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Keywords = complex pore structure

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20 pages, 9044 KiB  
Article
Simulation of Low-Salinity Water-Alternating Impure CO2 Process for Enhanced Oil Recovery and CO2 Sequestration in Carbonate Reservoirs
by Kwangduk Seo, Bomi Kim, Qingquan Liu and Kun Sang Lee
Energies 2025, 18(5), 1297; https://doi.org/10.3390/en18051297 - 6 Mar 2025
Viewed by 172
Abstract
This study investigates the combined effects of impurities in CO2 stream, geochemistry, water salinity, and wettability alteration on oil recovery and CO2 storage in carbonate reservoirs and optimizes injection strategy to maximize oil recovery and CO2 storage ratio. Specifically, it [...] Read more.
This study investigates the combined effects of impurities in CO2 stream, geochemistry, water salinity, and wettability alteration on oil recovery and CO2 storage in carbonate reservoirs and optimizes injection strategy to maximize oil recovery and CO2 storage ratio. Specifically, it compares the performance of pure CO2 water-alternating gas (WAG), impure CO2-WAG, pure CO2 low-salinity water-alternating gas (LSWAG), and impure CO2-LSWAG injection methods from perspectives of enhanced oil recovery (EOR) and CO2 sequestration. CO2-enhanced oil recovery (CO2-EOR) is an effective way to extract residual oil. CO2 injection and WAG methods can improve displacement efficiency and sweep efficiency. However, CO2-EOR has less impact on the carbonate reservoir because of the complex pore structure and oil-wet surface. Low-salinity water injection (LSWI) and CO2 injection can affect the complex pore structure by geochemical reaction and wettability by a relative permeability curve shift from oil-wet to water-wet. The results from extensive compositional simulations show that CO2 injection into carbonate reservoirs increases the recovery factor compared with waterflooding, with pure CO2-WAG injection yielding higher recovery factor than impure CO2-WAG injection. Impurities in CO2 gas decrease the efficiency of CO2-EOR, reducing oil viscosity less and increasing interfacial tension (IFT) compared to pure CO2 injection, leading to gas channeling and reduced sweep efficiency. This results in lower oil recovery and lower storage efficiency compared to pure CO2. CO2-LSWAG results in the highest oil-recovery factor as surface changes. Geochemical reactions during CO2 injection also increase CO2 storage capacity and alter trapping mechanisms. This study demonstrates that the use of impure CO2-LSWAG injection leads to improved oil recovery and CO2 storage compared to pure CO2-WAG injection. It reveals that wettability alteration plays a more significant role for oil recovery and geochemical reaction plays crucial role in CO2 storage than CO2 purity. According to optimization, the greater the injection of gas and water, the higher the oil recovery, while the less gas and water injected, the higher the storage ratio, leading to improved storage efficiency. This research provides valuable insights into parameters and injection scenarios affecting enhanced oil recovery and CO2 storage in carbonate reservoirs. Full article
(This article belongs to the Special Issue Oil Recovery and Simulation in Reservoir Engineering)
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<p>Phase envelope of fluid model.</p>
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<p>Depth variation of two-dimensional cross-sectional reservoir model.</p>
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<p>Relative permeability used in simulation at the end-point wetting conditions.</p>
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<p>Schematic diagram of water-alternating gas injection design.</p>
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<p>Oil viscosity changes at block (20, 1, 5).</p>
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<p>Interfacial tension at block (20, 1, 5).</p>
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<p>Cumulative gas injection during the WAG injection under reservoir conditions.</p>
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<p>Gas saturation distribution when 0.12 PV of gas is injected: (<b>a</b>) pure CO<sub>2</sub> injection; (<b>b</b>) impure CO<sub>2</sub> injection.</p>
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<p>Oil-recovery factor during pure and impure WAG injection.</p>
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<p>Pore volume changes with geochemistry: (<b>a</b>) pure CO<sub>2</sub> injection; (<b>b</b>) impure CO<sub>2</sub> injection.</p>
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<p>Oil-recovery changes with geochemistry: (<b>a</b>) pure CO<sub>2</sub> injection; (<b>b</b>) impure CO<sub>2</sub> injection.</p>
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<p>A comparison of CO<sub>2</sub> storage by geochemistry.</p>
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<p>Solubility trapped CO<sub>2</sub> with geochemistry.</p>
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<p>Pore volume changes with LSWI: (<b>a</b>) pure CO<sub>2</sub> injection; (<b>b</b>) impure CO<sub>2</sub> injection.</p>
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<p>Oil-recovery changes with wettability alteration: (<b>a</b>) pure CO<sub>2</sub> injection; (<b>b</b>) impure CO<sub>2</sub> injection.</p>
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<p>A comparison of CO<sub>2</sub> trapped mechanisms.</p>
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<p>Average water saturation wettability alteration: (<b>a</b>) pure CO<sub>2</sub> injection; (<b>b</b>) impure CO<sub>2</sub> injection.</p>
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<p>A comparison of oil recovery.</p>
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<p>A comparison of CO<sub>2</sub> storage.</p>
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<p>A comparison of CO<sub>2</sub> trapping mechanisms.</p>
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17 pages, 5550 KiB  
Article
Groundwater Tracer Tests as a Supporting Method for Interpreting the Complex Hydrogeological Environment of the Urbas Landslide in NW Slovenia
by Luka Serianz and Mitja Janža
Appl. Sci. 2025, 15(5), 2707; https://doi.org/10.3390/app15052707 - 3 Mar 2025
Viewed by 183
Abstract
This study investigates groundwater flow patterns in a landslide area above the settlement of Koroška Bela in NW Slovenia using a series of tracer tests with sodium chloride (NaCl) and fluorescein (uranine). The tracer experiments, using a combination of pumping tests and continuous [...] Read more.
This study investigates groundwater flow patterns in a landslide area above the settlement of Koroška Bela in NW Slovenia using a series of tracer tests with sodium chloride (NaCl) and fluorescein (uranine). The tracer experiments, using a combination of pumping tests and continuous groundwater observations, reveal two distinct groundwater flow horizons within the landslide body: a prevailing shallower flow within highly permeable gravel layers and a slower deep flow in the weathered low-permeability clastic layers. Uranine injections suggest longer retentions, indicating complex hydrogeological conditions. Groundwater is recharged by the infiltration of precipitation and subsurface inflow from the upper-lying carbonate rocks. In the upper landslide, highly permeable gravel layers accelerate flow, especially during heavy rainfall, while downstream interactions between permeable gravel and less permeable clastic materials create local aquifers and springs. These groundwater dynamics significantly influence landslide stability, as rapid infiltration during intense precipitation events can lead to transient increases in pore water pressure, reducing shear strength and potentially triggering slope movement. Meanwhile, slow deep flows contribute to prolonged saturation of critical failure surfaces, which may weaken the landslide structure over time. The study emphasizes the region’s geological heterogeneity and landslide stability, providing valuable insights into the groundwater dynamics of this challenging environment. By integrating hydrogeological assessments with engineering measures, the study provides supportive information for mitigating landslide risks and improving groundwater management strategies. Full article
(This article belongs to the Section Earth Sciences)
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<p>Geological map of the study area with landslide boundary (modified after [<a href="#B26-applsci-15-02707" class="html-bibr">26</a>]), tracer injection and sampling points.</p>
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<p>Water-level (<b>above</b>) and temperature (<b>below</b>) fluctuations in piezometers PP-4 and PP-12A.</p>
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<p>Pumping test results in PP-12A.</p>
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<p>Electroconductivity at sampling points after NaCl injection on 17 April 2020.</p>
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<p>Electroconductivity at sampling points after NaCl injection on 1 October 2020.</p>
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<p>Fluorescence concentrations at sampling points after uranine injection on 14 May 2020.</p>
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<p>Fluorescence concentrations at sampling points after uranine injection on 1 October 2020.</p>
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<p>Fluorescence concentrations at sampling points after uranine injection on 16 May 2022.</p>
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<p>Hydrogeological cross-section A-B with conceptual characteristic groundwater flow within the Urbas landslide (modified after [<a href="#B39-applsci-15-02707" class="html-bibr">39</a>]).</p>
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13 pages, 7724 KiB  
Article
Study on Quantitative Characterization Method of Micro-Pore Structure of Carbonate Reservoir Based on Nuclear Magnetic Resonance Logging
by Lun Zhao, Shuqin Wang, Jincai Wang, Jue Hou, Xing Zeng and Tianyu Zheng
Processes 2025, 13(3), 729; https://doi.org/10.3390/pr13030729 - 3 Mar 2025
Viewed by 202
Abstract
Carbonate reservoirs have various types of reservoir spaces and complex pore structures, so the evaluation of microscopic pore structures is of great significance to favorable reservoir identification. In order to accurately characterize the micro-pore structure of carbonate reservoir, this paper uses the NMR [...] Read more.
Carbonate reservoirs have various types of reservoir spaces and complex pore structures, so the evaluation of microscopic pore structures is of great significance to favorable reservoir identification. In order to accurately characterize the micro-pore structure of carbonate reservoir, this paper uses the NMR experiment, high-pressure mercury injection, and NMR logging data to establish a conversion model between the NMR T2 spectrum and the capillary pressure curve by piecewise power function method. The nuclear magnetic T2 spectrum of the reservoir is mainly bimodal, with small pore T2 ranging from 0.1 to 6 ms, the peak value being about 2 ms, and the large pore throat T2 ranging from 100 to 6000 ms. The throat radius of small pores is 0.04–0.1 μm, the peak value is 0.08 μm, and the throat of large pores is 0.1–10 μm. The Bash layer has the smallest pore throat radius and median radius, higher median pressure, and poorer pore structure. By converting the T2 spectrum of nuclear magnetic logging into a pseudo-capillary pressure curve, the continuous and quantitative characterization of reservoir pore structure parameters was achieved vertically. The secondary method has important reference significance for the quantitative characterization of pore structure in reservoirs of the same type. Full article
(This article belongs to the Section Energy Systems)
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<p>T<sub>2</sub> Spectrum distribution of nuclear magnetic resonance.</p>
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<p>(<b>a</b>) Characteristic diagram of mercury injection curve. (<b>b</b>) Distribution of pore throat radius.</p>
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<p>(<b>a</b>) First, 48 # NMR T<sub>2</sub> spectrum, (<b>b</b>) 48 # pore throat radius distribution map, (<b>c</b>) 120 # NMR T<sub>2</sub> spectrum, (<b>d</b>) 120 # pore throat radius distribution map.</p>
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<p>(<b>a</b>) Mercury injection cumulative curve of 48#, (<b>b</b>) normalized NMR spectrum cumulative curve of 48#.</p>
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<p>Cross plot of T<sub>2</sub> value and throat radius.</p>
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<p>Comparison between calculated pseudo-capillary pressure curve and measured (<b>a</b>–<b>d</b>).</p>
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<p>Cross plot of conversion coefficient and macroscopic physical parameters of small pore throat (<b>a</b>–<b>f</b>).</p>
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<p>Cross plot of conversion coefficient and macroscopic physical parameters of large pore throat (<b>a</b>–<b>f</b>).</p>
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<p>Cross plot of a and m values of large pore throat.</p>
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<p>Analysis results of pore structure of Well A.</p>
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<p>Intersection diagram of median radius in rock core and calculated median radius.</p>
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17 pages, 5477 KiB  
Article
The Impact of pH on the Pore and Structural Characteristics of Acid-Modified Bentonites in Oxalate Solutions
by Maria Taxiarchou, Danai Tsakiri and Iliana Douni
Minerals 2025, 15(3), 257; https://doi.org/10.3390/min15030257 - 1 Mar 2025
Viewed by 232
Abstract
The present study aims to create porous materials through the acid activation of bentonites using 0.5 M oxalic acid at different pH values. Two types of bentonites (containing aluminum montmorillonite and ferruginous montmorillonite) were treated with oxalate solutions at pH 1 to 5. [...] Read more.
