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.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 

Saved Queries

Sign in to use this feature.

Search Filter Reset All

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
remove_circle_outline
Add Subjects Reset
Select Subjects

Journals

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
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
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
remove_circle_outline
remove_circle_outline
Add Journals Reset
Select Journals

Article Types

remove_circle_outline
remove_circle_outline
Add Article Types Reset
Select Article Types

Countries / Regions

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
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
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
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
Add Countries / Regions Reset
Select Countries / Regions
Update Search
share announcement

Search Results (98)

Search Parameters:
Keywords = pore-water electrical conductivity

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
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 616
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 447
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 545
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 563
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 609
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
Viewed by 811
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 1 | Viewed by 1592
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 Section Polymer Composites and Nanocomposites)
►▼ 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 801
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
(This article belongs to the Topic Formation, Exploration and Development of Natural Gas Hydrate)
►▼ 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 882
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 3 | Viewed by 2456
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 θm-εa0.5 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 εa-σb, 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 ">
12 pages, 4884 KiB  
Article
Effect of Freeze–Thaw and Wetting–Drying Cycles on the Hydraulic Conductivity of Modified Tailings
by Longlong Meng, Liangxiong Xia, Min Xia, Shaokai Nie, Jiakai Chen, Wenyuan Wang, Aifang Du, Haowen Guo and Bate Bate
Geosciences 2024, 14(4), 93; https://doi.org/10.3390/geosciences14040093 - 25 Mar 2024
Viewed by 1722
Abstract
Mine tailings have shown viability as the fine–grained layer in a capillary barrier structure for controlling acid mine drainage in a circular economy. Their saturated hydraulic conductivities (ksat) under wetting–drying cycles and freeze–thaw cycles remain unexplored. In this study, modified [...] Read more.
Mine tailings have shown viability as the fine–grained layer in a capillary barrier structure for controlling acid mine drainage in a circular economy. Their saturated hydraulic conductivities (ksat) under wetting–drying cycles and freeze–thaw cycles remain unexplored. In this study, modified tailings with a weight ratio of 95:5 (tailings/hydrodesulfurization (HDS) clay from waste–water treatment) and an initial water content of 12% were used. The ksat of specimens was measured after up to 15 wetting–drying cycles, each lasting 24 h, with a drying temperature of 105 °C. The ksat for wetting–drying cycles decreased from 3.9 × 10−6 m/s to 9.5 × 10−7 m/s in the first three cycles and then stabilized in the subsequent wetting–drying cycles (i.e., 5.7 × 10−7 m/s–6.3 × 10−7 m/s). Increased fine particles due to particle breakage are the primary mechanism for the ksat trend. In addition, the migration of fines and their preferential deposition near the pore throat area may also promote this decreasing trend through the shrinking and potentially clogging–up of pore throats. This could be explained by the movement of the meniscus, increased salinity, and, subsequently, the shrinkage of the electrical diffuse layer during the drying cycle. Similar specimens were tested to measure ksat under up to 15 freeze–thaw cycles with temperatures circling between −20 °C and 20 °C at 12 h intervals. Compared to the untreated specimen (i.e., 3.8 × 10−6 m/s), the ksat after three freeze–thaw cycles decreased by 77.6% (i.e., 8.5 × 10−7 m/s) and then remained almost unchanged (i.e., 5.6 × 10−7 m/s–8.9 × 10−7 m/s) in subsequent freeze–thaw cycles. The increased fine grain content (i.e., 3.1%) can be used to explain the decreased ksat trend. Moreover, the migration of fines toward the pore throat area, driven by the advancing and receding of ice lens fronts and subsequent deposition at the pore throat, may also contribute to this trend. Full article
(This article belongs to the Special Issue Advances in Thermo-Hydro-Mechanical Characterization and Modelling of Unsaturated Soils)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic of the inclined two–layer mining capillary barrier system.</p> Full article ">Figure 2
