and Sw (<b>B</b>) amended or not with the different biochar types. Letters (a, b refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (n = 5 ± SE). Significant differences between amended and unamended Mw and Sw at Day (12) are presented in (<b>C</b>,<b>D</b>) by * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01) and *** (<span class="html-italic">p</span> &lt; 0.001). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar.</p>
Full article ">Figure 4
<p>Soil pore water pH of Mw (<b>A</b>) and Sw (<b>C</b>) and electrical conductivity (EC) of Mw (<b>B</b>) and Sw (<b>D</b>), unamended and amended with the different biochar types. Letters (a, b, c, d) refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (n = 5 ± SE). Significant difference between amended and unamended Mw and Sw are presented in <a href="#app1-land-14-00372" class="html-app">Table S1</a> by * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01) and *** (<span class="html-italic">p</span> &lt; 0.001). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar.</p>
Full article ">Figure 5
<p>Soil pore water metals concentrations of Mw (<b>A</b>–<b>C</b>) and Sw (<b>D</b>–<b>F</b>), unamended and amended with the different biochar types, at two sampling days D(0) and D(12) − Pb in Mw (<b>A</b>) and Sw (<b>D</b>), Zn in Mw (<b>B</b>) and Sw (<b>E</b>), Cu in Mw (<b>C</b>) and Sw (<b>F</b>). Letters (a, b, c) refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (n = 5 ± SE). Significant difference between amended and unamended Mw and Sw at Day (12) are presented in <a href="#app1-land-14-00372" class="html-app">Table S2</a> by * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01) and *** (<span class="html-italic">p</span> &lt; 0.001). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar. nd: refers to ‘not detected’.</p>
Full article ">Figure 6
<p>Pb (<b>A</b>,<b>D</b>), Zn (<b>B</b>,<b>E</b>), and Cu (<b>C</b>,<b>F</b>) accumulation in the shoots and roots of <span class="html-italic">Phaseolus vulgaris</span> grown on Mw (<b>A</b>–<b>C</b>), and in the leaves, stems, and roots grown on Sw (<b>D</b>–<b>F</b>), amended or unamended with the different biochar types. Letters (a, b, c) refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (n = 5 ± SE). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar.</p>
Full article ">Figure 7
<p>Germination, development, and seed vigor index combined diagram of <span class="html-italic">Lepidium sativum</span> growing in different biochars. Letters (a, b, c) refer to a significant difference between treatments at <span class="html-italic">p</span> &lt; 0.05 (n = 3 ± SE). Mw: unamended muddy waste; Sw: unamended sandy waste; Pf: palm fronds biochar, Cw: citrus wood biochar, Ec: eucalyptus chips biochar, Ew: eucalyptus wood biochar, and An: argan nut shells biochar.</p>
Full article ">
20 pages, 8145 KiB  
Article
Assessing a Multilayered Hydrophilic–Electrocatalytic Forward Osmosis Membrane for Ammonia Electro-Oxidation
by Perla Cruz-Tato, Laura I. Penabad, César Lasalde, Alondra S. Rodríguez-Rolón and Eduardo Nicolau
Membranes 2025, 15(2), 37; https://doi.org/10.3390/membranes15020037 - 22 Jan 2025
Viewed by 995
Abstract
Over the years, the ammonia concentration in water streams and the environment is increasing at an alarming rate. Many membrane-based processes have been studied to alleviate this concern via adsorption and filtration. On the other hand, ammonia electro-oxidation is an approach of particular [...] Read more.
Over the years, the ammonia concentration in water streams and the environment is increasing at an alarming rate. Many membrane-based processes have been studied to alleviate this concern via adsorption and filtration. On the other hand, ammonia electro-oxidation is an approach of particular interest owing to its energetic and environmental benefits. Thus, a plausible alternative to combine these two paths is by using an electroconductive membrane (ECM) to complete the ammonia oxidation reaction (AOR). This combination of processes has been studied very limitedly, and it can be an area for development. Herein, we developed a multilayered membrane with hydrophilic and electrocatalytic properties capable of completing the AOR. The porosity of carbon black (CB) particles was embedded in the polymeric support (CBES) and the active side was composed of a triple layer consisting of polyamide/CB/Pt nanoparticles (PA:CB:Pt). The CBES increased the membrane porosity, changed the pores morphology, and enhanced water permeability and electroconductivity. The deposition of each layer was monitored and corroborated physically, chemically, and electrochemically. The final membrane CBES:PA:VXC:Pt reached higher water flux than its PSF counterpart (3.9 ± 0.3 LMH), had a hydrophilic surface (water contact angle: 19.8 ± 0.4°), and achieved the AOR at −0.3 V vs. Ag/AgCl. Our results suggest that ECMs with conductive material in both membrane layers enhanced their electrical properties. Moreover, this study is proof-of-concept that the AOR can be succeeded by a polymeric FO-ECMs. Full article
(This article belongs to the Section Membrane Applications for Water Treatment)
Show Figures

Figure 1

Figure 1
<p>Physical comparison between the PSF and CBES supports. (<b>A</b>) Photograph of the fabricated supports, (<b>B</b>) summary of the water flux (<span class="html-italic">J<sub>w</sub></span>) and contact angle (CA), and (<b>C</b>) SEM micrographs of the surface and cross-section.</p>
Full article ">Figure 2
<p>Electrochemical evaluation of the PSF and CBES supports in a three-electrode cell where the supports were used as working electro (WE), Pt wire was used as a counter electrode (CE), and Ag/AgCl (0.197 V vs. NHE) as the reference electrode (RE). (<b>A</b>) Cyclic voltammograms using the ferri/ferrocyanide redox couple (5 mM in 0.1 M KCl) at 50mV/s, (<b>B</b>) Randles–Sevcik plot to determine the ferri/ferro diffusion coefficient, (<b>C</b>) Nyquist plot to compare the membrane impedance, (<b>D</b>) CBES Nyquist plot close-up, and (<b>E</b>) Randles EEC and the respective values of each electrical component.</p>
Full article ">Figure 3
<p>Evaluation of the :PA and :VXC layers over the PSF and CBES supports. (<b>A</b>–<b>F</b>) Morphological comparison via SEM images, (<b>G</b>) summary of water flux (<span class="html-italic">J<sub>w</sub></span>) and contact angle (CA), (<b>H</b>) continuous water contact angle results of the membranes containing the :PA and :VXC layers, and (<b>I</b>) electrochemical evaluation via cyclic voltammetry (CV) using ferri/ferrocyanide redox couple (5 mM in 0.1 M KCl, RE: Ag/AgCl (NaCl saturated, 0.197V vs. NHE) and CE: Pt wire, and a scan rate of 50 mV/s).</p>
Full article ">Figure 4
<p>Surface morphology comparison at different SEM magnifications (5k, 10k, and 100k) between (<b>A</b>–<b>C</b>) PSF:PA:VXC:Pt and (<b>D</b>–<b>F</b>) CBES:PA:VXC:Pt.</p>
Full article ">Figure 5
<p>Cyclic voltammogram of (<b>A</b>) PSF:PA:VXC and (<b>B</b>) CBES:PA:VXC before and after the Pt electrodeposition at a scan rate of 50 mV/s in 0.5 M H<sub>2</sub>SO<sub>4</sub>. The RE was Ag/AgCl (NaCl saturated, 0.197 V vs. NHE) and the CE was a Pt wire. The inset graph in (<b>B</b>) corresponds to a higher magnification of the Pt electrochemical processes.</p>
Full article ">Figure 6
<p>Electrochemical evaluation and comparison between CBES:PA:VXC and CBES:PA:VXC:Pt. (<b>A</b>) Randles–Sevcik plot to determine the diffusion coefficient, (<b>B</b>) Nyquist plot to study the membrane’s impedance, and (<b>C</b>) Randles EEC and estimated values for each electrical component.</p>
Full article ">Figure 7
<p>Carbon black embedded support and membrane. (<b>A</b>) Schematic representation of all the layers in the fabricated membrane, (<b>B</b>) FO performance comparison including the water flux (<span class="html-italic">J<sub>w</sub></span>) and reverse salt flux (<span class="html-italic">J<sub>s</sub></span>), (<b>C</b>) AOR evaluation using PSF:PA:VXC:Pt as the WE, and (<b>D</b>) AOR evaluation using CBES:PA:VXC:Pt as the WE.</p>
Full article ">Figure 8
<p>Evaluation of the AOR activity over the CBES:PA:VXC:Pt membrane at different ammonia concentrations. (<b>A</b>) <span class="html-italic">j-t<sup>−</sup></span><sup>1/2</sup> plot at constant −0.3 V vs. Ag/AgCl and (<b>B</b>) <span class="html-italic">j-c</span> plot at −0.3 V vs. Ag/AgCl.</p>
Full article ">Scheme 1
<p>Representation of the membrane assembling steps. L1: the support was manufactured via phase inversion with two different casting solutions, L2: interfacial polymerization of polyamide film, L3: spray coating of VXC, and PVA followed by a crosslinking process with glutaraldehyde (GA), and L4: electrodeposition of Pt nanoparticles.</p>
Full article ">
18 pages, 16661 KiB  
Article
Change in Lead–Zinc Waste Slag’s Physical and Chemical Properties and Heavy Metal Migration Characteristics Under Acid Soaking Environment
by Shibo Li, Fuli Han, Jianquan Ma, Junfang Dai, Hao Guo, Jinduo Chen, Yashu Ji and Chenguang Xiang
Water 2025, 17(1), 115; https://doi.org/10.3390/w17010115 - 3 Jan 2025
Viewed by 542
Abstract
As a kind of industrial solid waste, lead–zinc waste slag can easily cause heavy metal migration in acid environments, resulting in safety risks. Along these lines, in this work, the waste slag of a lead–zinc mining area in western Qinling, Shaanxi, China, was [...] Read more.
As a kind of industrial solid waste, lead–zinc waste slag can easily cause heavy metal migration in acid environments, resulting in safety risks. Along these lines, in this work, the waste slag of a lead–zinc mining area in western Qinling, Shaanxi, China, was selected as the experimental material. Seven groups of acid soaking solutions with different pH values were set up with three parallel samples in each group, and the acid soaking experiments were conducted for 100 days. During the experiment, the electrical conductivity, pH value, and heavy metal content of the solution, as well as the pore distribution and heavy metal content of the waste slag surface, were measured. The results showed that with pH = 4 and pH = 7 as the environmental limit values, the pH value, electrical conductivity (EC), and heavy metal contents in the solution changed to different types after the waste slag was soaked in the solution with a pH of less than 4 and the solution with a pH of 5–7. The release of heavy metals from waste slag exceeded the discharge standard in the environment with a pH of less than 4, and the pore structure of waste slag was obviously enhanced, especially in the soaking solution with an initial pH of 1. The maximum soaking amounts of Zn, Pb, and Cd were 2.584 mg/L, 1.28 mg/L, and 0.0169 mg/L, respectively, during the experiment, which did not meet the “Environmental quality standards for surface water” (GB 3838-2002) and could not be excreted as direct surface water. However, when the environmental pH was greater than 7, the heavy metals showed reverse adsorption. This result indicated that when the acid soaking solution entered the alkaline range, the heavy metal content in the solution was less, which can basically meet the discharge standard. However, the pores of waste slag continued to expand. Our work provides valuable insights into the treatment of waste slag and environmental protection in lead-zinc mining areas containing sulfur. Full article
Show Figures