The present study aims to create porous materials through the acid activation of bentonites using 0.5 M oxalic acid at different pH values. Two types of bentonites (containing aluminum montmorillonite and ferruginous montmorillonite) were treated with oxalate solutions at pH 1 to 5. During acid activation at the three pH values, Al, Fe, Mg and Si kinetics were monitored; the porosity of the samples was modified; and the specific surface area increased, while the crystal structure did not completely collapse. The optimum conditions occurred at pH 1, where the highest metal leaching was obtained for both samples. For the sample with aluminum smectite, the specific surface increased from 28.1 m2/g to 149 m2/g and the pore volume quadrupled. In the case of samples with ferruginous smectite, the specific surface area rose from 63. 2 m2/g to 372 m2/g and the pore volume increased sixfold. The mechanism of smectite activation was investigated, revealing that at the optimum experimental conditions, which is ferruginous bentonite activation at pH 1, the products have the highest concentration of small 30 to 50 Å pores, which is attributed to the creation of an adequate number of active sites and the formation of aluminum complexes with the oxalate anions. The modified bentonites have elevated porosity; therefore, they could be used as adsorbents in industry. Full article
(This article belongs to the Collection Clays and Other Industrial Mineral Materials)
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<p>(<b>a</b>) X-Ray diffraction of raw materials, (<b>b</b>) 060 regions of smectites [<a href="#B28-minerals-15-00257" class="html-bibr">28</a>].</p>
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<p>FT−IR spectra of raw materials [<a href="#B28-minerals-15-00257" class="html-bibr">28</a>].</p>
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<p>Pore size distribution of raw samples.</p>
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<p>Extraction of metals versus time at pH 1, 3 and 5 for AlBe-G (oxalate concentration 0.5 M, 80 °C, 2% pulp density).</p>
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<p>Extraction of metals versus time at pH 1, 3 and 5 for FeBe-I (oxalate concentration 0.5 M, 80 °C, 2% pulp density).</p>
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<p>Specific surface area, total pore volume and average pore diameter of AlBe-G treated with oxalic acid vs. pH (oxalate concentration 0.5 M, 80 °C, 2% pulp density).</p>
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<p>Pore size distribution of AlBe-G (raw and activated at pH = 1, 3 and 5; oxalate concentration 0.5 M; 80 °C; 2% pulp density).</p>
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<p>Specific surface area, total pore volume and average pore diameter of FeBe-I treated with oxalic acid vs. pH (oxalate concentration 0.5 M, 80 °C, 2% pulp density).</p>
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<p>Pore size distribution of FeBe-I (raw and activated at pH = 1, 3 and 5; oxalate concentration 0.5 M; 80 °C; 2% pulp density).</p>
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<p>FT-IR spectra of AlBe-G samples (raw and activated at pH = 1, 3 and 5; oxalate concentration 0.5 M; 80 °C; 2% pulp density).</p>
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<p>FT-IR spectra of FeBe-I samples (raw and activated at pH = 1, 3 and 5; oxalate concentration 0.5 M; 80 °C; 2% pulp density).</p>
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<p>XRD spectra of AlBe-G samples (raw and activated at pH = 1, 3 and 5; oxalate concentration 0.5 M; 80 °C; 2% pulp density).</p>
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<p>XRD spectra of FeBe-I samples (raw and activated at pH = 1, 3 and 5; oxalate concentration 0.5 M; 80 °C; 2% pulp density).</p>
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<p>Speciation of oxalate as a function of pH according to a previous study [<a href="#B36-minerals-15-00257" class="html-bibr">36</a>].</p>
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17 pages, 4442 KiB  
Article
Controllable Preparation of Low-Cost Coal Gangue-Based SAPO-5 Molecular Sieve and Its Adsorption Performance for Heavy Metal Ions
by Le Kang, Boyang Xu, Pengfei Li, Kai Wang, Jie Chen, Huiling Du, Qianqian Liu, Li Zhang and Xiaoqing Lian
Nanomaterials 2025, 15(5), 366; https://doi.org/10.3390/nano15050366 - 27 Feb 2025
Viewed by 181
Abstract
With the advancement of industrial production and urban modernization, pollution from heavy metal ions and the accumulation of solid waste have become critical global environmental challenges. Establishing an effective recycling system for solid waste and removing heavy metals from wastewater is essential. Coal [...] Read more.
With the advancement of industrial production and urban modernization, pollution from heavy metal ions and the accumulation of solid waste have become critical global environmental challenges. Establishing an effective recycling system for solid waste and removing heavy metals from wastewater is essential. Coal gangue was used in this study as the primary material for the synthesis of a fully coal gangue-based phosphorus-silicon-aluminum (SAPO-5) molecular sieve through a hydrothermal process. The SAPO-5 molecular sieve was characterized through several methods, including X-ray diffraction (XRD), scanning electron microscopy (SEM), BET surface analysis, Fourier-transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS), to examine its mineral phases, microstructure, pore characteristics, and material structure. Adsorption performance towards wastewater with Cd2+ and Pb2+ ions was investigated. It was found that the adsorption processes of these ions are well described by both the pseudo-second-order model and the Langmuir isotherm. According to the Langmuir model, the coal gangue-based SAPO-5 molecular sieve exhibited maximum adsorption capacities of 93.63 mg·g−1 for Cd2+ and 157.73 mg·g−1 for Pb2+. After five cycles, the SAPO-5 molecular sieve retained strong stability in adsorbing Cd2+ and Pb2+, with residual adsorption capacities of 77.03 mg·g−1 for Cd2+ and 138.21 mg·g−1 for Pb2+. The excellent adsorption performance of the fully solid waste coal gangue-based SAPO-5 molecular sieve is mainly attributed to its mesoporous channel effects, the complexation of -OH functional groups, and electrostatic attraction. Full article
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<p>Preparation flowchart of coal gangue-based SAPO-5 molecular sieve.</p>
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<p>(<b>a</b>) XRD pattern of CG; (<b>b</b>) XRD pattern of PCG; (<b>c</b>) SEM diagram of CG; (<b>d</b>) FESEM atlas of PCG.</p>
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<p>(<b>a</b>) XRD spectra of CG, PCG, and SAPO-5 molecular sieve; (<b>b</b>) FESEM images of the SAPO-5 molecular sieve; (<b>c</b>) EDS spectrum of the SAPO-5 molecular sieve; (<b>d</b>) N<sub>2</sub> adsorption–desorption isotherms and pore size distribution of the SAPO-5 molecular sieve; (<b>e</b>) FT-IR spectrum of the SAPO-5 molecular sieve; (<b>f</b>) High-resolution spectrum of O1s of the SAPO-5 molecular sieve.</p>
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<p>(<b>a</b>) The relationship between the initial concentration and the adsorption performance of Cd<sup>2+</sup> and Pb<sup>2+</sup> (pH: 6, adsorbent dosage: 0.05 g·L<sup>−1</sup>, adsorption temperature: 25 °C, adsorption time: 120 min); (<b>b</b>) the relationship between the adsorbent dosage and the adsorption performance of Cd<sup>2+</sup> and Pb<sup>2+</sup> (pH: 6, initial concentration: 300 mg·L<sup>−1</sup>, adsorption temperature: 25 °C, adsorption time: 120 min); (<b>c</b>) the relationship between the adsorption temperature and the adsorption performance of Cd<sup>2+</sup> and Pb<sup>2+</sup> (pH: 6, initial concentration: 300 mg·L<sup>−1</sup>, adsorbent dosage: 0.05 g·L<sup>−1</sup>, adsorption time: 120 min); (<b>d</b>) the relationship between the adsorption time and the adsorption performance of Cd<sup>2+</sup> and Pb<sup>2+</sup> (pH: 6, initial concentration: 300 mg·L<sup>−1</sup>, adsorbent dosage: 0.05 g·L<sup>−1</sup>, adsorption temperature: 25 °C).</p>
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<p>(<b>a</b>) The relationship between the pH and the adsorption performance of Cd<sup>2+</sup> (initial concentration: 300 mg·L<sup>−1</sup>, adsorbent dosage: 0.05 g·L<sup>−1</sup>, adsorption temperature: 25 °C, adsorption time: 120 min); (<b>b</b>) the relationship between the pH and the adsorption performance of Pb<sup>2+</sup> (initial concentration: 300 mg·L<sup>−1</sup>, adsorbent dosage: 0.05 g·L<sup>−1</sup>, adsorption temperature: 25 °C, adsorption time: 120 min); (<b>c</b>) kinetic fitting of Pb<sup>2+</sup> adsorption; (<b>d</b>) kinetic fitting of Cd<sup>2+</sup> adsorption; (<b>e</b>) isothermal fitting of Cd<sup>2+</sup> adsorption; (<b>f</b>) isothermal fitting of Pb<sup>2+</sup> adsorption; (<b>g</b>) thermodynamic fitting of Cd<sup>2+</sup> adsorption; (<b>h</b>) thermodynamic fitting of Pb<sup>2+</sup> adsorption; (<b>i</b>) regeneration performance of SAPO-5 molecular sieve in adsorption (pH: 6, initial concentration: 300 mg·L<sup>−1</sup>, dosage of adsorbent: 0.05 g·L<sup>−1</sup>, adsorption temperature: 25 °C, adsorption time: 120 min).</p>
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<p>Schematic of the adsorption mechanism of heavy metal ions by coal gangue-based SAPO-5 molecular sieve.</p>
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<p>(<b>a</b>) FT-IR spectra of SAPO-5, SAPO-5-Cd, and SAPO-5-Pb; (<b>b</b>) XPS survey spectra; (<b>c</b>) high-resolution spectra of C1s of SAPO-5, SAPO-5-Cd, and SAPO-5-Pb; (<b>d</b>) high-resolution spectra of O1s of SAPO-5, SAPO-5-Cd, and SAPO-5-Pb; (<b>e</b>) high-resolution spectra of Pb4f of SAPO-5-Pb; (<b>f</b>) high-resolution spectra of Cd3d of SAPO-5-Cd.</p>
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20 pages, 5784 KiB  
Article
Pore Structure Evolution of Coal After Supercritical CO2–Water–Rock Treatment: A Multifractal Analysis
by Sijian Zheng, Yanzhi Liu, Fansheng Huang, Shiqi Liu, Shuxun Sang, Xuguang Dai and Meng Wang
Fractal Fract. 2025, 9(3), 144; https://doi.org/10.3390/fractalfract9030144 - 25 Feb 2025
Viewed by 171
Abstract
The evolution of coal’s pore structure is crucial to the efficient capture of carbon dioxide (CO2) within coalbeds, as it provides both adsorption sites and seepage space for the adsorbed- and free-phase CO2, respectively. However, the conventional single fractal [...] Read more.