<p>Copper mine waste: (<b>a</b>) waste rock; (<b>b</b>) tailings; (<b>c</b>) HDS clay (photo courtesy of Dr. Qiong Wang).</p> Full article ">Figure 3
<p>Particle size distributions of mining materials.</p> Full article ">Figure 4
<p>Schematic of the test apparatuses.</p> Full article ">Figure 5
<p>Test paths: (<b>a</b>) Freeze–thaw cycles; (<b>b</b>) wetting–drying cycles.</p> Full article ">Figure 6
<p>X–ray diffraction patterns of tailings and HDS clay (K: kaolinite; M: muscovite; C: clinochlore; Q: quartz; A: albite; Ca: calcite; and MI: microcline).</p> Full article ">Figure 7
<p>Percentage change in particle groups of modified tailings under freeze–thaw cycles.</p> Full article ">Figure 8
<p>The <span class="html-italic">k</span><sub>sat</sub> of modified tailings under freeze–thaw cycles.</p> Full article ">Figure 9
<p>Conceptual illustration of the micro–particles of modified tailings under freeze–thaw cycles.</p> Full article ">Figure 10
<p>Percentage change in particle groups of modified tailings under wetting–drying cycles.</p> Full article ">Figure 11
<p>The <span class="html-italic">k</span><sub>sat</sub> of modified tailings under wetting–drying cycles.</p> Full article ">Figure 12
<p>Conceptual illustration of the micro–particles of modified tailings under wetting–drying cycles.</p> Full article ">
17 pages, 5055 KiB  
Article
Influence of Concentration, Surface Charge, and Natural Water Components on the Transport and Adsorption of Polystyrene Nanoplastics in Sand Columns
by Gabriela Hul, Hande Okutan, Philippe Le Coustumer, Stéphan Ramseier Gentile, Stéphane Zimmermann, Pascal Ramaciotti, Pauline Perdaems and Serge Stoll
Nanomaterials 2024, 14(6), 529; https://doi.org/10.3390/nano14060529 - 15 Mar 2024
Cited by 1 | Viewed by 1261
Abstract
Information about the influence of surface charges on nanoplastics (NPLs) transport in porous media, the influence of NPL concentrations on porous media retention capacities, and changes in porous media adsorption capacities in the presence of natural water components are still scarce. In this [...] Read more.
Information about the influence of surface charges on nanoplastics (NPLs) transport in porous media, the influence of NPL concentrations on porous media retention capacities, and changes in porous media adsorption capacities in the presence of natural water components are still scarce. In this study, laboratory column experiments are conducted to investigate the transport behavior of positively charged amidine polystyrene (PS) latex NPLs and negatively charged sulfate PS latex NPLs in quartz sand columns saturated with ultrapure water and Geneva Lake water, respectively. Results obtained for ultrapure water show that amidine PS latex NPLs have more affinity for negatively charged sand surfaces than sulfate PS latex NPLs because of the presence of attractive electrical forces. As for the Geneva Lake water, under natural conditions, both NPL types and sand are negatively charged. Therefore, the presence of repulsion forces reduces NPL’s affinity for sand surfaces. The calculated adsorption capacities of sand grains for the removal of both types of NPLs from both types of water are oscillating around 0.008 and 0.004 mg g−1 for NPL concentrations of 100 and 500 mg L−1, respectively. SEM micrography shows individual NPLs or aggregates attached to the sand and confirms the limited role of the adsorption process in NPL retention. The important NPL retention, especially in the case of negatively charged NPLs, in Geneva Lake water-saturated columns is related to heteroaggregate formation and their further straining inside narrow pores. The presence of DOM and metal cations is then crucial to trigger the aggregation process and NPL retention. Full article
(This article belongs to the Special Issue Environmental Fate, Transport and Effects of Nanoplastics)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Breakthrough curves of amidine PS latex NPLs (–NH–NH<sub>2</sub><sup>+</sup>) and sulfate PS latex NPLs (–SO<sub>4</sub><sup>2−</sup>) injected punctually at an initial concentration of 100 mg L<sup>−1</sup> or 500 mg L<sup>−1</sup> into quartz sand columns saturated with ultrapure water. One PV ranged between 69 mL and 73.0 mL. Z-average hyrodynamic diameter (<b>b</b>) and ζ potential (<b>c</b>) changes of amidine PS latex and sulfate PS latex NPLs present in the effluent water.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Breakthrough curves of amidine PS latex NPLs (–NH–NH<sub>2</sub><sup>+</sup>) and sulfate PS latex NPLs (–SO<sub>4</sub><sup>2−</sup>) injected punctually at an initial concentration of 100 mg L<sup>−1</sup> or 500 mg L<sup>−1</sup> into quartz sand columns saturated with ultrapure water. One PV ranged between 69 mL and 73.0 mL. Z-average hyrodynamic diameter (<b>b</b>) and ζ potential (<b>c</b>) changes of amidine PS latex and sulfate PS latex NPLs present in the effluent water.</p> Full article ">Figure 2