Figure 1

Figure 1
<p>Material of lead–zinc waste slag, (<b>a</b>) lead–zinc waste slag deposition located in Fengxian, Shanxi Province, China, (<b>b</b>,<b>c</b>) the raw lead–zinc waste slag, (<b>d</b>–<b>f</b>) the main mineral composition was derived by performing X-ray diffraction, polarizing microscopy, and scanning electron microscopy measurements, respectively.</p>
Full article ">Figure 2
<p>Depiction of the experimental procedure and test methods.</p>
Full article ">Figure 3
<p>The elemental distribution raw LZWS by SEM-EDS: (<b>a</b>,<b>d</b>) and (<b>b</b>,<b>e</b>) were part of the enlarged raw LZWS; (<b>c</b>,<b>f</b>) were EDS of elemental distribution of No.1 and No.2.</p>
Full article ">Figure 4
<p>Pore distribution and pore characteristics of the raw lead–zinc waste slag, (<b>a</b>) pore characteristics from SEM-EDS images, (<b>b</b>) pore volume distribution via specific surface area and pore size analyzer, and (<b>c</b>) the pore distribution acquired from polarizing microscope.</p>
Full article ">Figure 5
<p>Variation in the electrical conductivity and pH value of the soak solution with time: (<b>a</b>) the initial pH = 1 of soak solution, (<b>b</b>) the initial pH = 2 of soak solution, (<b>c</b>) the initial pH = 3 of soak solution, (<b>d</b>) the initial pH = 4 of soak solution, (<b>e</b>) the initial pH = 5 of soak solution, (<b>f</b>) the initial pH = 6 of soak solution, and (<b>g</b>) the initial pH = 7 of deionized water.</p>
Full article ">Figure 5 Cont.
<p>Variation in the electrical conductivity and pH value of the soak solution with time: (<b>a</b>) the initial pH = 1 of soak solution, (<b>b</b>) the initial pH = 2 of soak solution, (<b>c</b>) the initial pH = 3 of soak solution, (<b>d</b>) the initial pH = 4 of soak solution, (<b>e</b>) the initial pH = 5 of soak solution, (<b>f</b>) the initial pH = 6 of soak solution, and (<b>g</b>) the initial pH = 7 of deionized water.</p>
Full article ">Figure 6
<p>Five heavy metals (Pb, Zn, Cd, Cr, and Cu) contents of soaking solution from waste slag particle size in 32-64 mm with time under 7 different pH values: (<b>a</b>) the initial pH = 1 of soak solution, (<b>b</b>) the initial pH = 2 of soak solution, (<b>c</b>) the initial pH = 3 of soak solution, (<b>d</b>) the initial pH = 4 of soak solution, (<b>e</b>) the initial pH = 5 of soak solution, (<b>f</b>) the initial pH = 6 of soak solution, and (<b>g</b>) the initial pH = 7 of deionized water.</p>
Full article ">Figure 6 Cont.
<p>Five heavy metals (Pb, Zn, Cd, Cr, and Cu) contents of soaking solution from waste slag particle size in 32-64 mm with time under 7 different pH values: (<b>a</b>) the initial pH = 1 of soak solution, (<b>b</b>) the initial pH = 2 of soak solution, (<b>c</b>) the initial pH = 3 of soak solution, (<b>d</b>) the initial pH = 4 of soak solution, (<b>e</b>) the initial pH = 5 of soak solution, (<b>f</b>) the initial pH = 6 of soak solution, and (<b>g</b>) the initial pH = 7 of deionized water.</p>
Full article ">Figure 7
<p>Nitrogen adsorption/desorption isotherms of lead–zinc waste slag samples after 264 h at different pH in the acid soaking environment: (<b>a</b>) waste slag sample at pH = 1, (<b>b</b>) waste slag sample at pH = 3, (<b>c</b>) waste slag sample at pH = 5, and (<b>d</b>) waste slag sample with deionized water.</p>
Full article ">Figure 8
<p>Pore volume of lead–zinc waste slag samples in different acid soaking times at different pH: (<b>a</b>) waste slag sample at pH = 1, (<b>b</b>) waste slag sample at pH = 3, (<b>c</b>) waste slag sample at pH = 5, and (<b>d</b>) waste slag sample with deionized water.</p>
Full article ">Figure 9
<p>Variation in EC, pH, pore volume, and mean aperture with time at different pH in the acid soaking environment: (<b>a</b>) waste slag sample at pH = 1, (<b>b</b>) waste slag sample at pH = 3, (<b>c</b>) waste slag sample at pH = 5, and (<b>d</b>) waste slag sample with deionized water.</p>
Full article ">Figure 10
<p>Comparison of energy spectrum element content analysis from SEM-EDS and heavy metal content analysis from acid soaking solution at the end of the experiments.</p>
Full article ">Figure 11
<p>Lead–zinc waste slag, water, and acid interaction process.</p>
Full article ">
22 pages, 12163 KiB  
Article
Assessing the Use of Electrical Resistivity for Monitoring Crude Oil Contaminant Distribution in Unsaturated Coastal Sands Under Varying Salinity
by Margaret A. Adeniran, Michael A. Oladunjoye and Kennedy O. Doro
Geosciences 2024, 14(11), 308; https://doi.org/10.3390/geosciences14110308 - 14 Nov 2024
Viewed by 1129
Abstract
Monitoring crude oil spills in coastal areas is challenging due to limitations in traditional in situ methods. Electrical resistivity imaging (ERI) offers a high-resolution approach to monitoring the subsurface spatial distribution of crude oil, but its effectiveness in highly-resistive, unsaturated coastal sands with [...] Read more.
Monitoring crude oil spills in coastal areas is challenging due to limitations in traditional in situ methods. Electrical resistivity imaging (ERI) offers a high-resolution approach to monitoring the subsurface spatial distribution of crude oil, but its effectiveness in highly-resistive, unsaturated coastal sands with varying salinity remains unexplored. This study assessed the effectiveness of ERI for monitoring crude oil spills in sandy soil using a 200 × 60 × 60 cm 3D sandbox filled with medium-fine-grained sand under unsaturated conditions. Two liters of crude oil were spilled under controlled conditions and monitored for 48 h using two surface ERI transects with 98 electrodes spaced every 2 cm and a dipole–dipole electrode array. The influence of varying salinity was simulated by varying the pore-fluid conductivities at four levels (0.6, 20, 50, and 85 mS/cm). After 48 h, the results show a percentage resistivity increase of 980%, 280%, 142%, and 70% for 0.6, 20, 50, and 85 mS/cm, respectively. The crude oil migration patterns varied with porewater salinity as higher salinity enhanced the crude oil retention at shallow depth. High salinity produces a smaller resistivity contrast, thus limiting the sensitivity of ERI in detecting the crude oil contaminant. These findings underscore the need to account for salinity variations when designing remediation strategies, as elevated salinity may restrict crude oil migration, resulting in localized contaminations. Full article
(This article belongs to the Section Geophysics)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Experimental design of the sandbox showing an inflow and outflow chamber on the left and right sides; (<b>b</b>) Laboratory setup of the sandbox and the geophysical measurements with the cables connected to 98 electrodes. The electrodes are spaced 2 cm apart along a 198 cm profile length.</p>
Full article ">Figure 2
<p>Two-dimensional resistivity inversion results for (<b>a</b>) unsaturated sand with a concentration of (0.6 mS/cm), iteration no. = 3, RMS = 1.12; (<b>b</b>) unsaturated salt-impacted sand with a concentration of (20 mS/cm), iteration no. = 3, RMS = 1; and (<b>c</b>) unsaturated salt-impacted sand with a concentration of (50 mS/cm), iteration no. = 3, RMS = 1.2; (<b>d</b>) unsaturated salt-impacted sand with concentration of (85 mS/cm), iteration no = 3, RMS = 1.5.</p>
Full article ">Figure 3
<p>Two-dimensional resistivity inversion result taken across Profile 1 and Profile 2 for unsaturated sand during the crude oil spillage experiment. Five separate measurements were taken at different times for over 49.15 h using the dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm. The white-dashed lines show the left and right boundaries of the crude oil contaminant front.</p>
Full article ">Figure 4
<p>Two-dimensional resistivity inversion results across Profiles 1 and 2 for unsaturated salt-impacted sand with a salinity of 20 mS/cm during the crude oil spill experiment. Five separate measurements were taken at different times for over 49.15 h using the dipole–dipole array. The red box at the top of the profiles show the crude oil spill surface location between x = 60 cm and x = 75 cm. The white-dashed lines show the left and right boundaries of the crude oil contaminant front.</p>
Full article ">Figure 5
<p>Two-dimensional resistivity inversion results taken across Profiles 1 and 2 for unsaturated salt-impacted sand with a salt concentration of 50 mS/cm during the crude oil spill experiment. Five separate measurements were taken at different times for over 49.15 h using a dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm. The white-dashed lines show the left and right boundaries of the crude oil contaminant front.</p>
Full article ">Figure 6
<p>Two-dimensional resistivity inversion results taken across Profiles 1 and 2 for unsaturated salt-impacted sand with a salt concentration of 85 mS/cm during the crude oil spill experiment. Five separate measurements were taken at different times for over 49.15 h using a dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm. The white-dashed lines show the left and right boundaries of the crude oil contaminant front.</p>
Full article ">Figure 7
<p>Two-dimensional time-lapse inversion results showing the percentage difference in an unsaturated sand with 0.6 mS/cm concentration, from 0 h to 49.15 h using a dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm.</p>
Full article ">Figure 8
<p>Two-dimensional time-lapse inversion results showing the percentage difference in an unsaturated salt-impacted sand with salt concentration of 20 mS/cm from 0 h to 49.15 h using a dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm.</p>
Full article ">Figure 9
<p>Two-dimensional time-lapse inversion result showing the percentage difference in an unsaturated salt-impacted sand with salt concentration of 50 mS/cm from 0 h to 49.15 h using a dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm.</p>
Full article ">Figure 10
<p>Two-dimensional time-lapse inversion results showing the percentage difference in an unsaturated salt-impacted sand with salt concentration of 85 mS/cm from 0 h to 49.15 h using a dipole–dipole array. The red box at the top of the profile shows the crude oil spill surface location between x = 60 cm and x = 75 cm.</p>
Full article ">Figure 11
<p>Scattered plots showing variations in percentage difference in resistivity with depth for an unsaturated sand extracted from inverted resistivity model for (<b>A1</b>–<b>A4</b>) 0.6 mS/cm, at x = 0.5 m, 0.9 m, 1.1 m, and 1.3 m, respectively; (<b>B1</b>–<b>B4</b>) 20 mS/cm, at x = 0.5 m, 0.9 m, 1.1 m, and 1.3 m, respectively; (<b>C1</b>–<b>C4</b>) 50 mS/cm at x = 0.5 m, 0.9 m, 1.1 m, and 1.3 m, respectively; and (<b>D1</b>–<b>D4</b>) 85 mS/cm at x = 0.5 m, 0.9 m, 1.1 m, and 1.3 m, respectively.</p>
Full article ">
16 pages, 9971 KiB  
Article
The Saturation Calculation of NMR Logging Based on Constructing Water Spectrum Function
by Yongfu Liu, Rui Deng, Shenchao Luo, Hong Li, Lei Zhang and Lixiong Gan
Processes 2024, 12(11), 2518; https://doi.org/10.3390/pr12112518 - 12 Nov 2024
Viewed by 729
Abstract
Tight sandstone oil reservoirs are characterized by complex structures, poor pore connectivity, and strong heterogeneity, with features such as low porosity and ultra-low permeability. Conventional methods for calculating saturation cannot accurately evaluate the hydrocarbon saturation of these reservoirs. To address this, a study [...] Read more.
Tight sandstone oil reservoirs are characterized by complex structures, poor pore connectivity, and strong heterogeneity, with features such as low porosity and ultra-low permeability. Conventional methods for calculating saturation cannot accurately evaluate the hydrocarbon saturation of these reservoirs. To address this, a study was conducted from the perspective of non-electrical logging methods, focusing on the inherent nuclear magnetic resonance (NMR) characteristics of different fluids to develop a saturation calculation method that avoids the influence of the rock matrix, thus enabling precise saturation measurement in tight sandstone oil reservoirs. The traditional NMR porosity model was modified by segmenting it using the clay-bound water cutoff value, aiming to identify the distribution pattern of fluids in pores outside the clay-bound water zone. Through theoretical derivation and water spectrum function simulation, a water spectrum function and its parameter range suitable for the NMR T2 distribution in tight sandstone reservoirs were determined. Using core-sealed core saturation as a reference, the particle swarm optimization (PSO) algorithm was applied to optimize the parameter range and construct the final water spectrum function tailored to tight sandstone oil reservoirs. The accuracy and practicality of this method were validated by applying the derived water spectrum function to NMR logging in the exploration block, allowing for precise saturation calculations and the accurate evaluation of tight reservoir saturation. Full article
(This article belongs to the Special Issue Oil and Gas Drilling Processes: Control and Optimization)
Show Figures