The evolution of coal’s pore structure is crucial to the efficient capture of carbon dioxide (CO2) within coalbeds, as it provides both adsorption sites and seepage space for the adsorbed- and free-phase CO2, respectively. However, the conventional single fractal method for characterizing pore structure fails to depict the intricacies and variations in coal pores. This study innovatively applies the low-temperature N2/CO2 sorption measurement and multifractal theory to investigate the evolution of the microporous structure of coals (e.g., from the Huainan coalfield) during the supercritical CO2(ScCO2)–water–rock interaction process. Firstly, we observed that the ScCO2–water–rock interaction does not significantly alter the coal’s pore morphology. Notably, taking the ZJ-8# sample as an example, low-temperature N2 sorption testing displayed a stable pore volume following the reaction, accompanied by an increase in specific surface area. Within the CO2 sorption testing range, the ZJ-8# sample’s pore volume remained unchanged, while the specific surface and pore width performed displayed a slight decrease. Secondly, by introducing key parameters from multifractal theory (such as Dq, α(q), τ(q), and f(α)), we assessed the heterogeneity characteristics of the coal’s pore structure before and after the ScCO2–water–rock reaction. The N2 sorption analysis reveals an increase in pore heterogeneity for the ZJ-8# sample and a decrease for the GQ-13# sample within the sorption testing range. In the context of low-temperature CO2 sorption analysis, the pore distribution complexity and heterogeneity of the GQ-11# and GQ-13# samples’ pores were escalated after ScCO2–water–rock interaction. The experimental and analysis results elucidated the dual roles of precipitation and dissolution exerted by the ScCO2–water–rock interaction on the micropores of coal reservoirs, underscoring the heterogeneous nature of the reaction’s influence on pore structures. The application of fractal theory offers a novel perspective compared to traditional pore characterization methods, significantly improving the precision and comprehensiveness of pore structure change descriptions. Full article
(This article belongs to the Special Issue Multiscale Fractal Analysis in Unconventional Reservoirs)
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<p>Schematic of the ScCO<sub>2</sub>–water–rock experimental process.</p>
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<p>Low-temperature N<sub>2</sub> sorption isotherms of the samples before and after the ScCO<sub>2</sub>–water–rock reaction. (<b>a</b>) presents the low-temperature nitrogen adsorption isotherm of the ZJ-8# sample before the SCCO<sub>2</sub>—water—rock reaction, (<b>b</b>) shows the isotherm after the reaction, (<b>c</b>) displays the isotherm of the GQ-11# sample before the reaction, (<b>d</b>) illustrates the isotherm after the reaction, (<b>e</b>) provides the isotherm of the GQ-13# sample before the reaction, and (<b>f</b>) depicts the isotherm after the reaction.</p>
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<p>PSD of the samples from low-temperature N<sub>2</sub> testing before and after the ScCO<sub>2</sub>–water–rock reaction. (<b>a</b>) presents the PSD of the samples from low-temperature N<sub>2</sub> testing before and after the ScCO<sub>2</sub>–water–rock reaction of sample ZJ-8#; (<b>b</b>) presents the PSD of the samples from low-temperature N<sub>2</sub> testing before and after the ScCO<sub>2</sub>–water–rock reaction of sample GQ-11#; (<b>c</b>) presents the PSD of the samples from low-temperature N<sub>2</sub> testing before and after the ScCO<sub>2</sub>–water–rock reaction of sample GQ-13#.</p>
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<p>Isothermal adsorption lines from low-temperature CO<sub>2</sub> testing before and after the ScCO2–water–rock reaction. (<b>a</b>) presents the isothermal adsorption lines from low-temperature CO<sub>2</sub> testing before the ScCO2–water–rock reaction; (<b>b</b>) presents the isothermal adsorption lines from low-temperature CO<sub>2</sub> testing after the ScCO2–water–rock reaction.</p>
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<p>PSD from low-temperature CO<sub>2</sub> testing before and after the ScCO2–water–rock reaction. (<b>a</b>) presents the PSD from low-temperature CO<sub>2</sub> testing before and after the ScCO2–water–rock reaction of sample ZJ-8#; (<b>b</b>) presents the PSD from low-temperature CO<sub>2</sub> testing before and after the ScCO2–water–rock reaction of sample GQ-11#; (<b>c</b>) presents the PSD from low-temperature CO<sub>2</sub> testing before and after the ScCO2–water–rock reaction of sample GQ-13#.</p>
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<p>Multifractal parameter plots from N<sub>2</sub> testing before and after the ScCO2–water–rock reaction. (<b>a</b>) presents the multifractal <span class="html-italic">q</span>-<span class="html-italic">D<sub>q</sub></span> spectrum of the pore size distribution for the ZJ-8# sample prior to the SCCO<sub>2</sub>—water—rock reaction, (<b>b</b>) shows the spectrum for the same sample after the reaction, (<b>c</b>) displays the multifractal <span class="html-italic">τ</span>(<span class="html-italic">q</span>)-<span class="html-italic">q</span> spectrum of the pore size distribution for the GQ-11# sample before the reaction, (<b>d</b>) illustrates the spectrum after the reaction, (<b>e</b>) provides the multifractal <span class="html-italic">f</span>(<span class="html-italic">α</span>)-<span class="html-italic">α</span> spectrum of the pore size distribution for the GQ-13# sample before the reaction, and (<b>f</b>) depicts the spectrum after the reaction.</p>
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<p>Multifractal parameters from CO<sub>2</sub> testing before and after the ScCO2–water–rock reaction. (<b>a</b>) presents the multifractal <span class="html-italic">q</span>-<span class="html-italic">D<sub>q</sub></span> spectrum of the pore size distribution for the ZJ-8# sample prior to the SCCO<sub>2</sub>—water—rock reaction, (<b>b</b>) shows the spectrum for the same sample after the reaction, (<b>c</b>) displays the multifractal <span class="html-italic">τ</span>(<span class="html-italic">q</span>)-<span class="html-italic">q</span> spectrum of the pore size distribution for the GQ-11# sample before the reaction, (<b>d</b>) illustrates the spectrum after the reaction, (<b>e</b>) provides the multifractal <span class="html-italic">f</span>(<span class="html-italic">α</span>)-<span class="html-italic">α</span> spectrum of the pore size distribution for the GQ-13# sample before the reaction, and (<b>f</b>) depicts the spectrum after the reaction.</p>
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<p>The evolution of coal’s micropore structure during the ScCO<sub>2</sub> fluid reaction process.</p>
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10 pages, 2235 KiB  
Article
Enhancing C-C Coupling in CO2 Electroreduction by Engineering Pore Size of Porous Carbon-Supported Cu Catalysts
by Aiming Huang, Jiayue Yu, Junjun Zhang, Yifan Zhang, Yang Wu, Yong Wang and Wen Luo
Catalysts 2025, 15(3), 199; https://doi.org/10.3390/catal15030199 - 20 Feb 2025
Viewed by 335
Abstract
The electroreduction of CO2 (CO2RR) is a promising and environmentally sustainable approach to closing the carbon cycle. However, achieving high activity and selectivity for multicarbon (C2₊) products remains a significant challenge due to the complexity of reaction pathways. [...] Read more.
The electroreduction of CO2 (CO2RR) is a promising and environmentally sustainable approach to closing the carbon cycle. However, achieving high activity and selectivity for multicarbon (C2₊) products remains a significant challenge due to the complexity of reaction pathways. In this study, porous carbon-supported copper catalysts (CuHCS) with pore sizes of 120 nm (CuHCS120) and 500 nm (CuHCS500) were synthesized to tailor the microenvironment at the electrode–electrolyte interface and enhance product selectivity. CuHCS120 achieved a maximum faradaic efficiency (FE) for C2₊ products of 46%, double that of CuHCS500 (23%). In contrast, CuHCS500 showed a higher FE for CO (36%) compared to CuHCS120 (14%) at the same potential. In-depth ex situ and in situ investigations revealed that smaller pores promote the enrichment and adsorption of *CO intermediates, thereby enhancing C–C coupling and the formation of C2₊ products. These findings underscore the critical role of structural confinement in modulating the catalytic microenvironment and provide valuable insights for the rational design of advanced catalysts for CO2RR. Full article
(This article belongs to the Special Issue Nanostructured Materials for Photocatalysis and Electrocatalysis)
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<p>(<b>a</b>) XRD patterns of 120 nm and 500 nm carbon supports and the corresponding CuHCS (20 wt.%) catalysts. SEM images of CuHCS (20 wt.%) catalysts on 120 nm (<b>b</b>) and 500 nm (<b>c</b>) carbon supports. (<b>d</b>) HAADF-STEM images of CuHCS120 (20 wt.%) and (<b>e</b>) CuHCS500 (20 wt.%) samples with corresponding element maps.</p>
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<p>Faraday efficiency diagram for (<b>a</b>) CuHCS120 (20 wt.%), (<b>b</b>) CuHCS500 (20 wt.%) at different applied potentials. (<b>c</b>) The enhancement factor of C<sub>2</sub>H<sub>4</sub> and CO at different potentials, defined as the ratio of the partial current densities of these products on CuHCS120 (20 wt.%) to the partial current densities on CuHCS500 (20 wt.%). (<b>d</b>) FE and current density of C<sub>2+</sub> products on CuHCS120 (20 wt.%) and CuHCS500 (20 wt.%) at different applied potentials.</p>
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<p>(<b>a</b>) ATR-SEIRAS spectra of the CO<sub>2</sub>RR on the CuHCS120 (20 wt.%) and (<b>b</b>) CuHCS500 (20 wt.%) in CO<sub>2</sub>-saturated 0.1 M KHCO<sub>3</sub>. The background spectrum is obtained at open circuit potential.</p>
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16 pages, 5023 KiB  
Article
Study on the Influence of Crystal Plugging on the Mechanical Behavior of Karst Tunnel Lining Structure
by Yehao Wang, Shiyang Liu, Li Zhang, Shaojie Guan, Zongzhi Li, Liang Cheng, Jie Liu and Jie Chen
Processes 2025, 13(2), 568; https://doi.org/10.3390/pr13020568 - 17 Feb 2025
Viewed by 329
Abstract
The blockage of a tunnel drainage system has a significant impact on the stability and operation safety of a tunnel lining structure. In this paper, changes in the pore water pressure, stress and displacement of a tunnel lining under different blocking conditions are [...] Read more.