<p>(<b>a</b>) Retained mass of different types of nanoplastics in quartz sand columns saturated with ultrapure water. (<b>b</b>) SEM image of amidine PS latex NPLs attached to the sand surface. Experimental conditions: [–NH–NH<sub>2</sub><sup>+</sup> NPLs] = 500 mg L<sup>−1</sup>, ultrapure water. (<b>c</b>) SEM image of sulfate PS latex NPLs attached to the sand surface. Experimental conditions: [–SO<sub>4</sub><sup>2−</sup> NPLs] = 500 mg L<sup>−1</sup>, ultrapure water. White arrows indicate NPLs.</p> Full article ">Figure 3
<p>(<b>a</b>) Breakthrough curves of amidine PS latex NPLs (–NH–NH<sub>2</sub><sup>+</sup>) and sulfate PS latex NPLs (–SO<sub>4</sub><sup>2−</sup>) injected punctually at an initial concentration of 100 mg L<sup>−1</sup> or 500 mg L<sup>−1</sup> into quartz sand columns saturated with Geneva Lake water. One PV ranged between 69 mL and 73.0 mL. Z-average hyrodynamic diameter (<b>b</b>) and ζ potential (<b>c</b>) changes of amidine PS latex and sulfate PS latex NPLs present in the effluent water.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Breakthrough curves of amidine PS latex NPLs (–NH–NH<sub>2</sub><sup>+</sup>) and sulfate PS latex NPLs (–SO<sub>4</sub><sup>2−</sup>) injected punctually at an initial concentration of 100 mg L<sup>−1</sup> or 500 mg L<sup>−1</sup> into quartz sand columns saturated with Geneva Lake water. One PV ranged between 69 mL and 73.0 mL. Z-average hyrodynamic diameter (<b>b</b>) and ζ potential (<b>c</b>) changes of amidine PS latex and sulfate PS latex NPLs present in the effluent water.</p> Full article ">Figure 4
<p>(<b>a</b>) Retained mass of different types of nanoplastics in quartz sand columns saturated with Geneva Lake water. (<b>b</b>,<b>c</b>) SEM image of amidine PS latex NPLs attached to the sand surface. Experimental conditions: [–NH–NH<sub>2</sub><sup>+</sup> NPLs] = 500 mg L<sup>−1</sup>, Geneva Lake water. (<b>d</b>) SEM image of sulfate PS latex NPLs attached to the sand surface. Experimental conditions: [–SO<sub>4</sub><sup>2−</sup> NPLs] = 500 mg L<sup>−1</sup>, Geneva Lake water. White arrows indicate NPLs.</p> Full article ">Figure 5
<p>Summary of nanoplastics retention mechanisms in quartz sand filtration columns. A—stabilization due to the presence of repulsive forces; B—stabilization due to the presence of repulsive forces and despite the presence of DOM and cation coating; C—aggregation of NPLs confined in a limited pore space and further aggregate straining; D—adsorption to the sand surface; E—adsorption to the surface of clay minerals; F—adsorption to the sand surface via cation bridging, G—straining of formed aggregates; H—occupation of surface active sites by positively charged water components; I—aggregation due to DOM charge screening effects; J—ripening.</p> Full article ">
12 pages, 1960 KiB  
Article
Sensor-Based Fertigation Management Enhances Resource Utilization and Crop Performance in Soilless Strawberry Cultivation
by Lucia Bonelli, Francesco Fabiano Montesano, Massimiliano D’Imperio, Maria Gonnella, Angela Boari, Beniamino Leoni and Francesco Serio
Agronomy 2024, 14(3), 465; https://doi.org/10.3390/agronomy14030465 - 26 Feb 2024
Cited by 1 | Viewed by 1382
Abstract
The use of wireless sensors for real-time sensing of substrate water status and electrical conductivity could be an effective tool for precision irrigation management in soilless cultivation. In this research, the effects of timer-based (TB) compared to smart sensor-based irrigation (SB) were investigated. [...] Read more.
The use of wireless sensors for real-time sensing of substrate water status and electrical conductivity could be an effective tool for precision irrigation management in soilless cultivation. In this research, the effects of timer-based (TB) compared to smart sensor-based irrigation (SB) were investigated. The highest consumption of fertilizers and water were recorded in TB, with nutrient solution and total applied water savings of 38% and 26%, respectively, in SB. The highest yield was obtained in SB treatment, with a total and marketable yield decrease of 7% in TB, with no differences in terms of the total soluble solids content, dry matter, firmness, juice pH and titratable acidity of the strawberry fruits. The higher yield, combined with water and nutrient saving in SB, allowed water use efficiency (fresh weight of marketable fruits per liter of total water applied) to be increased by 46% and nutrient productivity (fresh weight of marketable product per gram of nutrient supplied via nutrient solution) by 74%. The study confirms that sensor-based, compared to empiric fertigation management, ameliorates the sustainability of open, free-drain, soilless cultivation of strawberry, leading to better resource use without compromising crop performance and fruit quality. Full article
(This article belongs to the Section Water Use and Irrigation)
►▼ Show Figures