Figure 1

Figure 1
<p>Schematic of traditional NMR porosity model [<a href="#B25-processes-12-02518" class="html-bibr">25</a>]. (<b>A</b>) is the rock skeleton model (<b>B</b>) is the T2 distribution corresponding to the rock skeleton model.</p>
Full article ">Figure 2
<p>Schematic diagram of improved NMR porosity model.</p>
Full article ">Figure 3
<p>Basic process for constructing NMR water spectrum. The blue line is the T2 distribution of water, and the red line is the T2 distribution of oil, which is consistent with <a href="#processes-12-02518-f002" class="html-fig">Figure 2</a>.</p>
Full article ">Figure 4
<p>Core experiment analysis. (<b>a</b>) Nuclear magnetic reverse cumulative saturation spectrum (<b>b</b>) Mercury injection curve.</p>
Full article ">Figure 5
<p>Constructed function fitting.</p>
Full article ">Figure 6
<p>Variation properties of water spectrum function <span class="html-italic">m</span>.</p>
Full article ">Figure 7
<p>Variation properties of water spectrum function <span class="html-italic">T</span><sub>2<span class="html-italic">CW</span></sub>.</p>
Full article ">Figure 8
<p>Relationship between weights of water spectrum function and <span class="html-italic">m</span>, <span class="html-italic">T</span><sub>2<span class="html-italic">CW</span></sub>.</p>
Full article ">Figure 9
<p>Iteration of parameter optimization using particle swarm algorithm (PSO).</p>
Full article ">Figure 10
<p>Distribution of optimal parameters.</p>
Full article ">Figure 11
<p>Water spectrum function corresponding to optimized parameters.</p>
Full article ">Figure 12
<p>Practical application in Well S1.</p>
Full article ">Figure 13
<p>Practical application in Well S2.</p>
Full article ">Figure 14
<p>Error analysis of Well S1.</p>
Full article ">Figure 15
<p>Error analysis of Well S2.</p>
Full article ">
25 pages, 30704 KiB  
Article
Micro–Macro-Analysis and Model Derivation of Electrical Resistivity of Ningxia Cement–Loess
by Zhijia Xue, Qiquan Deng, Jianqiang Gao, Ying Zhang, Ziwei Zhang, Changgen Yan, Jie Wang, Fangyuan Han, Longshan Li and Yongzhi Ma
Buildings 2024, 14(10), 3265; https://doi.org/10.3390/buildings14103265 - 15 Oct 2024
Viewed by 677
Abstract
In recent years, highway infrastructure in the Ningxia region of China has rapidly advanced. Cement–loess is extensively utilized in the roadbed and foundation reinforcement. It is necessary to conduct micro–macro-analysis and model derivation of the electrical resistivity on Ningxia cement–loess, which are beneficial [...] Read more.
In recent years, highway infrastructure in the Ningxia region of China has rapidly advanced. Cement–loess is extensively utilized in the roadbed and foundation reinforcement. It is necessary to conduct micro–macro-analysis and model derivation of the electrical resistivity on Ningxia cement–loess, which are beneficial for both the practical application of electrical resistivity and the evaluation of the geotechnical properties of cement–loess. Therefore, a series of electrical resistivity measurements, microstructural observations (scanning electron microscopy), mineral analyses (thermogravimetric analysis), and theoretical analyses were adopted on the cement–loess. The following conclusions can be drawn: The electrical resistivity is negatively related to dry density and water content, while it is positively related to cement dosage and curing age. A cement dosage of 6% exhibits a lower hydration reaction potential compared to 12%, causing a slower increase in electrical resistivity. The formation of calcium silicate gel around particles results in particle clustering and pore filling, reducing the pore area and increasing electrical resistivity. Increased hydration also decreases microscopic orientation, contributing to a higher electrical resistivity of cement–loess. Finally, a new three-dimensional electrical resistivity model was created, finding that the electrical resistivity of Ningxia cement–loess was determined by the dry density, water content (ρd·w), cement dosage, and curing age (aw·T) in an exponential function form. The new three-dimensional electrical resistivity model could be used in the high-efficiency evaluation of the cement–loess geotechnical parameter, offering valuable insights for the monitoring and maintenance of road infrastructure. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
Show Figures