The blockage of a tunnel drainage system has a significant impact on the stability and operation safety of a tunnel lining structure. In this paper, changes in the pore water pressure, stress and displacement of a tunnel lining under different blocking conditions are studied by means of indoor test and numerical simulation. The results show that the calcium carbonate crystallization phenomenon in the tunnel’s initial support concrete gradually appears with the passage of time, which leads to a decline in concrete quality and has a negative impact on its compressive strength and elastic modulus. The pore water pressure, stress and displacement increase with the precipitation of calcium carbonate crystals and the intensification of drainage system blockage. However, the influence of calcium carbonate crystallization on pore water pressure, stress and displacement is relatively limited within 40 days. The study provides a reference for tunnel construction in complex geological environments. Full article
(This article belongs to the Section Materials Processes)
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<p>Surface crystallization of concrete at different crystallization days.</p>
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<p>Analysis of concrete quality change.</p>
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<p>Change analysis of concrete compressive strength.</p>
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<p>Change analysis of concrete elastic modulus.</p>
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<p>Schematic diagram of equivalent permeability coefficient of lining.</p>
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<p>Calculation model.</p>
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<p>Cloud chart of pore water pressure under different working conditions.</p>
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<p>Pore water pressure values at various parts of the tunnel.</p>
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<p>Cloud chart of tunnel lining displacement (taking condition 1 as an example).</p>
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<p>Maximum displacement of tunnel lining.</p>
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<p>Cloud chart of tunnel lining stress (taking condition 1 as an example).</p>
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<p>Maximum principal stress of tunnel lining under different working conditions.</p>
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21 pages, 20678 KiB  
Article
Estimation of Equivalent Pore Aspect Ratio in Rock Physics Models and Validation Using Digital Rocks
by Luiz Eduardo Queiroz, Dario Grana, Celso Peres Fernandes, Tapan Mukerji, Leandro Passos de Figueiredo and Iara Frangiotti Mantovani
Geosciences 2025, 15(2), 67; https://doi.org/10.3390/geosciences15020067 - 15 Feb 2025
Viewed by 318
Abstract
Complex pore structures with multiple inclusions challenge the predictive accuracy of rock physics models. This study introduces a novel method for estimating a single equivalent pore aspect ratio that optimizes rock physics model predictions by minimizing discrepancies with experimental measurements in porous rocks [...] Read more.
Complex pore structures with multiple inclusions challenge the predictive accuracy of rock physics models. This study introduces a novel method for estimating a single equivalent pore aspect ratio that optimizes rock physics model predictions by minimizing discrepancies with experimental measurements in porous rocks with multiple inclusions with variable aspect ratios and proportions. The proposed methodology uses digital rock physics numerical simulations for validation. A comparative analysis is conducted between the equivalent aspect ratio derived from optimized rock physics models, numerical simulations, and the aspect ratio distribution estimated from digital rock samples. The approach is tested on both synthetic and real core samples, demonstrating its robustness and applicability to field data, including core samples and well log data. The validation results confirm that the method enhances predictive accuracy and offers a versatile framework for addressing pore complexity in subsurface rock formations. Full article
(This article belongs to the Section Geophysics)
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<p>Sensitivity of equivalent pore aspect ratio (EPAR) to RPM parameters. The EPAR values are obtained by optimizing the aspect ratio value of a single inclusion. The EPAR values are plotted as function of the volume fraction of the inclusions with aspect ratio <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math>. Their variations are shown with respect to multiple values of the following parameters: porosity <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> (<b>a</b>), matrix bulk modulus <math display="inline"><semantics> <msub> <mi>K</mi> <mi>m</mi> </msub> </semantics></math> (<b>b</b>), matrix shear modulus <math display="inline"><semantics> <msub> <mi>μ</mi> <mi>m</mi> </msub> </semantics></math> (<b>c</b>), aspect ratio of inclusion 1 <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math> (<b>d</b>), and aspect ratio of inclusion 2 <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math> (<b>e</b>). In each plot, one parameter varies, whereas the others are kept constant and equal to the reference parameters.</p>
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<p>Equivalent pore aspect ratio (EPAR) for porous medium with inclusions of aspect ratios <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math> and variable volumetric proportions for different rock physics models. The colored lines represent predictions for different values of (<b>a</b>) porosity, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math>. In each plot, one parameter varies, whereas the others are kept constant and equal to the reference parameters.</p>
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<p>EPAR for porous rocks with 3 inclusion types: (<b>a</b>) EPAR variations for variable inclusion proportions and (<b>b</b>) EPAR variations in limiting cases with two inclusions only.</p>
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<p>Example of synthetic digital sample with ellipsoidal inclusions.</p>
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<p>Comparison between RPM predictions and DRP numerical simulations for synthetic digital images with constant aspect ratio for bulk and shear moduli. The stars represent the values of the numerical simulations, and the lines represent the RPM predictions.</p>
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<p>Relative error between RPM predictions and DRP numerical simulations for synthetic digital images for bulk and shear moduli.</p>
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<p>Comparison between RPM predictions and DRP numerical simulations for synthetic digital images with two inclusion types with aspect ratios <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> for bulk and shear moduli. The stars represent the values of the numerical simulations, and the lines represent the RPM predictions.</p>
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<p>Bulk and shear moduli calculated using DRP numerical simulations in GeoDict. The stars represent the calculated porosity and elastic moduli of each sub-sample; the colors represent each sample type. The dashed-dotted lines represent the RPM predictions assuming different mineralogical compositions consistent with the samples for three different values of aspect ratios equal to 0.1, 0.2, and 0.3.</p>
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<p>Estimated EPAR of digital images of 80 sub-samples for four RPMs. Each color represents a different rock type.</p>
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<p>Estimated bulk and shear moduli of digital images of 80 sub-samples for four RPMs. Circles indicate values of elastic moduli numerically calculated using DRP; crosses indicate elastic moduli calculated using RPMs with the calculated EPARs. Each color represents a different rock type.</p>
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<p>Aspect ratio distribution calculated from digital image analysis: on the (<b>left</b>), we show the distribution of aspect ratios; on the (<b>right</b>), we show the volume-weighted distribution of aspect ratios for sample BVE from lower Barra Velha formation (blue histograms) and sample ITP from Itapema formation (orange histograms).</p>
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<p>Pore space separation into individual objects (3D and 2D views) or sample BVE from lower Barra Velha formation (<b>left</b>) and sample ITP from Itapema formation (<b>right</b>). Each color represents an object considered as an individual pore.</p>
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<p>Measured and computed log data. From (<b>left</b>) to (<b>right</b>): porosity, estimated EPAR, and bulk and shear moduli. Black lines represent well logs, blue lines represent RPM predictions using EPAR, and golden lines represent EPAR mean values for each formation. The background colors represent stratigraphic zones corresponding to the intervals upper and lower Barra Velha and Itapema, respectively.</p>
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<p>Crossplot of bulk modulus versus porosity for well log data. The smaller dots represent well log measurements, the larger dots represent the laboratory measurements in core samples, and the colored lines represent the rock physics model for different values of the aspect ratio. Two thin sections from core samples illustrate the different pore structure and shape in the stratigraphic zones. The colors represent three stratigraphic zones corresponding to the intervals upper and lower Barra Velha and Itapema.</p>
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13 pages, 3665 KiB  
Article
Molecular Structure of the mRNA Export Factor Gle1 from Debaryomyces hansenii
by Min Jeong Jang, Soo Jin Lee and Jeong Ho Chang
Int. J. Mol. Sci. 2025, 26(4), 1661; https://doi.org/10.3390/ijms26041661 - 15 Feb 2025
Viewed by 225
Abstract
Gle1 functions as a regulator of Dbp5, a DEAD-box-containing RNA helicase that is a component of the nuclear pore complex. In association with Gle1 and inositol hexakisphosphate (IP6), ADP-bound Dbp5 facilitates the release of RNA. The RNA-bound Dbp5 undergoes ATP hydrolysis and is [...] Read more.
