Soil contamination with heavy metals presents a substantial environmental peril, necessitating the exploration of innovative remediation approaches. This research aimed to investigate the efficiency of nano-silica in stabilizing heavy metals in a calcareous heavy metal-contaminated soil. The soil was treated with five nano-silica levels of 0, 100, 200, 500, and 1000 mg/kg and incubated for two months. The results showed that nano-silica had a specific surface area of 179.68  m 2 / g . At 1000 mg/kg, the DTPA-extractable concentrations of Pb, Zn, Cu, Ni, and Cr decreased by 12%, 11%, 11.6%, 10%, and 9.5% compared to the controls, respectively. Additionally, as the nano-silica application rate increased, both soil pH and specific surface area increased. The augmentation of nano-silica adsorbent in the soil led to a decline in the exchangeable (EX) and carbonate-bound fractions of Pb, Cu, Zn, Ni, and Cr, while the distribution of heavy metals in fractions bonded with Fe-Mn oxides, organic matter, and residue increased. The use of 1000 mg/kg nano-silica resulted in an 8.0% reduction in EX Pb, 4.5% in EX Cu, 7.3% in EX Zn, 7.1% in EX Ni, and 7.9% in EX Cr compared to the control treatment. Overall, our study highlights the potential of nano silica as a promising remediation strategy for addressing heavy metal pollution in contaminated soils, offering sustainable solutions for environmental restoration and ecosystem protection.

This paper introduces a method for determining the specific surface area (SSA) of biomass activated carbon (BAC) using a tracer-based headspace gas chromatography (HS-GC) technique. The method relies on the adsorption equilibrium of methanol on BAC samples at elevated temperature. A mathematical model allows for the calculation of SSA from the methanol signal obtained during the headspace analysis. The results demonstrate high precision (relative standard deviation < 2.44%) and strong accuracy (correlation with the conventional BET-N2 adsorption method, R² = 0.986). This method offers several advantages over traditional techniques, including ease of operation, significant time efficiency, and the the ability to perform batch determinations of SSA, as multiple samples can be processed simultaneously during the phase equilibrium step.

Biomass-derived carbon for supercapacitors faces the challenge of achieving hierarchical porous carbon with graphitic structure and specific heteroatoms through a single-stage thermal process that minimises resource input. Herein, molten base carbonisation and activation is proposed. The process utilises the inherent moisture of Moso bamboo shoots, coupled with a low amount of KOH, to form potassium organic salts before drying. The resultant potassium salts promote in-situ activation during single-stage heating process, yielding hierarchical porous, large specific surface area, and partially graphitised carbon with heteroatoms (N, O). As an electrode material, this carbon exhibits a specific capacitance of 327F g-1 in 6 M KOH and 182F g-1 in 1 M TEABF4/AN, demonstrating excellent cycling stability over 10,000 cycles at 2 A/g. Overall, this study presents a straightforward process that avoids pre-drying of biomass, minimises base consumption, and employs single-stage heating to fabricate electrode carbon suitable for supercapacitors.

The physicochemical properties of natural bentonite and its sorbents were studied. It has been established the modification of natural bentonites using polyhydroxoxides of iron (III) (mod.1_Fe_5-c) and aluminum (III) (mod.1_Al_5-c) by the \"co-precipitation\" method led to changes in their chemical composition, structure, and sorption properties. It was shown that modified sorbents based on natural bentonite are finely porous (nanostructured) objects with a predominance of pores of 1.5-8.0 nm in size. The modification of bentonite with iron (III) and aluminum compounds by the \"co-precipitation\" method also leads to an increase in the sorption capacity of the obtained sorbents with respect to bichromate and arsenate anions. A kinetic analysis showed that, at the initial stage, the sorption process was controlled by an external diffusion factor, that is, the diffusion of the sorbent from the solution to the liquid film on the surface of the sorbent. The sorption process then began to proceed in a mixed diffusion mode when it limited both the external diffusion factor and the intra-diffusion factor (diffusion of the sorbent to the active centers through the system of pores and capillaries). To clarify the contribution of the chemical stage to the rate of adsorption of bichromate and arsenate anions by the sorbents under study, kinetic curves were processed using equations of chemical kinetics (pseudo-first-order, pseudo-second-order, and Elovich models). It was found that the adsorption of the studied anions by the modified sorbents based on natural bentonite was best described by a pseudo-second-order kinetic model. The high value of the correlation coefficient for the Elovich model (R2 > 0.9) allows us to conclude that there are structural disorders in the porous system of the studied sorbents, and their surfaces can be considered heterogeneous. Considering that heterogeneous processes occur on the surface of the sorbent, it is natural that all surface properties (structure, chemical composition of the surface layer, etc.) play an important role in anion adsorption.