Figure 1

Figure 1
<p>Particle size distribution (<b>a</b>) and XRD results of loess and cement (<b>b</b>) [<a href="#B24-buildings-14-03265" class="html-bibr">24</a>].</p>
Full article ">Figure 2
<p>A self-made electrical resistivity testing box: a VICTOR-4091C digital bridge instrument (<b>a</b>) and a cement–loess sample (<b>b</b>).</p>
Full article ">Figure 3
<p>The relationship between electrical resistivity, water content, and dry density at curing ages of 1 (<b>a</b>), 7 (<b>b</b>), 14 (<b>c</b>), and 28 days (<b>d</b>).</p>
Full article ">Figure 3 Cont.
<p>The relationship between electrical resistivity, water content, and dry density at curing ages of 1 (<b>a</b>), 7 (<b>b</b>), 14 (<b>c</b>), and 28 days (<b>d</b>).</p>
Full article ">Figure 3 Cont.
<p>The relationship between electrical resistivity, water content, and dry density at curing ages of 1 (<b>a</b>), 7 (<b>b</b>), 14 (<b>c</b>), and 28 days (<b>d</b>).</p>
Full article ">Figure 4
<p>The relationship between electrical resistivity, curing age, and cement dosage at water contents of 8 (<b>a</b>), 10 (<b>b</b>), 12.5 (<b>c</b>), and 14% (<b>d</b>).</p>
Full article ">Figure 4 Cont.
<p>The relationship between electrical resistivity, curing age, and cement dosage at water contents of 8 (<b>a</b>), 10 (<b>b</b>), 12.5 (<b>c</b>), and 14% (<b>d</b>).</p>
Full article ">Figure 5
<p>Microstructures under different dry densities (dry density, water content, curing age, and cement dosage, respectively).</p>
Full article ">Figure 6
<p>Microstructures under different water contents (dry density, water content, curing age, and cement dosage, respectively).</p>
Full article ">Figure 7
<p>Microstructures under different curing ages (dry density, water content, curing age, and cement dosage, respectively) [<a href="#B24-buildings-14-03265" class="html-bibr">24</a>].</p>
Full article ">Figure 7 Cont.
<p>Microstructures under different curing ages (dry density, water content, curing age, and cement dosage, respectively) [<a href="#B24-buildings-14-03265" class="html-bibr">24</a>].</p>
Full article ">Figure 8
<p>Microstructure under different cement dosages (dry density, water content, curing age, and cement dosage, respectively).</p>
Full article ">Figure 9
<p>The directional frequency distribution of cement–loess pores under the conditions (dry density; water content; curing age; cement dosage, respectively) of different dry densities (<b>a</b>), different water contents (<b>b</b>), different curing ages (<b>c</b>), and different cement dosages (<b>d</b>).</p>
Full article ">Figure 9 Cont.
<p>The directional frequency distribution of cement–loess pores under the conditions (dry density; water content; curing age; cement dosage, respectively) of different dry densities (<b>a</b>), different water contents (<b>b</b>), different curing ages (<b>c</b>), and different cement dosages (<b>d</b>).</p>
Full article ">Figure 10
<p>Pore sizes and percentages at different curing ages and different cement dosages.</p>
Full article ">Figure 11
<p>Comparative analysis of TG/DTG under different dry densities (<span class="html-italic">ω</span> = 12.5%, <span class="html-italic">t</span> = 28 days, <span class="html-italic">a<sub>ω</sub> </span>= 9%).</p>
Full article ">Figure 12
<p>Comparative analysis of TG/DTG under different water contents (<b>a</b>) and cement dosages (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 13
<p>Comparative analysis of TG/DTG under different curing ages.</p>
Full article ">Figure 14
<p>The fitting curves of cement–loess resistivity with dry density and water content (<span class="html-italic">ρ<sub>d</sub></span>·<span class="html-italic">ω</span>) under cement dosages of 6 (<b>a</b>), 9 (<b>b</b>), and 12% (<b>c</b>).</p>
Full article ">Figure 14 Cont.
<p>The fitting curves of cement–loess resistivity with dry density and water content (<span class="html-italic">ρ<sub>d</sub></span>·<span class="html-italic">ω</span>) under cement dosages of 6 (<b>a</b>), 9 (<b>b</b>), and 12% (<b>c</b>).</p>
Full article ">Figure 15
<p>The fitting curves of cement–loess resistivity with dry density, water content, curing age, and cement dosage.</p>
Full article ">
16 pages, 15551 KiB  
Article
Development of an Underwater Adaptive Penetration System for In Situ Monitoring of Marine Engineering Geology
by Miaojun Sun, Zhigang Shan, Wei Wang, Shaopeng Zhang, Heyu Yu, Guangwei Cheng and Xiaolei Liu
Sensors 2024, 24(17), 5563; https://doi.org/10.3390/s24175563 - 28 Aug 2024
Viewed by 843
Abstract
In recent years, offshore wind farms have frequently encountered engineering geological disasters such as seabed liquefaction and scouring. Consequently, in situ monitoring has become essential for the safe siting, construction, and operation of these installations. Current technologies are hampered by limitations in single-parameter [...] Read more.
In recent years, offshore wind farms have frequently encountered engineering geological disasters such as seabed liquefaction and scouring. Consequently, in situ monitoring has become essential for the safe siting, construction, and operation of these installations. Current technologies are hampered by limitations in single-parameter monitoring and insufficient probe-penetration depth, hindering comprehensive multi-parameter dynamic monitoring of seabed sediments. To address these challenges, we propose a foldable multi-sensor probe and establish an underwater adaptive continuous penetration system capable of concurrently measuring seabed elevation changes and sediment pore water pressure profiles. The reliability of the equipment design is confirmed through static analysis of the frame structure and sealed cabin. Furthermore, laboratory tests validate the stability and accuracy of the electrical and mechanical sensor measurements. Preliminary tests conducted in a harbor environment demonstrate the system’s effectiveness. Full article
(This article belongs to the Section Physical Sensors)
Show Figures

Figure 1

Figure 1
<p>Design schematic of UAPS/ILMM.</p>
Full article ">Figure 2
<p>Schematic of UAPS/ILMM in the offshore sea. Note this figure is not to scale, a red arrow indicates that the survey ship is equipped with a remote control platform.</p>
Full article ">Figure 3
<p>Schematic of foldable multi-sensor probe. Note the red line is used as a segment identifier for the foldable multi-sensor probe, the symbol # in this paper is used to indicate the serial number of PPS or ER.</p>
Full article ">Figure 4
<p>Schematic of self-potential probe. (<b>a</b>) Size of self-potential probe, (<b>b</b>) The connective wire of the self-potential sensor inside the probe, (<b>c</b>) The probe rod measurement method.</p>
Full article ">Figure 5
<p>Schematic of the underwater adaptive penetration device. (<b>a</b>) Schematic of the penetration device, (<b>b</b>) Friction wheel transmission, (<b>c</b>) Rod screwing device, (<b>d</b>) Connection structure of the rods.</p>
Full article ">Figure 6
<p>Static analysis of the system structure. (<b>a</b>) Stress distribution, (<b>b</b>) Total deformation.</p>
Full article ">Figure 7
<p>Static analysis of the sealed cabin. (<b>a</b>) Simplified model, (<b>b</b>) Meshing, (<b>c</b>) Stress distribution, (<b>d</b>) Total deformation.</p>
Full article ">Figure 8
<p>Self-potential sensor functionality test. (<b>a</b>) Test facility, (<b>b</b>) Diagram of the test process, (<b>c</b>) Potential difference test results.</p>
Full article ">Figure 9
<p>Pore water pressure sensor test results.</p>
Full article ">Figure 10
<p>Harbor testing of UAPS/ILMM. (<b>a</b>) Deployment, (<b>b</b>) Recovery.</p>
Full article ">Figure 11
<p>Real-time data measured by self-potential probe during UAPS/ILMM deployment. (<b>a</b>) Self-potential profile, (<b>b</b>) Potential difference.</p>
Full article ">Figure 12
<p>Diagram of the foldable multi-sensor probe penetration.</p>
Full article ">Figure 13
<p>Real-time data measured by self-potential probe after UAPS/ILMM deployment. (<b>a</b>) The position of the ER at the sediment-water interface, (<b>b</b>) Self-potential profile, (<b>c</b>) Potential difference.</p>
Full article ">Figure 14
<p>The excess pore water pressure monitoring results.</p>
Full article ">
14 pages, 3130 KiB  
Article
Assessment of Different Humate Ureas on Soil Mineral N Balanced Supply
by Shengjun Bai, Lingying Xu, Rongkui Ren, Yue Luo, Xiaoqi Liu, Jingli Guo, Xu Zhao and Wentai Zhang
Agronomy 2024, 14(8), 1856; https://doi.org/10.3390/agronomy14081856 - 21 Aug 2024
Viewed by 897
Abstract
Urea supplements, such as humic acids, could enhance fertilizer nitrogen use effectiveness. Melting is superior to mixing for humate urea application; however, the effects of diverse humate ureas from various coal sources on soil N supply remain unclear. This study compared the properties [...] Read more.
