Arsenic (As) contamination of groundwater is a serious global issue requiring effective and sustainable remediation strategies. For long-term As immobilization, this study explores the potential of in-situ magnetite precipitation, induced by anaerobic nitrate-reducing Fe(II) -oxidizing (NRFO) bacteria. A nitrate-intercalated layered double hydroxide (NO₃-MgFe LDH) was introduced to provide nitrate as an electron acceptor for Fe(II) bio-oxidation and serve as an iron-based precursor in magnetite formation. The experimental results showed that NO₃-MgFe LDH was transformed into green rust (GR) in the presence of Fe(II) and HCO₃⁻. Meanwhile, 0.5 g/L of NO₃-MgFe LDH released cumulatively about 1.21 mM of nitrate within 12 h, promoting the transformation of GR into magnetite induced by Acidovorax sp. BoFeN1. As a result, the aqueous As concentration decreased from 2 mg/L to <0.008 mg/L, with approximately 70% of As confined in recalcitrant Fe oxides, suggesting high potential for long-term As immobilization. Environmental factors influenced the transformation process: a lower Fe(II) concentration (0.5 mM) delayed GR formation, while varying HCO₃⁻ concentrations (2.5–10 mM) had minimal effect. Subsequently, an elevated As level (5 mg/L) inhibited the bio-formation of magnetite, leading to lepidocrocite as the dominant mineral phase. Given the stability of magnetite, this study provides a cost-effective and environmentally friendly strategy for the durable in-situ remediation of As-contaminated groundwater.

The nanofluid-based direct absorption solar collector (NDASC) ensures that solar radiation passing through the tube wall is directly absorbed by the nanofluid, reducing thermal resistance in the energy transfer process. However, further exploration is required to suppress the outward thermal losses from the nanofluid at high temperatures. Herein, this paper proposes a novel NDASC in which the outer surface of the collector tube is covered with functional coatings and a three-dimensional computational fluid dynamics model is established to study the energy flow distributions on the collector within the temperature range of 400–600 K. When the nanofluid’s absorption coefficient reaches 80 m⁻¹, the NDASC shows the optimal thermal performance, and the NDASC with local Sn-In₂O₃ coating achieves a 7.8% improvement in thermal efficiency at 400 K compared to the original NDASC. Furthermore, hybrid coatings with SnIn₂O₃/WTi-Al₂O₃ are explored, and the optimal coverage angles are determined. The NDASC with such coatings shows a 10.22%–17.9% increase in thermal efficiency compared to the original NDASC and a 7.6%–19.5% increase compared to the traditional surface-type solar collectors, demonstrating the effectiveness of the proposed energy flow control strategy for DASCs.

This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels, accounting for geological uncertainties. First, the shield tunnel is modeled as an Euler-Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length. A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation. Next, a risk assessment index system is established. The normalized relative depth between the excavation and the shield tunnel is used as a risk index, while the maximum longitudinal deformation, the maximum circumferential opening, and the maximum longitudinal bending moment serve as performance indicators. Based on these, a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed. Finally, the technique is applied to a case study involving a foundation pit excavation above a shield tunnel, which is the primary application scenario of this method. Vulnerability curves for different performance indicators are derived, and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored. The results reveal significant differences in vulnerability curves depending on the performance index used. Compared to the maximum circumferential opening and the maximum longitudinal bending moment, selecting maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances. The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness, and the subgrade stiffness has a more pronounced effect. Parametric analyses suggest that actively reinforcing the substratum is more effective at reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.

In the practical slope engineering, the stability of lower sliding body (region A) with back tensile cracks of the jointed rock slope is received more attentions, but the upper rock mass (region B) may also be unstable. Therefore, in this study, based on the stepped failure mode of bedding jointed rock slopes, considering the influence of the upper rock mass on the lower stepped sliding body, the improved failure model for analyzing the interaction force (F_AB) between two regions is constructed, and the safety factors (F_S) of two regions and whole region (A and B) are derived. In addition, this paper proposes a method to determine the existence of F_AB using their respective acceleration values (a_A and a_B) when regions A and B are unstable. The influences of key parameters on two regions and the whole region are analyzed. The results show that the variation of the F_AB and F_S of two regions can be obtained accurately based on the improved failure model. The accuracy of the improved failure model is verified by comparative analysis. The research results can explain the interaction mechanism of two regions and the natural phenomenon of slope failure caused by the development of cracks.

The surrounding rock of the soft rock roadway is seriously deformed and damaged under the superposition of mining stress and fault tectonic stress. In this paper, taking the No. 232206 intake roadway in Meihuajing coal mine as the engineering background, the deformation and failure law of the surrounding rock of the roadway in different fault protection pillar widths are obtained by numerical simulation method. On this basis, the mechanical model of the roadway under the action of hanging wall overburden migration and fault slip in normal faults is established, and the energy-driven mechanism of large deformation of the surrounding rock of the roadway was revealed. The ratio T of the energy applying on anchoring surrounding rock to the resistant energy of anchoring surrounding rock as the criterion for the deformation of the roadway. Finally, it was calculated according to the actual working conditions on site, and the control method of “stress relief-support reinforcement” was used to support the roadway with the risk of large deformation. The on-site monitoring results show that the control effect of the surrounding rock of the roadway is obvious.