This study aimed to experimentally investigate the compressive strength and air voids of cement pastes with varying dosages of Superabsorbent Polymer (SAP) and water-to-cement (w/c) ratios. Cement pastes were prepared using three different w/c ratios of 0.4, 0.5, and 0.6, along with different dosages of SAP ranging from 0.2% to 0.5% by weight of cement. Additionally, SAP was introduced in two forms: dry and wet. After casting the cubes, two distinct curing conditions were employed: curing at a temperature of 20 °C with a Relative Humidity (RH) of 60% (Curing 1), and water curing (Curing 2). The results revealed that the addition of SAP increased early strength when subjected to Curing 1, followed by a decrease in later strength. On the other hand, samples with SAP and water curing exhibited higher strength compared to those without SAP, especially with w/c ratios of 0.4 and 0.5. However, at a w/c ratio of 0.6, nearly all samples showed a reduction in strength compared to those without SAP. Furthermore, air void analysis was performed on all samples cured for 28 days using an image analysis technique. The samples containing wet SAP resulted in a higher total air content compared to the samples with dry SAP. Additionally, the incorporation of wet SAP in cement paste led to lower specific surface areas and a higher spacing factor than the samples with dry SAP. These findings suggest that the clumping of wet SAP particles during presoaking resulted in coarser air voids compared to the samples containing dry SAP.

As a common method for preparing micron powder in industrial operations, the mechanical extrusion method simply pursues the particle size without considering the microstructure characteristics of sepiolite, which leads to problems such as bundles of sepiolite not being effectively dispersed, and thus the disruption of fibers is inevitably caused. In this work, a new micronization method for disaggregating these bundles while preserving the original structural integrity of the fibers is proposed based on steam pressure changes. The effects of steam pressure changes on the particle size distribution, microstructure, and properties of treated sepiolite are studied using X-ray fluorescence spectrometer (XRF), X-ray diffractometer (XRD), Field Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscopy (TEM), and a specific surface area and aperture analyzer (BET). The experimental results show that the particle size of sepiolite powder depends greatly on steam pressure, and sepiolite powder with mass ratio of 91.6% and a particle size D97 of 21.27 μm is obtained at a steam pressure of 0.6 MPa. Compared to the sepiolite after mechanical extrusion, the sepiolite treated with steam pressure changes can maintain the integrity of its crystalline structure. The specific surface area of sepiolite enhanced from 80.15 m2 g-1 to 141.63 m2 g-1 as the steam pressure increased from 0.1 to 0.6 MPa, which is about 1.6 times that of the sample treated with mechanical extrusion.

X-ray microtomography is a non-destructive method that allows for detailed three-dimensional visualisation of the internal microstructure of materials. In the context of using phosphorus-rich residual streams in combustion for further ash recycling, physical properties of ash particles can play a crucial role in ensuring effective nutrient return and sustainable practices. In previous work, parameters such as surface area, porosity, and pore size distribution, were determined for ash particles. However, the image analysis involved binary segmentation followed by time-consuming manual corrections. The current work presents a method to implement deep learning segmentation and an approach for quantitative analysis of morphology, porosity, and internal microstructure. Deep learning segmentation was applied to microtomography data. The model, with U-Net architecture, was trained using manual input and algorithm prediction.•The trained and validated deep learning model could accurately segment material (ash) and air (pores and background) for these heterogeneous particles.•Quantitative analysis was performed for the segmented data on porosity, open pore volume, pore size distribution, sphericity, particle wall thickness and specific surface area.•Material features with similar intensities but different patterns, intensity variations in the background and artefacts could not be separated by manual segmentation - this challenge was resolved using the deep learning approach.