Figure 1

Figure 1
<p>Cumulative irrigation consumption (limited to the treatment periods) in timer- and sensor-based irrigation of soilless-grown strawberry.</p> Full article ">Figure 2
<p>Daily irrigation consumption (limited to the treatment periods) of soilless-grown strawberry with timer-based fertigation management.</p> Full article ">Figure 3
<p>Daily irrigation consumption (limited to the treatment periods) of soilless-grown strawberry with sensor-based fertigation management and average daily temperature trend inside the greenhouse.</p> Full article ">Figure 4
<p>Substrate volumetric water content (VWC) and pore water electrical conductivity (ECp) trends (limited to treatment periods) measured by dielectric sensors in soilless strawberry subjected to sensor-based irrigation management.</p> Full article ">Figure 5
<p>Leaching fraction electrical conductivity (EC) trend during the growing cycle (limited to treatment periods).</p> Full article ">
18 pages, 7752 KiB  
Article
Soil Substrate Characteristics for Planting Hole Greening Technology for High, Steep, Rocky Slope Vegetation in Semi-Arid Areas
by Xiaodong Chen, Tongqian Zhao, Xiaojun Nie, Xiaoming Guo and Pengbo Li
Land 2024, 13(3), 287; https://doi.org/10.3390/land13030287 - 26 Feb 2024
Viewed by 1307
Abstract
Soil substrate plays a central role in the vegetation restoration of high and steep slopes, especially in semi-arid regions. This study aims to develop an optimal soil substrate that can provide a favorable environment for the vegetation growth of the high and steep [...] Read more.
Soil substrate plays a central role in the vegetation restoration of high and steep slopes, especially in semi-arid regions. This study aims to develop an optimal soil substrate that can provide a favorable environment for the vegetation growth of the high and steep rocky slopes in semi-arid areas. Within the framework of planting hole greening technology, we developed a synthetic substrate comprising base soil, peat, water-retaining and agglomerating agents, biochar, and controlled-release compound fertilizer. We conducted pot experiments to assess the impact of compound additions on soil properties and Parthenocissus himalayana growth. Field tests on exposed, high, and steep rocky slopes in a semi-arid region validated the optimal ratio of substrate components. The results showed that the base soil-to-peat ratio significantly influenced soil density, moisture, pH, organic matter, nitrogen content, and vegetation growth (Ps < 0.05). The controlled-release compound fertilizer significantly affected soil electrical conductivity and alkali-hydrolyzable nitrogen content (Ps < 0.05). Meanwhile, the water-retaining agent, biochar, and agglomerating agent had inconsequential effects on soil characteristics and plant growth. The optimal substrate composition included a 7:3 ratio of base soil to peat, 1.5 g/L of water retainer, 10 mg/L of agglomerating agent, 5 g/L of biochar, and 5 g/L of controlled-release compound fertilizer. The field verification showed that the developed optimal substrate possessed desirable pore structure, moisture, and nutrients, resulting in excellent growth of Parthenocissus himalayana. This optimal soil substrate could be suitable for establishing vegetation on high, steep, rocky slopes in semi-arid areas using planting hole greening technology. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>The graphs of laboratory experiment setup.</p> Full article ">Figure 2