Urea supplements, such as humic acids, could enhance fertilizer nitrogen use effectiveness. Melting is superior to mixing for humate urea application; however, the effects of diverse humate ureas from various coal sources on soil N supply remain unclear. This study compared the properties of two humic acids from different coal sources (HA1, weathered coal; HA2, lignite coal), and their impact on soil mineral N supply and the nitrate–ammonium ratio under flooded and 60% water-filled pore space (WFPS) over a 14-day incubation. Humate ureas stimulated soil mineral N accumulation and balanced the soil nitrate–ammonium ratio at 1:1; however, no significant difference existed between the two humate ureas under 60% WFPS. Humate urea enhanced soil ammonium nitrogen (NH4+-N) retention and delayed nitrate nitrogen (NH4-N) release, leading to soil mineral N retention, especially in lignite humic acid urea (H2AU) treatments from lignite under flooding. Structural equation modeling (SEM) and linear regression revealed that humic acids elevated soil redox potential (Eh) and electrical conductivity (EC), stimulating soil N mineralization and adjusting the optimal nitrate–ammonium ratio. Humate urea improved soil mineral N supply compared to traditional urea treatments, and humic acids from lignite were more beneficial for crop cultivation from a mineral soil N supply perspective. These findings enhance our understanding of humate urea benefits and aid in optimizing humic acids application for N management. Full article
(This article belongs to the Special Issue Advances in Application Effects and Mechanisms of Fertilizer Products)
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>,<b>B</b>) represent the net mineral nitrogen accumulation of the soil and the soil nitrate-ammonium ratio, respectively. Soil mineral N accumulation and soil nitrate–ammonium ratio under different treatment conditions. Different small letters mean significant at 0.05 level. *** indicates <span class="html-italic">p</span> &lt; 0.001, M, moisture. T, treatment. M*T, moisture*treatment.</p>
Full article ">Figure 2
<p>Incubation-induced variations in soil NH<sub>4</sub><sup>+</sup>-N and soil NO<sub>3</sub><sup>−</sup>-N levels under different treatments. (<b>A</b>,<b>C</b>) depict the impact of diverse humic acids on soil NH<sub>4</sub><sup>+</sup>-N and soil NO<sub>3</sub><sup>−</sup>-N under 60% WFPS (dryland) moisture conditions. (<b>B</b>,<b>D</b>) portray the effects of diverse humic acids on soil NH<sub>4</sub><sup>+</sup>-N and soil NO<sub>3</sub><sup>−</sup>-N in a flooded environment.</p>
Full article ">Figure 3
<p>(<b>A</b>,<b>B</b>) represent the net mineral nitrogen accumulation of the soil and the soil nitrate-ammonium ratio, respectively. Cumulative soil N mineralization and soil nitrate–ammonium ratio under different treatment conditions. Different small letters mean significant at 0.05 level. *** indicates <span class="html-italic">p</span> &lt; 0.001, ** indicates <span class="html-italic">p</span> &lt; 0.01. M, moisture. T, treatment. M*T, moisture*treatment.</p>
Full article ">Figure 4
<p>Changes in soil NH<sub>4</sub><sup>+</sup>-N and soil NO<sub>3</sub><sup>−</sup>-N concentrations during incubation under different treatments. (<b>A</b>,<b>C</b>) represent the effect of different humate ureas on soil NH<sub>4</sub><sup>+</sup>-N and soil NO<sub>3</sub><sup>−</sup>-N under 60% WFPS (dryland) moisture conditions, respectively. (<b>B</b>,<b>D</b>) represent the effects of different humate ureas on soil NH<sub>4</sub><sup>+</sup>-N and soil NO<sub>3</sub><sup>−</sup>-N, respectively, in a flooded environment.</p>
Full article ">Figure 5
<p>Changes in nitrification inhibition efficiency during incubation under different treatments. (<b>A</b>,<b>B</b>) represent changes in nitrification inhibition in different treatments under dryland and flooded conditions, respectively.</p>
Full article ">Figure 6
<p>Correlation between soil NH<sub>4</sub><sup>+</sup>-N, soil NO<sub>3</sub><sup>−</sup>-N, soil nitrogen mineralization, soil nitrate–ammonium ratio and soil physico-chemical properties (pH, EC, Eh). *** indicates <span class="html-italic">p</span> &lt; 0.001, ** indicates <span class="html-italic">p</span> &lt; 0.01, * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 7
<p>Structural equation modeling (SEM) utilizing humic acids from weathered coal (HA1), humic acids from lignite (HA2), soil pH, soil electrical conductivity (EC), soil redox potential (Eh), soil total nitrogen (TN), soil total carbon (TC), soil N mineralization rate (Min), soil nitrification rate (NIT), soil denitrification rate (Denit), soil nitrate–ammonium ratio (NAR), and soil nitrification inhibition rate (NIR). Path coefficients indicate correlations between variables: blue indicates positive and red denotes negative. Arrow thickness correlates with standardized path coefficients’ magnitude. Significant standardized path coefficients are marked with ***, **, and *. R<sup>2</sup> denotes model’s explained variance of the respective variable. The models yield satisfactory results. (<b>A</b>) displays dryland data, with SEM’s GFI at 0.48. (<b>B</b>) presents flooded conditions, with SEM’s GFI at 0.66. (<b>C</b>,<b>D</b>) denote the standardized effects of the main factors under dryland and flooded conditions, respectively.</p>
Full article ">
17 pages, 6882 KiB  
Article
Experimental Study on Combined Microwave–Magnetic Separation–Flotation Coal Desulfurization
by Guangming Wang, Zhijun Ma, Zhijing Zhou, Yunsheng Zheng and Liang Cheng
Molecules 2024, 29(16), 3729; https://doi.org/10.3390/molecules29163729 - 6 Aug 2024
Cited by 1 | Viewed by 1013
Abstract
In order to reduce the content of sulfur and ash in coal, improve the desulfurization and deashing rates, a combined experiment method of microwave magnetic separation-flotation was proposed for raw coal. The desulfurization and deashing rates of three experiment methods, namely, single magnetic [...] Read more.
In order to reduce the content of sulfur and ash in coal, improve the desulfurization and deashing rates, a combined experiment method of microwave magnetic separation-flotation was proposed for raw coal. The desulfurization and deashing rates of three experiment methods, namely, single magnetic separation, microwave magnetic separation, and microwave magnetic separation–flotation, were compared. Taking the microwave magnetic separation–flotation experiment method as the main line, the effects of the microwave irradiation time, microwave power, grinding time, magnetic field intensity, plate seam width, foaming agent dosage, collector dosage, and inhibitor dosage on desulfurization and deashing were discussed, and the mechanism of microwave irradiation on magnetic separation and flotation was revealed. The results show that under the conditions of a microwave irradiation time of 60 s, a microwave power of 80% of the rated power (800 W), a grinding time of 8 min, a plate seam width (the plate seam width of a magnetic separator sorting box) of 1 mm, a magnetic field intensity of 2.32 T, a foaming agent dosage of 90 g/t, a collector dosage of 2125 g/t, and an inhibitor dosage of 1500 g/t, the desulfurization and deashing effect is the best. The desulphurization rate is 76.51%, the sulfur removal rate of pyrite is 96.50%, and the deashing rate is 61.91%. Microwaves have the characteristic of selective heating, and the thermal conductivity of organic matter in coal is greater than that of mineral. Microwave irradiation can improve the reactivity of pyrite in coal, pyrolyze pyrite into high-magnetic pyrite, improve the magnetic properties, and improve the magnetic separation effect. Therefore, microwave irradiation plays a role in promoting magnetic separation. Through microwave irradiation, the positive and negative charges in coal molecules constantly vibrate and create friction under the action of an electric field force, and the thermal action generated by this vibration and friction process affects the structural changes in oxygen-containing functional groups in coal. With the increase in the irradiation time and power, the hydrophilic functional groups of –OH and –COOH decrease and the hydrophilicity decreases. Microwave heating evaporates the water in the pores of coal samples and weakens surface hydration. At the same time, microwave irradiation destroys the structure of coal and impurity minerals, produces cracks at the junction, increases the surface area of coal to a certain extent, enhances the hydrophobicity, and then improves the effect of flotation desulfurization and deashing. Therefore, after the microwave irradiation of raw coal, the magnetic separation effect is enhanced, and the flotation desulfurization effect is also enhanced. Full article
(This article belongs to the Section Physical Chemistry)
Show Figures