Acid-modified biochar is a modified biochar material with convenient preparation, high specific surface area, and rich pore structure. It has great potential for application in the heavy metal remediation, soil amendments, and carrying catalysts. Specific surface area (SSA), average pore size (APS), and total pore volume (TPV) are the key properties that determine its adsorption capacity, reactivity, and water holding capacity, and an intensive study of these properties is essential to optimize the performance of biochar. But the complex interactions among the preparation conditions obstruct finding the optimal modification strategy. This study collected dataset through bibliometric analysis and used four typical machine learning models to predict the SSA, APS, and TPV of acid-modified biochar. The results showed that the extreme gradient boosting (XGB) was optimal for the test results (SSA R2 = 0.92, APS R2 = 0.87, TPV R2 = 0.96). The model interpretation revealed that the modification conditions were the major factors affecting SSA and TPV, and the pyrolysis conditions were the major factors affecting APS. Based on the XGB model, the modification conditions of biochar were optimized, which revealed the ideal preparation conditions for producing the optimal biochar (SSA = 727.02 m2/g, APS = 5.34 nm, TPV = 0.68 cm3/g). Moreover, the biochar produced under specific conditions verified the generalization ability of the XGB model (R2 = 0.99, RMSE = 12.355). This study provides guidance for optimizing the preparation strategy of acid-modified biochar and promotes its potentiality for industrial application.

This paper discusses efforts made by past researchers to steady the expansive (problematic) soils using mechanical and chemical techniques - specifically with EPS beads, lime and fly ash. Administering swelling of problematic soils is critical for civil engineers to prevent structural distress. This paper summarizes studies on reduction of swelling potential using EPS, lime and fly ash individually. Chemical stabilization with lime and fly ash are conventional methods for expansive soil stabilization, with known merits and demerits. This paper explores the suitability of different materials under various conditions and stabilization mechanisms, including cation exchange, flocculation, and pozzolanic reactions. The degree of stabilization is influenced by various factors such as the type and amount of additives, soil mineralogy, curing temperature, moisture content during molding, and the presence of nano-silica, organic matter, and sulfates. Additionally, expanded polystyrene (EPS) improves structural integrity by compressing when surrounded clay swells, reducing overall swelling. Thus, EPS addresses limitations of chemicals by mechanical means. Combining EPS, lime and fly ash creates a customized system promoting efficient, long-lasting, cost-effective and eco-friendly soil stabilization. Chemicals address EPS limitations like poor stabilization. This paper benefits civil engineers seeking to control expansive soil swelling and prevent structural distress. It indicates potential of an EPS-lime-fly ash system and concludes by identifying research gaps for further work on such combinatorial stabilizer systems.

The rapid discovery of photocatalysts with desired performance among tens of thousands of potential perovskites represents a significant advancement. To expedite the design of perovskite-oxide-based photocatalysts, we developed a model of ABO3-type perovskites using machine learning methods based on atomic and experimental parameters. This model can be used to predict specific surface area (SSA), a key parameter closely associated with photocatalytic activity. The model construction involved several steps, including data collection, feature selection, model construction, web-service development, virtual screening and mechanism elucidation. Statistical analysis revealed that the support vector regression model achieved a correlation coefficient of 0.9462 for the training set and 0.8786 for the leave-one-out cross-validation. The potential perovskites with higher SSA than the highest SSA observed in the existing dataset were identified using the model and our computation platform. We also developed a webserver of the model, freely accessible to users. The methodologies outlined in this study not only facilitate the discovery of new perovskites but also enable exploration of the correlations between the perovskite properties and the physicochemical features. These findings provide valuable insights for further research and applications of perovskites using machine learning techniques.