Gle1 functions as a regulator of Dbp5, a DEAD-box-containing RNA helicase that is a component of the nuclear pore complex. In association with Gle1 and inositol hexakisphosphate (IP6), ADP-bound Dbp5 facilitates the release of RNA. The RNA-bound Dbp5 undergoes ATP hydrolysis and is activated by Gle1 in the presence of IP6. The formation of a ternary complex involving Dbp5, Gle1, and the nucleoporin Nup159 promotes ADP secretion and prevents RNA recombination. To date, several complex structures of Gle1 with its binding partners have been described; however, the structure of unbound Gle1 remains elusive. To investigate the structural features associated with complex formation, the crystal structure of N-terminally truncated Gle1 from Debaryomyces hansenii (DhGle1ΔN) was determined at a resolution of 1.5 Å. The DhGle1ΔN protein comprises 13 α-helices. Structural comparisons with homologs, all of which have been characterized in various complexes, revealed no significant conformational changes. However, several distinct secondary structural elements were identified in α1, α3, α4, and α8. This study may provide valuable insights into the architecture of yeast Gle1 proteins and their interactions with Dbp5, which is crucial for understanding the regulation of mRNA export. Full article
(This article belongs to the Special Issue Advanced Research on Protein Structure and Protein Dynamics)
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<p>Overall structure of N-terminally truncated Gle1 from <span class="html-italic">Debaryomyces hansenii</span>. (<b>A</b>) An amino acid sequence alignment of four N-terminally truncated Gle1 proteins is shown: <span class="html-italic">Dh</span>Gle1 from <span class="html-italic">Debaryomyces hansenii</span>, <span class="html-italic">Sp</span>Gle1 from <span class="html-italic">Schizosaccharomyces pombe</span>, <span class="html-italic">Kl</span>Gle1 from <span class="html-italic">Kluyveromyces lactis</span>, and <span class="html-italic">Sc</span>Gle1 from <span class="html-italic">Saccharomyces cerevisiae</span>. Conserved residues with 100% identity among all four proteins are highlighted in red. Small and large black dots above the sequences are placed every ten and fifty residues, respectively. (<b>B</b>) The predicted structure of full-length Gle1 by Alpha-Fold 2 is presented by ribbon diagram. The N-terminal truncated region (residue 1–219) is colored in orange. (<b>C</b>) The overall structure of the N-terminally truncated Gle1 from <span class="html-italic">D. hansenii</span> (<span class="html-italic">Dh</span>Gle1ΔN), characterized in this study, is presented as a ribbon diagram. The N-terminal extra segment is colored red.</p>
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<p>N-terminal extra segment and crystallographic packing of <span class="html-italic">Dh</span>Gle1ΔN. (<b>A</b>) Electron density representation of the N-terminal extra segment, which consists of Gly-Pro-His-Met residues. (<b>B</b>) A detailed view of the N-terminal extra segment (in yellow), showing the hydrogen bonds (illustrated as red dotted lines) it forms with surrounding residues. The bonding distances are provided for each bond. (<b>C</b>) A simplified depiction that includes detailed information regarding the long and short N-terminal extra segments of His6_<span class="html-italic">Dh</span>Gle1ΔN and <span class="html-italic">Dh</span>Gle1ΔN. (<b>D</b>) Crystallographic packing of <span class="html-italic">Dh</span>Gle1ΔN, presented from different perspectives rotated by 90° along the <span class="html-italic">y</span>-axis, with the N-terminal extra segment colored in red.</p>
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<p>Structural overlays comparing <span class="html-italic">Dh</span>Gle1ΔN with four homologs: (<b>A</b>) Gle1 from <span class="html-italic">S. cerevisiae</span>, (<b>B</b>) Gle1 from <span class="html-italic">H. sapiens</span>, (<b>C</b>) Gle1 from <span class="html-italic">C. thermophilum</span>, and (<b>D</b>) eIF4G from <span class="html-italic">S. cerevisiae</span>. Regions that exhibit structural differences among the proteins are highlighted with red dashed boxes. The flexible invisible loop in the N-terminal region of <span class="html-italic">Sc</span>eIF4GΔN (<b>D</b>) is depicted as a magenta dashed line and is marked with a question mark.</p>
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<p>IP<sub>6</sub> binding site of <span class="html-italic">Dh</span>Gle1ΔN. (<b>A</b>) The chemical structure of IP<sub>6</sub>. (<b>B</b>) Superimposed structures of <span class="html-italic">Dh</span>Gle1ΔN and <span class="html-italic">Sc</span>Gle1ΔN in complex with IP<sub>6</sub> (left panel), along with a detailed view of the interactions between modeled IP6 and <span class="html-italic">Dh</span>Gle1ΔN (right panel). (<b>C</b>) Electrostatic surface representations of <span class="html-italic">Dh</span>Gle1ΔN, <span class="html-italic">Sc</span>Gle1ΔN, <span class="html-italic">Ct</span>Gle1ΔN, and <span class="html-italic">Hs</span>Gle1ΔN. The regions colored in red denote negatively charged surfaces, while those in blue indicate positively charged surfaces. The positively charged IP6 binding pockets of <span class="html-italic">Dh</span>Gle1ΔN, <span class="html-italic">Sc</span>Gle1ΔN, <span class="html-italic">Ct</span>Gle1ΔN, and <span class="html-italic">Hs</span>Gle1ΔN are highlighted by yellow dashed circles.</p>
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<p>Size-exclusion chromatography (SEC) analysis of the <span class="html-italic">Dh</span>Gle1ΔN and <span class="html-italic">Dh</span>Dbp5 proteins. (<b>A</b>) SEC peak profiles (upper panel) and SDS–PAGE analysis (lower panel) of <span class="html-italic">Dh</span>Gle1ΔN and <span class="html-italic">Dh</span>Dbp5 in the presence of 10 mM ATP. The three peaks are labeled with their respective elution volumes. Fractions corresponding to DhDbp5 and DhGle1ΔN are marked with (*) and (**), respectively. The lanes are indicated as follows: M, standard molecular weight markers; 8–19, SEC-eluted fractions. (<b>B</b>) SEC peak profiles (upper panel) and SDS–PAGE analysis (lower panel) of <span class="html-italic">Dh</span>Gle1ΔN and <span class="html-italic">Dh</span>Dbp5 in the presence of 10 mM ATP and 10 mM IP6. Both <span class="html-italic">Dh</span>Dbp5 and <span class="html-italic">Dh</span>Gle1ΔN fractions are indicated by (*) and (**), respectively. The lanes are indicated as follows: M, standard molecular weight markers; 8–18, SEC-eluted fractions. (<b>C</b>) SEC peak profiles (upper panel) and SDS–PAGE analysis (lower panel) of <span class="html-italic">Dh</span>Gle1ΔN<sup>A313R</sup> and <span class="html-italic">Dh</span>Dbp5 in the presence of 10 mM ATP and 100 mM IP6. Similarly, fractions for <span class="html-italic">Dh</span>Dbp5 and <span class="html-italic">Dh</span>Gle1ΔN are indicated by (*) and (**), respectively. The lanes are marked as follows: M, standard molecular weight markers; 7–16, 22, SEC-eluted fractions. All reacted proteins were subjected to SEC analysis using a Superose-12 column (GE Healthcare, Mississauga, ON, Canada) with a flow rate of 0.5 mL/min by using AK-TA Pure (GE Healthcare, Mississauga, ON, Canada). A buffer used for SEC contained 25 mM Tris-Cl (pH 7.5), 150 mM NaCl, 2 mM DTT. All the SDS-PAGE analyses were conducted using a 15% acrylamide gel with a 180 voltage for 70 min.</p>
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22 pages, 5917 KiB  
Article
Development of a Widely Accessible, Advanced Large-Scale Microfluidic Airway-on-Chip
by Brady Rae, Gwenda F. Vasse, Jalal Mosayebi, Maarten van den Berge, Simon D. Pouwels and Irene H. Heijink
Bioengineering 2025, 12(2), 182; https://doi.org/10.3390/bioengineering12020182 - 13 Feb 2025
Viewed by 610
Abstract
On-chip microfluidics are advanced in vitro models that simulate lung tissue’s native 3D environment more closely than static 2D models to investigate the complex lung architecture and multifactorial processes that lead to pulmonary disease. Current microfluidic systems can be restrictive in the quantities [...] Read more.
On-chip microfluidics are advanced in vitro models that simulate lung tissue’s native 3D environment more closely than static 2D models to investigate the complex lung architecture and multifactorial processes that lead to pulmonary disease. Current microfluidic systems can be restrictive in the quantities of biological sample that can be retrieved from a single micro-channel, such as RNA, protein, and supernatant. Here, we describe a newly developed large-scale airway-on-chip model that employs a surface area for a cell culture wider than that in currently available systems. This enables the collection of samples comparable in volume to traditional cell culture systems, making the device applicable to any workflow utilizing these static systems (RNA isolation, ELISA, etc.). With our construction method, this larger culture area allows for easier handling, the potential for a wide range of exposures, as well as the collection of low-quantity samples (e.g., volatiles or mitochondrial RNA). The model consists of two large polydimethylsiloxane (PDMS) cell culture chambers under an independent flow of medium or air, separated by a semi-permeable polyethylene (PET) cell culture membrane (23 μm thick, 0.4 μm pore size). Each chamber carries a 5 × 18 mm, 90 mm2 (92 mm2 with tapered chamber inlets) surface area that can contain up to 1–2 × 104 adherent structural lung cells and can be utilized for close contact co-culture studies of different lung cell types, including airway epithelial cells, fibroblasts, smooth muscle cells, and endothelial cells. The parallel bi-chambered design of the chip allows for epithelial cells to be cultured at the air–liquid interface (ALI) and differentiation into a dense, multi-layered, pseudostratified epithelium under biological flow rates. This millifluidic airway-on-chip advances the field by providing a readily reproducible, easily adjustable, and cost-effective large-scale fluidic 3D airway cell culture platform. Full article
(This article belongs to the Special Issue Microfluidics and Sensor Technologies in Biomedical Engineering)
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<p>Chip construction procedure. Detailed step-by-step visual guide to sealing a chip via the stamp method. Prepared and sterilized PDMS chip halves were rolled with a PDMS Mortar (5:7 PDMS–toluene) with a chemical-resistant rubber roller (*). The pre-cut and coated membrane was placed on the wet PDMS, mortar was applied to the top, and it was placed under a vacuum for 72 h to cure at RT. Both chip halves were treated in a plasma cleaning oven (320 mBarr 30 s) and sealed together with the application of a little manual force. The device was then prepared for cell seeding, an example of this can be seen with air-exposed epithelial cells and mesenchymal cells in co-culture. Created with BioRender.com.</p>
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<p>Complete experimental setup of the large scale airway-on-chip. Four chips attached to the Porcupine peristaltic pump (A). Each of the chambers in the chips was attached to a separate pump (B1/2), on the left of each pump the triangular bubble trap (C) can be seen, and on the right is the 1 mL media reservoir (D). Before the medium flowed into the chip, it was dropped into an Eppendorf that reduced flow variation (E). Cell culture areas of 92 mm<sup>2</sup> can be seen being cultured and submerged in the center of each device (F).</p>
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<p>Airway-on-chip design and structure. (<b>A</b>) Outline of the airway-on-chip design, top-down image produced from a render of the mold within fusion360. (<b>B</b>) A transverse view down the length of the chip, the apical and basal chambers can be seen containing the white arrows, and the surrounding PDMS contains the black arrows. White arrows indicate the flat membrane placement resulting from the RT cure. Black arrows highlight the tight PDMS binding between the top and bottom halves around the PET membrane. (<b>C</b>) Transverse and isometric views of the actual device. This shows the outcome of the mold (<b>A</b>), the perspective seen magnified in (<b>B</b>), and the clear optical view through the culture chamber that was imaged through in (<b>D</b>). The inner PDMS surface of the apical chamber produced from the surface of the mold was micro-milled from the above design. The location of the zoom in the design is indicated by the white arrows. (<b>D</b>) Growth of epithelial airway cells within the airway-on-chip system. The images show clear optical resolution of representative cultures of a confluent monolayer of human epithelial lung cells (Calu-3) grown on the PET membrane (<b>B</b>) within the chip device.</p>
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<p>Cell viability in the chip devices was not altered by construction with or without toluene as a thinning agent in the PDMS mortar. Calu-3 cells were seeded at 6.5 × 10<sup>4</sup> cells per chip and upon reaching confluency were incubated overnight before an AlamarBlue assay was performed on the supernatant and TrypanBlue on the cells. Differences between groups prepared with and without toluene were tested by unpaired Student’s <span class="html-italic">t</span> test, <span class="html-italic">p</span> &gt; 0.5 = ns (not significant).</p>