Figure 1

Figure 1
<p>Grinding ore time curve chart.</p>
Full article ">Figure 2
<p>Curve chart of desulfurization effect changing with plate seam width.</p>
Full article ">Figure 3
<p>A curve chart of the effect of deashing with the variation in the board seam width.</p>
Full article ">Figure 4
<p>Curve chart of desulfurization effect with field strength variation.</p>
Full article ">Figure 5
<p>Curve chart of deashing effect with field strength variation.</p>
Full article ">Figure 6
<p>A curve chart of the desulfurization effect changing with the irradiation time.</p>
Full article ">Figure 7
<p>A curve chart of the effect of deashing on the irradiation time.</p>
Full article ">Figure 8
<p>Curve chart of desulfurization effect changing with microwave power.</p>
Full article ">Figure 9
<p>Curve chart of deashing effect changing with microwave power.</p>
Full article ">Figure 10
<p>Curve chart of desulfurization effect changing with foaming agent dosage.</p>
Full article ">Figure 11
<p>Curve chart of deashing effect changing with foaming agent dosage.</p>
Full article ">Figure 12
<p>Curve chart of desulfurization effect changing with collector dosage.</p>
Full article ">Figure 13
<p>Curve chart of deashing effect changing with collector dosage.</p>
Full article ">Figure 14
<p>Curve chart of desulfurization effect changing with inhibitor dosage.</p>
Full article ">Figure 15
<p>Curve chart of deashing effect changing with inhibitor dosage.</p>
Full article ">Figure 16
<p>XRD curves of magnetic separation, microwave magnetic separation, and microwave magnetic separation–flotation ((<b>A</b>): magnetic separation, (<b>B</b>): microwave magnetic separation, and (<b>C</b>): microwave magnetic separation–flotation).</p>
Full article ">Figure 17
<p>FTIR curves of magnetic separation, microwave magnetic separation, and microwave magnetic separation–flotation ((<b>A</b>): magnetic separation, (<b>B</b>): microwave magnetic separation, and (<b>C</b>): microwave magnetic separation–flotation).</p>
Full article ">Figure 18
<p>FTIR spectra of different irradiation times and different powers ((<b>A</b>): irradiation time; (<b>B</b>): irradiation power).</p>
Full article ">Figure 19
<p>XPS curves of magnetic separation, microwave magnetic separation, and microwave magnetic separation–flotation ((<b>A</b>): magnetic separation, (<b>B</b>): microwave magnetic separation, and (<b>C</b>): microwave magnetic separation–flotation).</p>
Full article ">Figure 20
<p>XRD plot of raw coal.</p>
Full article ">Figure 21
<p>SEM-EDS energy spectrum of raw coal.</p>
Full article ">
47 pages, 16044 KiB  
Review
Comprehensive Review on the Impact of Chemical Composition, Plasma Treatment, and Vacuum Ultraviolet (VUV) Irradiation on the Electrical Properties of Organosilicate Films
by Mikhail R. Baklanov, Andrei A. Gismatulin, Sergej Naumov, Timofey V. Perevalov, Vladimir A. Gritsenko, Alexey S. Vishnevskiy, Tatyana V. Rakhimova and Konstantin A. Vorotilov
Polymers 2024, 16(15), 2230; https://doi.org/10.3390/polym16152230 - 5 Aug 2024
Cited by 2 | Viewed by 2043
Abstract
Organosilicate glass (OSG) films are a critical component in modern electronic devices, with their electrical properties playing a crucial role in device performance. This comprehensive review systematically examines the influence of chemical composition, vacuum ultraviolet (VUV) irradiation, and plasma treatment on the electrical [...] Read more.
Organosilicate glass (OSG) films are a critical component in modern electronic devices, with their electrical properties playing a crucial role in device performance. This comprehensive review systematically examines the influence of chemical composition, vacuum ultraviolet (VUV) irradiation, and plasma treatment on the electrical properties of these films. Through an extensive survey of literature and experimental findings, we elucidate the intricate interplay between these factors and the resulting alterations in electrical conductivity, dielectric constant, and breakdown strength of OSG films. Key focus areas include the impact of diverse organic moieties incorporated into the silica matrix, the effects of VUV irradiation on film properties, and the modifications induced by various plasma treatment techniques. Furthermore, the underlying mechanisms governing these phenomena are discussed, shedding light on the complex molecular interactions and structural rearrangements occurring within OSG films under different environmental conditions. It is shown that phonon-assisted electron tunneling between adjacent neutral traps provides a more accurate description of charge transport in OSG low-k materials compared to the previously reported Fowler–Nordheim mechanism. Additionally, the quality of low-k materials significantly influences the behavior of leakage currents. Materials retaining residual porogens or adsorbed water on pore walls show electrical conductivity directly correlated with pore surface area and porosity. Conversely, porogen-free materials, developed by Urbanowicz, exhibit leakage currents that are independent of porosity. This underscores the critical importance of considering internal defects such as oxygen-deficient centers (ODC) or similar entities in understanding the electrical properties of these materials. Full article
(This article belongs to the Special Issue Polymer-SiO2 Composites II)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Structure of amorphous SiO<sub>2</sub> (<b>a</b>) and porous methyl-terminated organosilicate glass (OSG) material (<b>b</b>), where some oxygen bridging atoms in the SiO<sub>2</sub> structure are replaced by terminal alkyl groups R. (<b>c</b>) Periodic mesoporous organosilica (PMO) with carbon bridges between Si atoms and methyl terminal groups on the pore wall surface. PMO materials are normally synthesized using sol–gel technology.</p>
Full article ">Figure 2
<p>FTIR spectra of organosilicate glass (OSG) films: 1—methylsilsesquioxane (MSSQ), and periodic mesoporous organosilicas (PMOs) with different bridges: 2—methylene, 3—ethylene, 4—1,4-phenylene, 100 mol%, annealed at 430 °C for 30 min in air.</p>
Full article ">Figure 3
<p>Characteristic X-ray photoelectron spectroscopy (XPS) spectra of the Si 2p peaks for the chemical solution-deposited (CSD) (<b>a</b>) and plasma-enhanced chemical vapor-deposited (PECVD) (<b>b</b>) methyl-terminated organosilicate glass (OSG) films deposited at different mass flow rate ratios of cinene porogen to triethoxymethylsilane: (a) 1.0; (b) 1.5; (c) 2.0. The presented pictures are redrawn from the data previously reported in our papers [<a href="#B46-polymers-16-02230" class="html-bibr">46</a>,<a href="#B47-polymers-16-02230" class="html-bibr">47</a>].</p>
Full article ">Figure 4
<p>Schematic representation of three different plasma chambers used in microelectronics processing. Inductive coupling plasma (ICP) (<b>a</b>) has the highest plasma density and can provide the highest isotropic etch rate, while capacitively coupled plasma (CCP) (<b>b</b>) offers a prefect anisotropic profile, but the etch rate is relatively low. For this reason, the reactors combining the ICP and CCP effects are used, and the etch rates and degree of plasma damage can be controlled [<a href="#B55-polymers-16-02230" class="html-bibr">55</a>]. Downstream plasma (DSP) (<b>c</b>) provides a soft regime and is mostly used for surface cleaning and resist removal when damage-free processing is important. The bottom picture depicts EFTEM results showing Si, C, and O profiles of low-<span class="html-italic">k</span> samples exposed in CCP (BPO), T&amp;BP, and downstream (TPO), and mixed (T&amp;BP) conditions. Reproduced from E. Kunnen, M. R. Baklanov, A. Franquet, D. Shamiryan, T. V. Rakhimova, A. M. Urbanowicz, H. Struyf, W. Boullart; Effect of energetic ions on plasma damage of porous SiCOH low-<span class="html-italic">k</span> materials. J. Vac. Sci. Technol. B, 2010; 28 (3): 450–459 [<a href="#B55-polymers-16-02230" class="html-bibr">55</a>], with the permission of AVS: Science &amp; Technology of Materials, Interfaces, and Processing.</p>
Full article ">Figure 5
<p>The depth profiles, ranging from the top (0 nm) to the bottom of the film (105 nm), showing the depletion of model Si–CH<sub>3</sub> bonds in a plasma-enhanced chemical vapor-deposited (PECVD) methyl-terminated organosilicate glass (OSG) film after exposure to 13.5, 58.4, 106, and 147 nm emissions for 7200 s. [Si–CH<sub>3</sub>]<sub>pristine</sub> refers to the initial SiCH<sub>3</sub> concentration before exposure to VUV light, while [Si–CH<sub>3</sub>]<sub>exposed</sub> denotes the SiCH<sub>3</sub> concentration after VUV exposure. The figure was taken from ref. [<a href="#B83-polymers-16-02230" class="html-bibr">83</a>].</p>
Full article ">Figure 6
<p>The average effective quantum yield for breaking Si–CH<sub>3</sub> bonds by VUV photons depending on low-<span class="html-italic">k</span> dielectrics porosity. The figure was redrawn based on the data from ref. [<a href="#B83-polymers-16-02230" class="html-bibr">83</a>].</p>
Full article ">Figure 7
<p>(<b>a</b>) Jablonsky diagram depicting electron distribution from the highest occupied molecular orbital (HOMO) in a molecule in a singlet ground state. (<b>b</b>) Schema of the possible bond scission in the model periodic mesoporous organosilica (PMO) molecule, with the corresponding dissociation energy calculated as the difference between the free Gibbs energies of the molecule in the ground state and the products of dissociation.</p>
Full article ">Figure 8
<p>Structural models for SiO<sub>2</sub> defects: (<b>a</b>) non-bridging oxygen hole center, NBOHC; (<b>b</b>) peroxy radical, POR; (<b>c</b>) peroxy linkage, POL; and defects with a deficit of oxygen: (<b>d</b>) E’ and (<b>e</b>) E‘<sub>δ</sub> centers; (<b>f</b>) relaxed oxygen vacancy, ODC(I); and (<b>g</b>) divalent silicon, ODC(II). Spin states are indicated by the arrows.</p>
Full article ">Figure 9
<p>Representative <span class="html-italic">K</span>-band electron spin resonance (ESR) spectra measured at 4.3 K on <span class="html-italic">p</span>-Si(100) crystal substrates with 200 nm thick layers of chemical vapor-deposited (CVD)-grown <span class="html-italic">a</span>-SiO<sub>2</sub> (CVD, <span class="html-italic">k</span> = 4.2) (<b>a</b>), nanocrystalline silica (NCS, <span class="html-italic">k</span> = 2.3, porosity 30%, pore size ~2 nm) prepared by spin-on coating (<b>b</b>), and CVD-grown carbon-doped oxide (BD, <span class="html-italic">k</span> = 3.0 and 7% ellipsometric porosimetry (EP)-measured open porosity, pore size ~1.8 nm) without (<b>c</b>) and with (<b>d</b>) the plasma surface treatment. See ref. [<a href="#B109-polymers-16-02230" class="html-bibr">109</a>] for more detail. Reproduced from M. R. Baklanov, V. Jousseaume, T. V. Rakhimova, D. V. Lopaev, Yu. A. Mankelevich, V. V. Afanas’ev, J. L. Shohet, S. W. King, E. T. Ryan; Impact of VUV photons on SiO<sub>2</sub> and organosilicate low-<span class="html-italic">k</span> dielectrics: General behavior, practical applications, and atomic models. Appl. Phys. Rev., 2019; 6 (1): 011301 [<a href="#B25-polymers-16-02230" class="html-bibr">25</a>] (Figure 39); and permission for underlying Figure from S. Shamuilia, V. V. Afanas’ev, P. Somers, A. Stesmans, Y.-L. Li, Zs. Tőkei, G. Groeseneken, K. Maex; Internal photoemission of electrons at interfaces of metals with low-<span class="html-italic">κ</span> insulators. Appl. Phys. Lett., 2006; 89 (20): 202909 [<a href="#B109-polymers-16-02230" class="html-bibr">109</a>] (Figure 3), with the permission of AIP Publishing.</p>
Full article ">Figure 10
<p>UV absorption spectra of organosilicate glass (OSG) films with various bridging groups shown in <a href="#polymers-16-02230-f011" class="html-fig">Figure 11</a>: 1a—SiO<sub>2</sub>, 2a—OSG with 1 methyl terminal group in the fragment, 3a—OSG with 2 methyl groups, 1b and 2b—one bridging methylene and 6 methyl terminal groups, 3b—ethylene bridge and 6 methyl terminal groups, 4b—1,4-benzene bridge, 5b—hyperconnected 1,3,5-benzene bridge.</p>
Full article ">Figure 11