<p>The graphs of field test setup. (<b>a</b>) Exposed high and steep rocky slope; (<b>b</b>) planting holes in rocky slopes; (<b>c</b>) <span class="html-italic">Parthenocissus himalayana</span> in the planting hole; (<b>d</b>) automatic temperature and humidity monitor.</p> Full article ">Figure 3
<p>Effects of the volume ratio of base soil to peat on (<b>a</b>) vegetation growth and (<b>b</b>) substrate properties. Different letters indicate significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05); CK, control.</p> Full article ">Figure 4
<p>Effects of water-retaining agent content on (<b>a</b>) vegetation growth and (<b>b</b>) substrate properties. Different letters indicate significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05); CK, control.</p> Full article ">Figure 5
<p>Effects of agglomerating agent content on (<b>a</b>) vegetation growth and (<b>b</b>) substrate properties. Different letters indicate significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05); CK, control.</p> Full article ">Figure 6
<p>Effects of biochar content on (<b>a</b>) vegetation growth and (<b>b</b>) substrate properties. Different letters indicate significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05); CK, control.</p> Full article ">Figure 7
<p>Effects of controlled-release compound fertilizer content on (<b>a</b>) vegetation growth and (<b>b</b>) substrate properties. Different letters indicate significant differences (ANOVA, <span class="html-italic">p</span> &lt; 0.05); CK, control.</p> Full article ">Figure 8
<p>Characterization of soil substrate moisture content changes under extreme monthly conditions.</p> Full article ">Figure 9
<p>Effect of (<b>a</b>) light and (<b>b</b>) rainfall events on the moisture content of soil substrates at different depths in planting holes.</p> Full article ">Figure 10
<p>Growth of <span class="html-italic">Parthenocissus himalayana</span> planted in drilled holes filled with a synthetic soil substrate in a rock slope. (<b>a</b>) Vegetation growth of local area, (<b>b</b>) growth of individual plant.</p> Full article ">Figure 11
<p>Characterization of temperature changes at different depths of optimal soil substrates in planting holes on a rocky slope under extreme monthly conditions.</p> Full article ">
16 pages, 13903 KiB  
Article
Research on CeO2 Activated Carbon Electrode Capacitance Method for Sulfate Removal from Mine Water
by Xiujuan Feng, Yanjun Zou, Sékou Mohamed Condé, Xiaoqing Wang and Chengliang Dong
Water 2024, 16(5), 675; https://doi.org/10.3390/w16050675 - 25 Feb 2024
Cited by 1 | Viewed by 1560
Abstract
Sulfate is a typical characteristic pollutant in mine water. Because of its high concentration and large discharge of mine water, it has become a difficult problem in mineral exploitation. Capacitive deionization (CDI) is an innovative and economical removal technology. There are few reports [...] Read more.
Sulfate is a typical characteristic pollutant in mine water. Because of its high concentration and large discharge of mine water, it has become a difficult problem in mineral exploitation. Capacitive deionization (CDI) is an innovative and economical removal technology. There are few reports on the use of CDI to remove SO42− from mine water. In this study, a CeO2 activated carbon electrode with good wettability, excellent electrochemical performance, and suitable pore structure was prepared by the sol-gel method. The application of the CeO2 activated carbon electrode to the capacitive method for treating high SO42− mine water was investigated using simulated