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<p>Junctional expression of cell–cell contact proteins in Calu-3 cells grown in the chips and mucus production after air exposure in the device. Calu-3 cells were grown to confluency and cultured for 10 days under a continuous flow rate of 150 μL/h in the basal compartment and either medium- or air-exposed from the apical side. After 10 days, the membranes were removed and stained. (<b>A</b>,<b>B</b>) Immunostaining of MUC5AC. (<b>C</b>,<b>D</b>) Immunostaining of E-cadherin. (<b>E</b>,<b>F</b>) Immunostaining of ZO-1. The left panels show stains performed on Calu-3 cells grown submerged for 10 days post confluency. The right panels show the stains on Calu-3 cells grown air-exposed for 10 days post confluency All fluorescent stains were counterstained with DAPI and pseudo-colored after imaging. Representatives images of 3 independent experiments are shown.</p>
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<p>Alcian Blue staining of human lung Calu-3 epithelial cells grown in the chips after air exposure in the device. Calu-3 cells were grown to confluency and cultured for 10 days under a continuous flow rate of 150 μL/h in the basal compartment and either medium- or air-exposed from the apical side. At the end of culturing, the membranes were removed and stained. (<b>A</b>,<b>B</b>) Transverse view of Alcian Blue staining of Calu-3 cells grown for 10 days submerged (<b>A</b>) and air-exposed (<b>B</b>).</p>
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<p>MUC5AC secretion from Calu-3 cells cultured in Transwell inserts and the airway-on-chip device was increased upon air exposure. Calu-3 cells were seeded in the chip device and on Transwell inserts and cultured until confluence, after which the cells were cultured submerged or air-exposed at 150 μL/h (medium and air) for 10 days, and apical washes were harvested to quantify MUC5AC secretion. Calu-3 cells (n = 4) grown for 10 days submerged or air-exposed on Transwell inserts (left) and in the airway-on-chip (right). * = <span class="html-italic">p</span> &lt; 0.05, ns = <span class="html-italic">p</span> &gt; 0.05 between indicated values as assessed by unpaired <span class="html-italic">t</span>-test.</p>
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<p>Growth of airway epithelial cells within the airway-on-chip system. Representative images of 3 independent cultures of human airway cells grown on the PET membrane within the chip device are shown. Airway fibroblasts were cultured on the basal side of the membrane, with epithelial cells cultured on top. After the cells reached confluency, the cultures were air-exposed from the apical compartment. The cells were images on days 7 and 21, and images of both layers of cells were taken top-down in the same position. Epithelial cells in the apical chamber can be seen above, fibroblasts on the other side of the membrane in the basal chamber can be seen below both at day 7 upon air exposure on the left and at day 21 on the right.</p>
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<p>Identification of epithelial cells and fibroblasts grown within the device. Airway fibroblasts were seeded into the device and allowed to attach overnight before inverting the device and seeding epithelial cells on the other side of the membrane. The cell were grown to confluency, air-exposed, and cultured for 21 days under a continuous flow rate of 150 μL/h. The membranes were removed post-culture, fixed, embedded, and mounted on slides before staining. All cells were stained with wheat germ agglutinin (WGA) to visualize the cellular phospholipid bilayers and their counterstained with DAPI. (<b>A</b>) Complete external structure of the epithelial layer in the apical chamber (transverse). (<b>B</b>) Cross-section view: produced from a Z-stack projection of images underlying (<b>A</b>,<b>C</b>) to visualize the location of the cells around the culture membrane within the device. (<b>C</b>) Complete external structure of the fibroblast layer in the basal chamber (transverse).</p>
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<p>Differentiation markers in airway epithelial cells cultured at the air–liquid interface (ALI) in static Transwells and co-cultured airway epithelial cells and fibroblasts in the airway-on-chip model. Airway epithelial cells were seeded into Transwell inserts or chips with fibroblasts cultured on the other side of the membrane. Once the cells reached confluency, they were exposed to air for 21 days and fixed for confocal staining. The cells were under a continuous flow rate of 150 μL/h in both the apical and basal compartment (air or medium). The top panels show stains for Mucin 5AC (MUC5AC), a component of mucus, forkhead box protein J1 (FOXJ1), a transcription factor involved in signaling for cilia production. The panels below show stains for cytokeratin-5 (KRT-5), a basal epithelial cells marker, and the bottom right panel shows alpha-smooth muscle actin (α-SMA) a cytoskeletal element that is specific to fibroblasts. All fluorescent stains were counterstained with DAPI and pseudo-colored after imaging. (<b>A</b>) MUC5AC and FOXJ1 on an ALI insert, (<b>B</b>) keratin (KRT)-5 stain on an ALI insert, (<b>C</b>) MUC5AC/FOXJ1 stain on a chip membrane. (<b>D</b>) KRT-5 stain on a chip membrane, (<b>E</b>) α-smooth muscle actin (SMA) stain of fibroblasts in the basal chamber of the same chip membrane as above.</p>
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<p>MUC5AC secretion in human airway epithelial cells (AECs) cultured in the airway-on-chip device was increased upon air exposure. AECs (n = 3) were seeded in the chip devices and cultured until confluence, after which the cells were air-exposed at 150 μL/h air for 7–21 days, and apical washes were harvested to quantify MUC5AC secretion. ** = <span class="html-italic">p</span> &lt; 0.01 between the indicated values as assessed by one-way ANOVA.</p>
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<p>Simulations of media and air movement through the geometry of the device. (<b>A</b>) Streamline simulations of air/media flow showing laminar flow directionality throughout the device (m/s). (<b>B</b>) Media velocities (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) in the basal media chamber. (<b>C</b>) Media velocities (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) in the apical air chamber. (<b>D</b>) Shear stress (Pa) exerted by media on the wall of the basal chamber. (<b>E</b>) Shear stress (Pa) exerted by air on the wall of the apical chamber.</p>
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15 pages, 9063 KiB  
Article
Study on the Imbibition Law of Laminated Shale Oil Reservoir During Injection and Shut-In Period Based on Phase Field Method
by Kun Yang, Shenglai Yang, Xinyue Liu, Shuai Zhao and Jilun Kang
Processes 2025, 13(2), 481; https://doi.org/10.3390/pr13020481 - 10 Feb 2025
Viewed by 454
Abstract
Laminated shale oil reservoirs feature well-developed microcracks, with significant differences in wettability on either side of these fractures. The complex pore structure of laminated shale oil reservoirs makes capillary imbibition prevalent during both water injection and well shut-in periods. Therefore, based on the [...] Read more.
Laminated shale oil reservoirs feature well-developed microcracks, with significant differences in wettability on either side of these fractures. The complex pore structure of laminated shale oil reservoirs makes capillary imbibition prevalent during both water injection and well shut-in periods. Therefore, based on the phase field method, this study investigates the imbibition behavior and the influencing factors during the injection and shut-in stage. This research shows that the imbibition mode determines the recovery rate: co-current imbibition > co-current imbibition + counter-current imbibition > counter-current imbibition. Co-current imbibition predominantly occurs in the dominant seepage channels, while counter-current imbibition mainly takes place in pore boundary regions. During the water injection stage, a low injection rate is beneficial for synergistic oil recovery through imbibition and displacement. As the injection rate increases, the capillary imbibition effect diminishes. Increased water saturation strengthens the co-current imbibition effect. Compared to injecting for 5 ms, injecting for 10 ms resulted in a 4.53% increase in imbibition recovery during the shut-in stage. The water sweep efficiency increases with the tortuosity of fractures. The wettability differences on either side of the fractures have a certain impact on imbibition. Around the fracture, the recovery in the strongly wetted area is 35% higher than that in the weakly water-wetted area. The wettability difference across fractures causes water to penetrate along the strongly water-wet pores, while only the inlet end and the pores near the fracture in the weakly water-wet zone are affected. Therefore, it is crucial to monitor the injection pressure to maximize the synergistic effects of displacement and imbibition during the development of laminated shale oil reservoirs. Additionally, surfactants should be used judiciously to prevent fingering due to wettability differences. Full article
(This article belongs to the Topic Enhanced Oil Recovery Technologies, 3rd Volume)
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<p>Schematic of the interface diffusion between two phases based on the phase field method.</p>
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<p>Schematic diagram of the geometric model and the model mesh division.</p>
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<p>Velocity cloud diagram for the single-phase flow.</p>
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<p>The oil–water distribution for different imbibition modes when t = 10 ms. (<b>a</b>) illustrate the counter-current imbibition; (<b>b</b>) illustrate the co-current imbibition and (<b>c</b>) illustrate the co-current + counter-current imbibition).</p>
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<p>Recovery and oil–water interface front displacement of different imbibition modes.</p>
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<p>Oil–water distribution and pressure distribution at different injection rates. (<b>a</b>,<b>c</b>,<b>e</b>) illustrate the oil–water distribution when injecting 0.2 PV at an injection speeds of 1 mm/s, 10 mm/s, and 100 mm/s, respectively; (<b>b</b>,<b>d</b>,<b>f</b>) illustrate the pressure distribution when injecting 0.2 PV at an injection speeds of 1 mm/s, 10 mm/s, and 100 mm/s, respectively.</p>
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<p>The movement distance of the oil–water interface front and the recovery rate at different injection rates.</p>
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<p>The recovery rate and front interface movement distance with different injection time. ((<b>a</b>,<b>b</b>) illustrate the recovery and the oil-water distribution when injection for 5 ms and 10 ms, respectively; (<b>c</b>,<b>d</b>) illustrate the front interface movement distance during processes the injection and imbibition when injection for 5 ms and 10 ms, respectively).</p>
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<p>Oil–water distribution during imbibition with different degrees of fracture tortuosity. (<b>a</b>) illustrate the oil-water distribution of Fracture 1 (τ = 1); (<b>b</b>) illustrate the oil-water distribution of Fracture 2 (τ = 1.04); (<b>c</b>) illustrate the oil-water distribution of Fracture 3 (τ = 1.21); (<b>d</b>) illustrate the oil-water distribution of Fracture 4 (τ = 1.21)).</p>
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<p>Oil–water distribution with varying degrees of fracture tortuosity over time.</p>
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<p>The movement distance of the oil–water interface front and the recovery rate with different fractures.</p>
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<p>The model considering laminal fractures and the differences in pore wettability on either side of the fractures.</p>
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<p>The pore recovery rate of different areas.</p>
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<p>The distribution of oil–water during the oil displacement process.</p>
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22 pages, 4482 KiB  
Article
Evolution of Pore Structure and Mechanical Characteristics of Red Sandstone Under Drying–Wetting Cycles
by Hongwei Deng, Shiyu Zhou, Songtao Yu, Yao Liu and Jingbo Xu
Minerals 2025, 15(2), 158; https://doi.org/10.3390/min15020158 - 7 Feb 2025
Viewed by 492
Abstract
Red sandstone is widely distributed in southern China. Due to the significant difference in mechanical properties before and after hydration and its poor water stability, red sandstone often triggers landslide accidents. In this paper, red sandstone from an open pit slope in Jiangxi [...] Read more.