<p>The fragments representing organosilicate glass (OSG) materials with different bridging groups and configurations. The numbers corresponding to the absorption spectra are shown in <a href="#polymers-16-02230-f010" class="html-fig">Figure 10</a>: 1a—SiO<sub>2</sub>, 2a—OSG with 1 methyl terminal group in the fragment, 3a—OSG with 2 methyl groups, 1b and 2b—one bridging methylene and 6 methyl terminal groups, 3b—ethylene bridge and 6 methyl terminal groups, 4b—1,4-benzene bridge, 5b—hyperconnected 1,3,5-benzene bridge. A challenge of such calculations is the selection of an appropriate cluster reflecting the real absorption spectrum of the bulk material. The absorption spectra calculated for the SiO<sub>2</sub> cluster are in good agreement with the measured spectra, confirming that the calculated spectra are realistic [<a href="#B117-polymers-16-02230" class="html-bibr">117</a>,<a href="#B118-polymers-16-02230" class="html-bibr">118</a>].</p>
Full article ">Figure 12
<p>Change in the absorption coefficient and index of refraction of plasma-enhanced chemical vapor-deposited (PECVD) organosilicate glass (OSG) films UV-cured at different times (<b>a</b>,<b>b</b>) and the films deposited with different porogen concentrations (<b>c</b>,<b>d</b>). The measured dielectric constant correlates with porosity via the Clausius–Mossotti equation: low dielectric constant corresponds to higher porosity, and therefore, to a higher porogen concentration. T is the optimal curing time used for the fabrication of a standard low-<span class="html-italic">k</span> film.</p>
Full article ">Figure 13
<p>Change in film porosity versus curing time (T). Calculations from the curves presented in <a href="#polymers-16-02230-f012" class="html-fig">Figure 12</a>b using Equation (8). The values of refractive indices at 1.8–2.0 eV are used for the calculation because the extinction coefficient is equal to zero in this region, and Equation (8) is valid.</p>
Full article ">Figure 14
<p>Band alignment for organosilicate glass (OSG) low-<span class="html-italic">k</span>/barrier interconnect structure with energy position of defect states related to oxygen-deficient center (ODC) and porogen residues as reported in the papers by King [<a href="#B97-polymers-16-02230" class="html-bibr">97</a>] and Marsik [<a href="#B131-polymers-16-02230" class="html-bibr">131</a>]. The Schottky barrier between TaN/Ta barrier and low-<span class="html-italic">k</span> dielectrics, equal to 4.5 ± 0.5 eV, was measured by using internal photoemission (IPE) measurements by both Shamiulia [<a href="#B109-polymers-16-02230" class="html-bibr">109</a>] and Atkin [<a href="#B119-polymers-16-02230" class="html-bibr">119</a>].</p>
Full article ">Figure 15
<p>Change in dielectric constant (<b>a</b>) and breakdown field (<b>b</b>) on porosity of plasma-enhanced chemical vapor-deposited (PECVD) low-<span class="html-italic">k</span> films. Reproduced from E. Van Besien, M. Pantouvaki, L. Zhao, D. De Roest, M.R. Baklanov, Z. Tőkei, G. Beyer; Influence of porosity on electrical properties of low-<span class="html-italic">k</span> dielectrics. Microelectronic Engineering, 2012, 92: 59–61 [<a href="#B136-polymers-16-02230" class="html-bibr">136</a>], with the permission of Elsevier.</p>
Full article ">Figure 16
<p>(<b>a</b>) Valence band X-ray photoelectron spectroscopy (XPS) spectra of an <span class="html-italic">a</span>-SiCOH (<span class="html-italic">k</span> = 3.3) film before and after ion sputtering, where the “0” binding energy corresponds to the energy of the Fermi level. (<b>b</b>) Schematic representation of the density of states of an <span class="html-italic">a</span>-SiCOH (<span class="html-italic">k</span> = 3.3) film before and after ion sputtering. Reproduced from X. Guo, H. Zheng, S. W. King, V. V. Afanas’ev, M. R. Baklanov, J.-F. de Marneffe, Y. Nishi, J. L. Shohet; Defect-induced bandgap narrowing in low-<span class="html-italic">k</span> dielectrics. Appl. Phys. Lett., 2015; 107 (8): 082903 [<a href="#B135-polymers-16-02230" class="html-bibr">135</a>], with the permission of AIP Publishing.</p>
Full article ">Figure 17
<p>(<b>a</b>) The leakage current and breakdown voltage of different types of organosilicate glass (OSG) low-<span class="html-italic">k</span> films with <span class="html-italic">k</span> values changing from 3.0 (CVD1, CVD4, CVD5) to <span class="html-italic">k</span> = 2.3 (CVD3) [<a href="#B144-polymers-16-02230" class="html-bibr">144</a>]. (<b>b</b>) Comparison of leakage current of CVD3 with organic low-<span class="html-italic">k</span> films (Samples 8 and 9 in <a href="#polymers-16-02230-t005" class="html-table">Table 5</a>) and sol–gel-based SOG films deposited by using a self-assembling approach. Reproduced from M. R. Baklanov, L. Zhao, E. V. Besien, M. Pantouvaki; Effect of porogen residue on electrical characteristics of ultra low-<span class="html-italic">k</span> materials. Microelectronic Engineering, 2011, 88: 990–993 [<a href="#B144-polymers-16-02230" class="html-bibr">144</a>], with the permission of Elsevier.</p>
Full article ">Figure 18
<p>Leakage current density as a function of the applied electric field recorded on metal–insulator–semiconductor (MIS) structures with SOG-2.2 low-<span class="html-italic">k</span> films hard-baked (HB)/hard-baked and UV-cured (HB + UV) for different times. Reproduced from M. Krishtab, V. Afanas’ev, A. Stesmans, S. De Gendt; Leakage current induced by surfactant residues in self-assembly based ultralow-<span class="html-italic">k</span> dielectric materials. Appl. Phys. Lett., 2017; 111 (3): 032908 [<a href="#B146-polymers-16-02230" class="html-bibr">146</a>], with the permission of AIP Publishing.</p>
Full article ">Figure 19
<p>(<b>a</b>) Current density as a function of the applied electrical field for porogen residue-free low-<span class="html-italic">k</span> films with different levels of porosity, as measured by metal dots. (<b>b</b>) Dielectric breakdown field as a function of open porosity at 25 °C. Reproduced from K. Vanstreels, I. Ciofi, Y. Barbarin, M. Baklanov; Influence of porosity on dielectric breakdown of ultralow-<span class="html-italic">k</span> dielectrics. J. Vac. Sci. Technol. B, 2013; 31 (5): 050604 [<a href="#B147-polymers-16-02230" class="html-bibr">147</a>], with the permission of AVS: Science &amp; Technology of Materials, Interfaces, and Processing.</p>
Full article ">Figure 20
<p>Contact-limited conduction mechanisms: (<b>a</b>) Schottky effect, (<b>b</b>) thermally assisted tunneling at the contact, (<b>c</b>) Fowler–Nordheim effect; bulk-limited conduction mechanisms: (<b>d</b>) Frenkel effect, (<b>e</b>) Hill–Adachi model, (<b>f</b>) Makram–Ebeid and Lanno model, (<b>g</b>) Nasyrov–Gritsenko model. Here, <span class="html-italic">e</span>—elementary charge, <span class="html-italic">F</span>—electric field, <span class="html-italic">W</span><sub>0</sub>—barrier height, <span class="html-italic">W</span>—trap ionization energy, <span class="html-italic">W<sub>t</sub></span>—thermal trap ionization energy, <span class="html-italic">a</span>—average distance between traps, <span class="html-italic">E<sub>C</sub></span>—conduction band bottom, <span class="html-italic">E<sub>V</sub></span>—valence band top, <span class="html-italic">β</span>—is the Frenkel constant, <span class="html-italic">V<sub>G</sub></span>—is the applied voltage.</p>
Full article ">Figure 20 Cont.
<p>Contact-limited conduction mechanisms: (<b>a</b>) Schottky effect, (<b>b</b>) thermally assisted tunneling at the contact, (<b>c</b>) Fowler–Nordheim effect; bulk-limited conduction mechanisms: (<b>d</b>) Frenkel effect, (<b>e</b>) Hill–Adachi model, (<b>f</b>) Makram–Ebeid and Lanno model, (<b>g</b>) Nasyrov–Gritsenko model. Here, <span class="html-italic">e</span>—elementary charge, <span class="html-italic">F</span>—electric field, <span class="html-italic">W</span><sub>0</sub>—barrier height, <span class="html-italic">W</span>—trap ionization energy, <span class="html-italic">W<sub>t</sub></span>—thermal trap ionization energy, <span class="html-italic">a</span>—average distance between traps, <span class="html-italic">E<sub>C</sub></span>—conduction band bottom, <span class="html-italic">E<sub>V</sub></span>—valence band top, <span class="html-italic">β</span>—is the Frenkel constant, <span class="html-italic">V<sub>G</sub></span>—is the applied voltage.</p>
Full article ">Figure 21
<p>Experimental (characters) and simulations with N-G model (black dash lines) current-voltage characteristics of the (<b>a</b>) periodic mesoporous organosilicas (PMO) carbon-bridged low-<span class="html-italic">k</span> dielectric [<a href="#B156-polymers-16-02230" class="html-bibr">156</a>], (<b>b</b>) methyl-terminated spin-on deposited OSG [<a href="#B157-polymers-16-02230" class="html-bibr">157</a>], and (<b>c</b>) PECVD methyl-terminated organosilicate glass (OSG) low-<span class="html-italic">k</span> dielectric [<a href="#B158-polymers-16-02230" class="html-bibr">158</a>]. The film thickness is 220 nm and the contact size is 0.5 mm<sup>2</sup>.</p>
Full article ">Figure 22
<p>A dielectric breakdown comparison for different low-<span class="html-italic">k</span> dielectrics. In this graph, the right two curves reflect SiO<sub>2</sub> layers fabricated by thermal oxidation of Si and plasma-enhanced chemical vapor-deposited (PECVD) SiO<sub>2</sub>. LK are OSG low-<span class="html-italic">k</span> dielectrics with <span class="html-italic">k</span> values from 2.5 to 3.0, and ultra-low-<span class="html-italic">k</span> (ULK) are low-<span class="html-italic">k</span> dielectrics with <span class="html-italic">k</span> values from 2.5 to 2.0. The figure is copied from E. T. Ogawa, O. Aubel; Electrical Breakdown. In Advanced Interconnect Dielectrics, 2012; pp. 369–434 [10], with the permission of Wiley &amp; Sons.</p>
Full article ">
18 pages, 17071 KiB  
Article
Multiphysics Measurements for Detection of Gas Hydrate Formation in Undersaturated Oil Coreflooding Experiments with Seawater Injection
by Bianca L. S. Geranutti, Mathias Pohl, Daniel Rathmaier, Somayeh Karimi, Manika Prasad and Luis E. Zerpa
Energies 2024, 17(13), 3280; https://doi.org/10.3390/en17133280 - 4 Jul 2024
Cited by 2 | Viewed by 1021
Abstract
A solid phase of natural gas hydrates can form from dissolved gas in oil during cold water injection into shallow undersaturated oil reservoirs. This creates significant risks to oil production due to potential permeability reduction and flow assurance issues. Understanding the conditions under [...] Read more.
A solid phase of natural gas hydrates can form from dissolved gas in oil during cold water injection into shallow undersaturated oil reservoirs. This creates significant risks to oil production due to potential permeability reduction and flow assurance issues. Understanding the conditions under which gas hydrates form and their impact on reservoir properties is important for optimizing oil recovery processes and ensuring the safe and efficient operation of oil reservoirs subject to waterflooding. In this work, we present two fluid displacement experiments under temperature control using Bentheimer sandstone core samples. A large diameter core sample of 3 inches in diameter and 10 inches in length was instrumented with multiphysics sensors (i.e., ultrasonic, electrical conductivity, strain, and temperature) to detect the onset of hydrate formation during cooling/injection steps. A small diameter core sample of 1.5 inches in diameter and 12 inches in length was used in a coreflooding apparatus with high-precision pressure transducers to determine the effect of hydrate formation on rock permeability. The fluid phase transition to solid hydrate phase was detected during the displacement of live-oil with injected water. The experimental procedure consisted of cooling and injection steps. Gas hydrate formation was detected from ultrasonic measurements at 7 °C, while strain measurements registered changes at 4 °C after gas hydrate concentration increased further. Ultrasonic velocities indicated the pore-filling morphology of gas hydrates, resulting in a high hydrate saturation of theoretically up to 38% and a substantial risk of intrinsic permeability reduction in the reservoir rock due to pore blockage by hydrates. Full article
Show Figures