wastewater and actual mine water. The study structure shows that CeO2:activated carbon (AC) has the best wettability, the highest specific capacitance, and the lowest electrical conductivity when the mass ratio of CeO2 is 5%. At 100 mg/L, the electrode has the maximum SO42− ion specific adsorption capacity (SAC). At 1 V and 20 mL/min, this value is measured. The electrode has a SAC value of 9.36 mg/g, far higher than the AC electrode’s 4.1 mg/g. The effect of CDI process factors such the voltage, flow rate, and initial concentration was studied to find the best treatment method. SAC retention is 91% after 10 adsorption–desorption cycles, demonstrating outstanding electrode performance. Under the best CDI process (1.4 volts, 30 mL/min), mine water was treated. After 20 cycles of treatment, the concentration of SO42− in mine water decreased from 1170 mg/L to 276.46 mg/L, and the removal rate was 76.37%. This study proved that the CeO2 modified activated carbon electrode capacitance method can effectively remove sulfate ions and other ions from mine water. Full article
(This article belongs to the Topic Capacitive Deionization Technology for Water Treatment)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of electrode preparation.</p> Full article ">Figure 2
<p>Schematic diagram and actual picture of the test device.</p> Full article ">Figure 3
<p>(<b>a</b>) AC, (<b>b</b>) CeO<sub>2</sub>(5)-AC and (<b>c</b>) SEM image of CeO<sub>2</sub>(10)-AC.</p> Full article ">Figure 4
<p>Mapping of CeO<sub>2</sub>(5)-AC.</p> Full article ">Figure 5
<p>XRD patterns of AC, CeO<sub>2</sub>(5)-AC, and CeO<sub>2</sub>(10)-AC.</p> Full article ">Figure 6
<p>The pore size distributions and N<sub>2</sub> adsorption-desorption isotherms for AC, CeO<sub>2</sub>(5)-AC, and CeO<sub>2</sub>(10)-AC (illustration).</p> Full article ">Figure 7
<p>Contact angles of electrode materials with different CeO<sub>2</sub> activation mass ratios.</p> Full article ">Figure 8
<p>(<b>a</b>) CV curves of AC, CeO<sub>2</sub>(5)-AC and CeO<sub>2</sub>(10)-AC electrodes at a 5 mV/s scanning rate. (<b>b</b>) CV curves of the CeO<sub>2</sub>(5)-AC electrode at different scanning rates (5 mV/s, 10 mV/s, 30 mV/s, 50 mV/s, 100 mV/s, and 200 mV/s). (<b>c</b>) The specific capacitance curves of AC, CeO<sub>2</sub>(5)-AC and CeO<sub>2</sub>(10)-AC electrodes at different scanning rates.</p> Full article ">Figure 9
<p>Nyquist diagram of AC, CeO<sub>2</sub>(5)-AC, and CeO<sub>2</sub>(10)-AC.</p> Full article ">Figure 10
<p>Changes in the electro-adsorption capacity of different electrodes in the electro-adsorption process.</p> Full article ">Figure 11
<p>Effects of (<b>a</b>) different voltages, (<b>b</b>) different flow rates, and (<b>c</b>) different SO<sub>4</sub><sup>2−</sup> concentrations on the CDI performance of electrodes.</p> Full article ">Figure 12
<p>SAC changes and SAC retention rate during 10 sorption–desorption cycles of the CeO<sub>2</sub>(5)-AC electrode(Purple indicates SAC, and pink indicates SAC retention).</p> Full article ">Figure 13
<p>The Langmuir isotherm model (<b>a</b>), the Freundlich isotherm model (<b>b</b>), pseudo first-order kinetics (<b>c</b>), and pseudo second-order kinetics (<b>d</b>) of SO<sub>4</sub><sup>2−</sup> on CeO<sub>2</sub>(5)-AC electrodes.</p> Full article ">Figure 14
<p>Changes and removal rates of salt ions and heavy metals in 20 cycles of mine water treated by CDI.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 1-50 on page 1 of 2.

Go to page    1 2 chevron_right
Back to TopTop