Red sandstone is widely distributed in southern China. Due to the significant difference in mechanical properties before and after hydration and its poor water stability, red sandstone often triggers landslide accidents. In this paper, red sandstone from an open pit slope in Jiangxi Province was taken as the research object. Two variables, namely the initial saturation degree (25%, 50%, 75%, and 100%) and the number of wetting–drying cycles (0, 10, 20, 30, and 40), were set. With the help of nuclear magnetic resonance, the Brazilian disc test, and fractal theory, the relationships among its meso-structure, macroscopic fracture mechanics characteristics, and deterioration mechanism were analyzed. The research results are as follows: (1) Wetting–drying cycles have a significant impact on the pore structure and fracture mechanics characteristics of red sandstone. Moreover, the higher the initial saturation degree, the more obvious the deterioration effect of the wetting–drying cycles on the rock mass. (2) After further subdividing the pores according to their size for research, it was found that sandstone is mainly composed of mesopores, and the deterioration laws of different types of pores after the wetting–drying cycles are different. The porosities of total pores and macropores increase, while the proportions of mesopores and micropores decrease. The fractal dimensions of macropores and total pores of each group of rock samples are all within the range of 2–3, and the fractal dimension value increases with the increase in the number of wetting–drying cycles, showing significant and regular fractal characteristics. Micropores and some mesopores do not possess fractal characteristics. The fractal dimension of rock samples basically satisfies the rule that the larger the pore diameter, the larger the fractal dimension and the more complex the pore structure. (3) Both the type I and type II fracture toughness of rock samples decrease with the increase in the number of cycles, and the decrease is the most significant when the initial saturation degree is 100%. After 40 cycles, the decreases in type I and type II fracture toughness reach 23.578% and 30.642%, respectively. The fracture toughness is closely related to the pore structure. The porosity and fractal dimension of rock samples and their internal macropores are linearly negatively correlated with the type II fracture toughness. The development of the macropore structure is the key factor affecting its fracture mechanics performance. (4) After the wetting–drying cycles, the internal pores of red sandstone continue to develop. The number of pores increases, the pore diameter enlarges, and the proportion of macropores rises, resulting in internal damage to the rock mass. When bearing loads, the expansion and connection of internal cracks intensify, ultimately leading to the failure of the rock mass. The research results can provide important reference for the stability analysis of sandstone slope engineering. Full article
(This article belongs to the Special Issue Advances in Mine Backfilling Technology and Materials)
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<p>The flowchart of the test.</p>
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<p>(<b>a</b>) Pore division basis. (<b>b</b>) Example diagram of fractal dimension calculation.</p>
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<p><span class="html-italic">T</span><sub>2</sub> spectra of pore characteristics of sandstones with different initial saturations undergoing different numbers of wet and dry cycles. (<b>a</b>) Initial saturation of 25%; (<b>b</b>) Initial saturation of 50%; (<b>c</b>) Initial saturation of 75%; (<b>d</b>) Initial saturation of 100%.</p>
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<p>Pore distribution of sandstones with different initial saturation after wet and dry cycles. (<b>a</b>) Initial saturation of 25%; (<b>b</b>) Initial saturation of 50%; (<b>c</b>) Initial saturation of 75%; (<b>d</b>) Initial saturation of 100%.</p>
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<p>The process of macroporosity formation.</p>
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<p>Plot of fractal dimension versus porosity fit. (<b>a</b>) Fitting conditions of <span class="html-italic">D</span><sub>Total</sub> and <span class="html-italic">Φ</span><sub>Total</sub> for rock samples with initial saturations of 25% and 50%; (<b>b</b>) Fitting conditions of <span class="html-italic">D</span><sub>Total</sub> and <span class="html-italic">Φ</span><sub>Total</sub> for rock samples with initial saturations of 75% and 100%; (<b>c</b>) Fitting conditions of <span class="html-italic">D</span><sub>ma</sub> and <span class="html-italic">Φ</span><sub>ma</sub> for rock samples with initial saturations of 25% and 50%; (<b>d</b>) Fitting conditions of <span class="html-italic">D</span><sub>ma</sub> and <span class="html-italic">Φ</span><sub>ma</sub> for rock samples with initial saturations of 75% and 100%; (<b>e</b>) Fitting conditions of <span class="html-italic">D</span><sub>me</sub> and <span class="html-italic">Φ</span><sub>me</sub> for rock samples with initial saturations of 25% and 50%; (<b>f</b>) Fitting conditions of <span class="html-italic">D</span><sub>me</sub> and <span class="html-italic">Φ</span><sub>me</sub> for rock samples with initial saturations of 75% and 100%.</p>
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<p>Type I (<b>a</b>) and II (<b>b</b>) fracture toughness of sandstones with different initial saturations.</p>
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<p>Comparison of Fitting Relationships between Porosity and Type II Fracture Toughness. (<b>a</b>) Fitting results between <span class="html-italic">Φ</span><sub>ma</sub> and Type II fracture toughness; (<b>b</b>) Fitting results between <span class="html-italic">Φ</span><sub>Total</sub> and Type II fracture toughness.</p>
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<p>Comparison of the Fitting Relationships between the Fractal Dimension of Sandstone and Type II Fracture Toughness. (<b>a</b>) Fitting results between <span class="html-italic">D</span><sub>ma</sub> and Type II fracture toughness; (<b>b</b>) Fitting results between <span class="html-italic">D</span><sub>Tota</sub> and Type II fracture toughness.</p>
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<p>Pore structure damage mechanism of red sandstone.</p>
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29 pages, 19276 KiB  
Article
Geochemistry of REE and Other Critical Elements in Deep-Sea Polymetallic Nodules from Interoceanmetal (IOM) Exploration Area in Eastern Part of Clarion–Clipperton Fracture Zone, NE Pacific
by Atanas Hikov, Zlatka Milakovska, Irena Peytcheva, Valcana Stoyanova, Elitsa Stefanova, Tomasz Abramowski, Milen Kadiyski, Silvia Chavdarova, Milen Stavrev and Dimitrina Dimitrova
Minerals 2025, 15(2), 154; https://doi.org/10.3390/min15020154 - 6 Feb 2025
Viewed by 569
Abstract
Deep-sea Fe-Mn polymetallic nodules formed nowadays at the deep-sea ocean floor were evaluated as promising critical raw materials (CRMs). Here, we report results of polymetallic nodules from the H22_NE block of the Interoceanmetal (IOM) exploration area in the eastern part of the Clarion–Clipperton [...] Read more.