Figure 1

Figure 1
<p>Core sample preparation. Lateral isolation with K20 epoxy in (<b>a</b>) and grooves creation for conductivity rings in (<b>b</b>).</p>
Full article ">Figure 2
<p>Sensor installation in the core sample: strain gauges and electrode rings installed in (<b>a</b>), wave crystals installed in (<b>b</b>), temperature sensors installed in (<b>c</b>), and all sensors finalized in (<b>d</b>).</p>
Full article ">Figure 3
<p>Soft epoxy process: PVC and foil was placed on the core in (<b>a</b>), followed by the soft epoxy deposition in (<b>b</b>), and finalized by the hardening of soft epoxy in (<b>c</b>).</p>
Full article ">Figure 4
<p>Coreflood setup constituted of a pressure vessel, vacuum pump, three ISCO pumps, chiller, and data acquisition equipment.</p>
Full article ">Figure 5
<p>Coreflood setup constituted of a pressure vessel, differential pressure transducer, pressure gauges, ISCO pump, continuous pulse-free pump, chiller, and back pressure regulator.</p>
Full article ">Figure 6
<p>Hydrate equilibrium curves for fresh water, seawater, and formation water, indicating the initial experiment pressure and temperature conditions.</p>
Full article ">Figure 7
<p>Raw P-waves (<b>left</b>) and S-waves (<b>right</b>) comparison for seawater injection at 8 °C and seawater injection at 7 °C indicating gas hydrate formation.</p>
Full article ">Figure 8
<p>P- and S-wave velocities for each crystal position of the large core sample at 15, 8, 7, and 4 °C. P-wave velocity increase was greater at the top of the sample due to a higher dissolved gas availability to form gas hydrate.</p>
Full article ">Figure 9
<p>P-wave average velocities for hydrate detection experimental procedure. From 8 to 7 °C an increase in velocity indicated gas hydrate formation. Gas hydrates kept growing for lower temperatures.</p>
Full article ">Figure 10
<p>S-wave average velocities for hydrate detection experimental procedure. No significant change was observed.</p>
Full article ">Figure 11
<p>Strain values for gas hydrate detection experimental procedure. Expansion of the core sample was notice at 4 ° C.</p>
Full article ">Figure 12
<p>Temperature readings from 8 °C to 7 °C from the gas hydrate detection experimental procedure. Gas hydrate exothermic reaction was noticed after 17 h of cooling.</p>
Full article ">Figure 13
<p>Pressure transducer differential measurements at water flow rates of 2, 1, and 0.5 mL/min including a linear trend line with a coefficient of determination of 1.</p>
Full article ">Figure 14
<p>Measured pressure differential and relative hydrate permeability plotted against the temperature step in the cooling temperature ramp. A sudden increase in differential pressure manifesting in a steep drop of relative hydrate permeability at the hydrate forming temperature of 7 °C can be seen.</p>
Full article ">Figure 15
<p>Theoretical permeability models based on the capillary bundle assumption and the Kozeny-type equations depending on the hydrate deposition morphology of pore filling and grain/pore coating. The equations yielding these models are derived in <a href="#app1-energies-17-03280" class="html-app">Appendix A</a>. Theoretical Permeability Models Derivation.</p>
Full article ">
19 pages, 9676 KiB  
Article
Three-Water Differential Parallel Conductivity Saturation Model of Low-Permeability Tight Oil and Gas Reservoirs
by Xiangyang Hu, Renjie Cheng, Hengrong Zhang, Jitian Zhu, Peng Chi and Jianmeng Sun
Energies 2024, 17(7), 1726; https://doi.org/10.3390/en17071726 - 3 Apr 2024
Viewed by 1089
Abstract
Addressing the poor performance of existing logging saturation models in low-permeability tight sandstone reservoirs and the challenges in determining model parameters, this study investigates the pore structure and fluid occurrence state of such reservoirs through petrophysical experiments and digital rock visualization simulations. The [...] Read more.
Addressing the poor performance of existing logging saturation models in low-permeability tight sandstone reservoirs and the challenges in determining model parameters, this study investigates the pore structure and fluid occurrence state of such reservoirs through petrophysical experiments and digital rock visualization simulations. The aim is to uncover new insights into fluid occurrence state and electrical conduction properties and subsequently develop a low-permeability tight sandstone reservoir saturation model with easily determinable parameters. This model is suitable for practical oilfield exploration and development applications with high evaluation accuracy. The research findings reveal that such reservoirs comprise three types of formation water: strongly bound water, weakly bound water, and free water. These types are found in non-connected micropores, poorly connected mesopores where fluid flow occurs when the pressure differential exceeds the critical value, and well-connected macropores. Furthermore, the three types of formation water demonstrate variations in their electrical conduction contributions. By inversely solving rock electrical experiment data, it was determined that for a single sample, the overall cementation index is the highest, followed by the cementation index of pore throats containing strongly bound water, and the lowest for the pore throats with free water. Building on the aforementioned insights, this study develops a parallel electrical pore cementation index term, ϕm, to account for the differences among the three types of water and introduces a parallel electrical saturation model suitable for logging evaluation of low-permeability tight oil and gas reservoirs. This model demonstrated positive application effects in the logging evaluation of low-permeability tight gas reservoirs in a specific basin in the Chinese offshore area, thereby confirming the advantages of its application. Full article
(This article belongs to the Section H: Geo-Energy)
Show Figures

Figure 1

Figure 1
<p>Experimental results of multi-stage centrifugal force nuclear magnetic resonance for six samples.</p>
Full article ">Figure 2
<p>Simulation of gas–water two-phase fluid distribution under different saturation states in two low-permeability tight sandstone rocks. Figures (<b>a</b>), (<b>b</b>), and (<b>c</b>) respectively show the simulated gas-water two-phase distribution of sample 3 with water saturation of 100%, 50%, and 25%. Figures (<b>d</b>), (<b>e</b>), and (<b>f</b>) respectively show the simulated gas-water two-phase distribution of sample 4 with water saturation of 100%, 50%, and 25%.</p>
Full article ">Figure 3
<p>Study of fluid occurrence state based on multi-stage centrifugal force nuclear magnetic resonance and visualization simulation. Figures (<b>a</b>), (<b>b</b>), and (<b>c</b>) respectively show the simulated gas-water two-phase distribution of sample 3 with water saturation of 100%, 50%, and 25%.</p>
Full article ">Figure 4
<p>Modeling of strongly bound water and total bound water saturation of the South China Sea region.</p>
Full article ">Figure 5
<p>Cross plot of three-water saturation and conductivity in water-saturated rocks. The red line, blue line, and green line represent the fitting relationship between strong bound water, weak bound water, and free water saturation and the conductivity of water saturated rocks, respectively.</p>
Full article ">Figure 6
<p>Petrophysical volume model of three-water differential parallel electrically conductive sandstone reservoirs.</p>
Full article ">Figure 7
<p>Fluid volume model for low-permeability tight oil and gas reservoirs under two different reservoir-forming dynamic conditions.</p>
Full article ">Figure 8
<p>The resistivity index (<span class="html-italic">RI</span>)–water-saturation (<span class="html-italic">S<sub>w</sub></span>) cross plot.</p>
Full article ">Figure 9
<p>The cross plot of rock electrical parameter <span class="html-italic">b</span> and saturation index <span class="html-italic">n</span> with core parameters in a specific area of the South China Sea.</p>
Full article ">Figure 10
<p>The cross plot of cementation index and core parameters in a gas field in the South China Sea Basin.</p>
Full article ">Figure 11
<p>The comprehensive interpretation and evaluation chart of the low-permeability gas reservoir logging in the South China Sea.</p>
Full article ">
17 pages, 3199 KiB  
Article
Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity
by Athanasios Fragkos, Dimitrios Loukatos, Georgios Kargas and Konstantinos G. Arvanitis
Sensors 2024, 24(7), 2206; https://doi.org/10.3390/s24072206 - 29 Mar 2024
Cited by 8 | Viewed by 3058
Abstract
In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical [...] Read more.
In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp’s permittivity–water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θma0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εab, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type. Full article
(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)
Show Figures

Figure 1

Figure 1
<p>Technical arrangement details supporting the data acquisition process: (<b>a</b>) hardware; (<b>b</b>) software.</p>
Full article ">Figure 2
<p>Characteristic instances from the soil preparation and measurement process.</p>
Full article ">Figure 3
<p>The ε<sub>a</sub> values against EC<sub>w</sub> in aqueous KCl solutions for the TEROS 12 and the 5TE sensors.</p>
Full article ">Figure 4
<p>The apparent dielectric permittivity (unitless) and soil water content (in cm<sup>3</sup> cm<sup>−3</sup>) relationship (i.e., ε<sub>a</sub>-θ<sub>m</sub>) for characteristic soil samples and for a salinity of EC<sub>w</sub> = 0.28 dS/m (blue points). More specifically, the soil cases were: (<b>a</b>) sand; (<b>b</b>) sandy loam; (<b>c</b>) loam; (<b>d</b>) clay; (<b>e</b>) silty clay loam; (<b>f</b>) sandy clay loam. Two other curves are also depicted per soil type. The first curve (black line) expresses the relationship ε<sub>a</sub>-θ, where θ is calculated by Equation (4) (TOPP curve) and the second curve (red line) expresses the relationship ε<sub>a</sub>-θ, where the θ is calculated by soil-specific CAL calibration equation (CAL curve).</p>
Full article ">Figure 4 Cont.
<p>The apparent dielectric permittivity (unitless) and soil water content (in cm<sup>3</sup> cm<sup>−3</sup>) relationship (i.e., ε<sub>a</sub>-θ<sub>m</sub>) for characteristic soil samples and for a salinity of EC<sub>w</sub> = 0.28 dS/m (blue points). More specifically, the soil cases were: (<b>a</b>) sand; (<b>b</b>) sandy loam; (<b>c</b>) loam; (<b>d</b>) clay; (<b>e</b>) silty clay loam; (<b>f</b>) sandy clay loam. Two other curves are also depicted per soil type. The first curve (black line) expresses the relationship ε<sub>a</sub>-θ, where θ is calculated by Equation (4) (TOPP curve) and the second curve (red line) expresses the relationship ε<sub>a</sub>-θ, where the θ is calculated by soil-specific CAL calibration equation (CAL curve).</p>
Full article ">Figure 5
<p>The apparent dielectric permittivity (unitless) and soil water content (in cm<sup>3</sup> cm<sup>−3</sup>) relationship (i.e., ε<sub>a</sub>-θ<sub>m</sub>) for specific salinity levels and for the characteristic soil samples: (<b>a</b>) sand; (<b>b</b>) sandy loam; (<b>c</b>) loam; (<b>d</b>) clay.</p>
Full article ">Figure 6
<p>Τhe relationship ε<sub>a</sub>-σ<sub>b</sub> for various EC<sub>w</sub> levels and for the characteristic soil types: (<b>a</b>) sand; (<b>b</b>) sandy loam; (<b>c</b>) loam; (<b>d</b>) clay; (<b>e</b>) silty clay loam; (<b>f</b>) sandy clay loam. For the last two types of soil, the relation ε<sub>a</sub>-σ<sub>b</sub> refers only to EC<sub>w</sub> = 0.28 dSm<sup>−1</sup>.</p>
Full article ">
Back to TopTop