Deep-sea Fe-Mn polymetallic nodules formed nowadays at the deep-sea ocean floor were evaluated as promising critical raw materials (CRMs). Here, we report results of polymetallic nodules from the H22_NE block of the Interoceanmetal (IOM) exploration area in the eastern part of the Clarion–Clipperton Zone (CCZ), NE Pacific Ocean. The polymetallic nodules were studied with X-ray Diffraction, Raman spectroscopy, SEM-EDS, and LA-ICP-MS (bulk nodules and in situ nodule layers). Additionally, we combine geochemical data of polymetallic nodules with the previously reported data of pore waters and sediments from six stations. Our study aims to define the mineral composition and determine the content of CRMs in the polymetallic nodules and to assess the main factors controlling metal deposition and nodule enrichment in some CRMs. Mn content and the Mn/Fe ratio of the nodules classify them mostly as mixed hydrogenetic–diagenetic type. They are also enriched in Ni, Cu, Co, Zn, Mo, W, Li, Tl, and REE. The in situ REE patterns exhibit MREE and HREE enrichment and a variable Ce anomaly that argues for a changing oxic/suboxic environment and periodically changing of diagenetic and hydrogenetic nodule growth. The results of the joint study of the bottom sediments, pore waters, and polymetallic nodules show a complexity of processes that influence the formation of these deposits. The changing oxic and anoxic conditions are well documented in the chemistry of the nodule layers. Probably the most important controlling factors are sedimentation rate, bioturbation, adsorption, desorption, and oxidation. In addition, growth rates, water depth variations, electro-chemical speciation, phosphatization, and the structures of the Fe-Mn adsorbents are also considered. The polymetallic nodule deposits in the IOM contract area are estimated for future mining for Ni, Cu, Co, and Mn resources. They, however, contain additional metals of economic importance, such as REE and other trace elements (referred to as CRMs) that are potential by-products for metal mining. They can significantly increase the economic importance of exploited polymetallic nodules. Full article
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<p>The location of the sampling area in the IOM exploration site (yellow) in the eastern part of the Clarion–Clipperton fracture zone (CCZ) (red rectangle), NE Pacific (left; adapted from [<a href="#B21-minerals-15-00154" class="html-bibr">21</a>]), and a geomorphological map of the seafloor of the IOM H22_NE exploitable block (right (blue box); adapted from [<a href="#B14-minerals-15-00154" class="html-bibr">14</a>,<a href="#B17-minerals-15-00154" class="html-bibr">17</a>]). The black dots with numbers indicate the tested sampling stations. The white boxes are Ares of Particular Environmental Interest (APEIs).</p>
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<p>Examples of polymetallic nodules: (<b>a</b>) 3603; (<b>b</b>) 3610-1; (<b>c</b>) 3604; (<b>d</b>) 3611; (<b>e</b>) 3607; (<b>f</b>) 3623-p; (<b>g</b>) 3630; (<b>h</b>) 3615. Numbers are sample stations.</p>
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<p>Examples of polished sections of polymetallic nodules: (<b>a</b>) 3600; (<b>b</b>) 3607; (<b>c</b>) 3609-1; (<b>d</b>) 3612; (<b>e</b>) 3611; (<b>f</b>) 3630; (<b>g</b>) 3621; (<b>h</b>) 3623-p. Numbers are sample stations.</p>
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<p>XRD spectra of studied nodules (T—todorokite; M—muscovite; Q—quartz; V—vernadite).</p>
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<p>Raman spectra of todorokite from CCZ compared to todorokite from South Africa [<a href="#B28-minerals-15-00154" class="html-bibr">28</a>].</p>
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<p>(<b>a</b>) Chondrite-normalized (after [<a href="#B30-minerals-15-00154" class="html-bibr">30</a>]) and (<b>b</b>) PAAS-normalized (after [<a href="#B31-minerals-15-00154" class="html-bibr">31</a>]) REE patterns of bulk nodule samples. Numbers are sample stations.</p>
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<p>SEM-BSE images representing fine colloform textures of polymetallic nodules: (<b>a</b>) 3600; (<b>b</b>) 3607; (<b>c</b>) 3609; (<b>d</b>) and (<b>e</b>) 3515; (<b>f</b>) 3612. Numbers are sample stations.</p>
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<p>SEM-BSE images representing detrital and authigenic mineral phases of polymetallic nodules: (<b>a</b>) 3600; (<b>b</b>) 3607; (<b>c</b>) 3612; (<b>d</b>) 3615 (Sed—sediment; Chl—clorite; Mica—Na-K mica; Ap—apatite; Bar—barite); (<b>e</b>) fine barite crystals (white) deposited between colloform layers of Fe-Mn oxyhydroxides and dehydration cracks in grey bands (3611); (<b>f</b>) radiolarian skeleton partly replaced by Mn oxyhydroxide mineral phase (3621). Numbers are sample stations.</p>
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<p>BSE image and EDS maps for distribution of Si, Al, Mn, O, Fe, Ca, Na, Cl, Ni, Cu, and Zn of part of sample 3611.</p>
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<p>(<b>a</b>) Chondrite-normalized (after [<a href="#B30-minerals-15-00154" class="html-bibr">30</a>]) and (<b>b</b>) PAAS-normalized (after [<a href="#B31-minerals-15-00154" class="html-bibr">31</a>]) REE patterns of in situ analyses of nodule 3600.</p>
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<p>PAAS-normalized (after [<a href="#B31-minerals-15-00154" class="html-bibr">31</a>]) REE patterns of in situ analyses of nodules: (<b>a</b>) 3603; (<b>b</b>) 3607; (<b>c</b>) 3610-1; (<b>d</b>) 3611; (<b>e</b>) 3628-p; (<b>f</b>) 3630. Numbers are sample stations.</p>
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<p>Comparison of bulk nodules and nodule layers on ternary Fe–Mn–(Co + Ni + Cu) × 10 diagram [<a href="#B34-minerals-15-00154" class="html-bibr">34</a>] (<b>a</b>) and (Cu + Ni) × 15 − (Mn + Fe)/4 − (Zr + Ce + Y) × 100 diagram [<a href="#B35-minerals-15-00154" class="html-bibr">35</a>] (<b>b</b>) for discrimination of polymetallic nodules and sediments.</p>
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<p>Ce/Ce*–Nd (<b>a</b>) and Ce/Ce*–Y/Ho (<b>b</b>) diagrams (after [<a href="#B36-minerals-15-00154" class="html-bibr">36</a>]) for discrimination of polymetallic nodules.</p>
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<p>Comparison of sediments, bulk nodules, and nodule layers on ternary Fe–Mn–(Co + Ni + Cu) × 10 diagram [<a href="#B34-minerals-15-00154" class="html-bibr">34</a>] for discrimination of polymetallic nodules and sediments. Data of sediments are from [<a href="#B14-minerals-15-00154" class="html-bibr">14</a>].</p>
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<p>PAAS-normalized (after [<a href="#B32-minerals-15-00154" class="html-bibr">32</a>]) REE patterns of pore water layers (pw 0–5, pw 10–15, pw 25–30) [<a href="#B15-minerals-15-00154" class="html-bibr">15</a>], sediment layers (sed 5–10, sed 10–20) [<a href="#B14-minerals-15-00154" class="html-bibr">14</a>], bulk nodules (bulk nod), and richest nodule layers (max layer) of station 3600 compared to average deep oceanic water (seawater) [<a href="#B49-minerals-15-00154" class="html-bibr">49</a>].</p>
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18 pages, 8693 KiB  
Article
Synthesis of Amorphous MnFe@SBA Composites for Efficient Adsorptive Removal of Pb(Ⅱ) and Sb(V) from Aqueous Solution
by Zhou Shi, Aogui Zhu, Fan Chen, Yishu Cai and Lin Deng
Molecules 2025, 30(3), 679; https://doi.org/10.3390/molecules30030679 - 4 Feb 2025
Viewed by 586
Abstract
The extensive release of water contaminated with lead (Pb(II)) and antimony (Sb(V)) constitutes a serious threat to the human living environment and public health, necessitating immediate attention. In this study, a novel MnFe@SBA composite was synthesized using the hydrothermal method through the in [...] Read more.
The extensive release of water contaminated with lead (Pb(II)) and antimony (Sb(V)) constitutes a serious threat to the human living environment and public health, necessitating immediate attention. In this study, a novel MnFe@SBA composite was synthesized using the hydrothermal method through the in situ growth of MnFe2O4 on SBA-15. The MnFe@SBA exhibits an amorphous structure with a high specific surface area of 405.9 m2/g and pore sizes ranging from 2 to 10 nm. Adsorption experiments demonstrated that MnFe@SBA removed over 99% of Pb(II) and 80% of Sb(V) within 120 min at initial concentrations of 10 mg/L, whereas both MnFe2O4 and SBA-15 exhibited poor adsorption capacities. Additionally, the MnFe@SBA displayed excellent tolerance towards coexisting cations, including Na+, K+, Mg2+, Ca2+, Zn2+, Ni2+, and Cd2+, as well as anions such as Cl, NO3, CO32−, and PO43−. The adsorption behavior of Pb(II) onto MnFe@SBA was satisfactorily described by the pseudo-second-order kinetic model and the Freundlich isotherm, while the adsorption of Sb(V) was well-fitted by the pseudo-second-order kinetic model and the Langmuir isotherm. At 318 K, the maximum adsorption capacities of MnFe@SBA for Pb(II) and Sb(V) were determined to be 329.86 mg/g and 260.40 mg/g, respectively. Mechanistic studies indicated that the adsorption of Pb(II) and Sb(V) onto MnFe@SBA involved two primary steps: electrostatic attraction and complexation. In conclusion, the MnFe@SBA is anticipated to serve as an ideal candidate for efficient removal of Pb(II) and Sb(V) from contaminated water. Full article
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<p>XRD patterns of SBA-15, MnFe<sub>2</sub>O<sub>4</sub>, and MnFe@SBA.</p>
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<p>SEM images of (<b>a</b>) SBA-15, (<b>b</b>) MnFe<sub>2</sub>O<sub>4</sub>, and (<b>c</b>) MnFe@SBA; (<b>d</b>) EDX elemental mapping images and (<b>e</b>) corresponding map sum spectrum of MnFe@SBA.</p>
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<p>Nitrogen adsorption–desorption isotherms and pore size distributions of (<b>a</b>) SBA-15, (<b>b</b>) MnFe<sub>2</sub>O<sub>4</sub>, and (<b>c</b>) MnFe@SBA; (<b>d</b>) FT-IR spectrum of SBA-15, MnFe<sub>2</sub>O<sub>4</sub>, and MnFe@SBA.</p>
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<p>High-resolution XPS spectra: (<b>a</b>) O 1s; (<b>b</b>) Fe 2p; (<b>c</b>) Mn 2p for MnFe<sub>2</sub>O<sub>4</sub> and MnFe@SBA.</p>
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<p>Effect of SBA-15 doping amount on the removal of (<b>a</b>) Pb(Ⅱ) and (<b>b</b>) Sb(Ⅴ).</p>
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<p>Effect of initial solution pH on removal of (<b>a</b>) Pb(Ⅱ) and (<b>b</b>) Sb(Ⅴ) by MnFe@SBA. (<b>c</b>) Zeta potential of MnFe@SBA at pH ranging from 3 to 11.</p>
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<p>Adsorption kinetics of Pb(II) onto MnFe@SBA fitted by (<b>a</b>) pseudo-first-order model and (<b>b</b>) pseudo-second-order model; adsorption kinetics of Sb(Ⅴ) onto MnFe@SBA fitted by (<b>c</b>) pseudo-first-order model and (<b>d</b>) pseudo-second-order model.</p>
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<p>Adsorption kinetics of Pb(II) onto MnFe@SBA fitted by (<b>a</b>) pseudo-first-order model and (<b>b</b>) pseudo-second-order model; adsorption kinetics of Sb(Ⅴ) onto MnFe@SBA fitted by (<b>c</b>) pseudo-first-order model and (<b>d</b>) pseudo-second-order model.</p>
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<p>Adsorption isotherms of Pb(II) and Sb(Ⅴ) on MnFe@SBA at temperatures of 298, 308, and 318 K: (<b>a</b>,<b>c</b>) Langmuir; (<b>b</b>,<b>d</b>) Freundlich.</p>
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<p>(<b>a</b>) XPS survey spectra for the fresh MnFe@SBA, Pb(II) adsorbed MnFe@SBA (MnFe@SBA-Pb), and Sb(V) adsorbed MnFe@SBA (MnFe@SBA-Sb). High-resolution XPS spectra for the fresh MnFe@SBA, MnFe@SBA-Pb, and MnFe@SBA-Sb: (<b>b</b>) O 1s, (<b>c</b>) Mn 2p and (<b>d</b>) Fe 2p. (<b>e</b>) High-resolution XPS Pb 4f spectra for MnFe@SBA-Pb.</p>
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<p>(<b>a</b>) XPS survey spectra for the fresh MnFe@SBA, Pb(II) adsorbed MnFe@SBA (MnFe@SBA-Pb), and Sb(V) adsorbed MnFe@SBA (MnFe@SBA-Sb). High-resolution XPS spectra for the fresh MnFe@SBA, MnFe@SBA-Pb, and MnFe@SBA-Sb: (<b>b</b>) O 1s, (<b>c</b>) Mn 2p and (<b>d</b>) Fe 2p. (<b>e</b>) High-resolution XPS Pb 4f spectra for MnFe@SBA-Pb.</p>
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<p>Diagram of the potential mechanisms of Sb(V) and Pb(II) adsorption by MnFe@SBA.</p>
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