High-purity silver (Ag) is extensively utilized in electronics, aerospace, and other advanced industries due to its excellent thermal conductivity, electrical conductivity, and machinability. However, the prohibitive material cost poses substantial challenges for optimizing thermal processing parameters through repetitive experimental trials. In this work, hot compression experiments on high-purity silver were conducted using a Gleeble-3800 thermal simulator. The high-temperature deformation behaviors, dynamic recovery (DRV) and dynamic recrystallization (DRX) of high-purity silver were studied by constructing an Arrhenius constitutive equation and developing thermal processing maps. The results show that plastic instability of high-purity silver occurs at high strain rates and the optimized hot processing parameters are the strain rate below 0.001 s⁻¹ and the temperature of 340–400 °C. Microstructural observations exhibit that DRV prefers to occur at lower deformation temperatures (e.g. 250 °C ). This is attributed to the low stacking fault energy of high-purity silver, which facilitates the decomposition of dislocations into partial dislocations and promotes high-density dislocation accumulation. Furthermore, DRX in high-purity silver becomes increasingly pronounced with increasing deformation temperature and reaches saturation at 350 °C.

Rocks will suffer different degree of damage under FT (freeze-thaw) cycles, which seriously threatens the long-term stability of rock engineering in cold regions. In order to study the mechanism of rock FT damage, energy calculation method and energy self-inhibition model are introduced to explore their energy characteristics in this paper. The applicability of the energy self-inhibition model was verified by combining the data of FT cycles and uniaxial compression tests of intact and pre-cracked sandstone samples, as well as published reference data. In addition, the energy evolution characteristics of FT damaged rocks were discussed accordingly. The results indicate that the energy self-inhibition model perfectly characterizes the energy accumulation characteristics of FT damaged rocks under uniaxial compression before the peak strength and the energy dissipation characteristics before microcrack unstable growth stage. Taking the FT damaged cyan sandstone sample as an example, it has gone through two stages dominated by energy dissipation mechanism and energy accumulation mechanism, and the energy rate curve of the pre-cracked sample shows a fall-rise phenomenon when approaching failure. Based on published reference data, it was found that the peak total input energy and energy storage limit conform to an exponential FT decay model, with corresponding decay constants ranging from 0.0021 to 0.1370 and 0.0018 to 0.1945, respectively. Finally, a linear energy storage equation for FT damaged rocks was proposed, and its high reliability and applicability were verified by combining published reference data,the energy storage coefficient of different types of rocks ranged from 0.823 to 0.992, showing a negative exponential relationship with the initial UCS (uniaxial compressive strength). In summary, the mechanism by which FT weakens the mechanical properties of rocks has been revealed from an energy perspective in this paper, which can provide reference for related issues in cold regions.

The development of superhydrophobic materials have demonstrated significant potential in the realm of corrosion protection for aluminum alloy (Al alloy) surfaces. However, the limited mechanical stability of superhydrophobic surfaces has impeded the rapid advancement in this field. In this research, we synthesized an aluminum phosphate (AP) inorganic binder and combined it with hydrophobic fumed SiO₂ (HF−SiO₂) nanoparticles and polydimethylsiloxane (PDMS) to develop a HF-SiO₂@PDMS@AP superhydrophobic composite coating with improved mechanical stability on Al alloy substrates using a simple spray-coating technique. The findings indicate that the addition of the AP inorganic binder significantly enhanced the coating’s resistance to abrasion, maintaining its superhydrophobic properties and micro-nano hierarchical structure even after being subjected to a sandpaper abrasion distance of 2000 cm. Electrochemical impedance spectroscopy (EIS) testing showed that the low-frequency modulus (∣Z∣_0.01Hz) of the HF-SiO₂@PDMS@AP superhydrophobic coating increased by four orders of magnitude compared to the initial Al alloy substrate, resulting in a substantial improvement in corrosion protection capacity. The impressive corrosion resistance and mechanical stability exhibited by this coating have the potential to greatly expand the practical applications of such materials for surface functional protection in marine and industrial environments.

In the mining industry, precise forecasting of rock fragmentation is critical for optimising blasting processes. In this study, we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF. This model combines the Grey Wolf Optimization (GWO) algorithm with the Random Forest (RF) technique to predict the D₈₀ value, a critical parameter in evaluating rock fragmentation quality. The study is conducted using a dataset from Sarcheshmeh copper mine, employing six different swarm sizes for the GWO-RF hybrid model construction. The GWO-RF model’s hyperparameters are systematically optimized within established bounds, and its performance is rigorously evaluated using multiple evaluation metrics. The results show that the GWO-RF hybrid model has higher predictive skills, exceeding traditional models in terms of accuracy. Furthermore, the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations (SHAP) values. The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.

Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment. However, the considerable time and financial burdens of landslide inventories often result in persistent data scarcity, which frequently impedes the generation of accurate and informative landslide susceptibility maps. Addressing this challenge, this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically. Notably, the proposed model, calibrated with the warmup-cosine annealing (WCA) learning rate strategy, demonstrated remarkable predictive capabilities, particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region. This is evidenced by the area under the receiver operating characteristic curve (AUC) values, which exhibited significant improvements of 51.00%, 24.40% and 2.15%, respectively, compared to a deep learning model, in contexts where only 1%, 5% and 10% of data from the target region were used for retraining. Simultaneously, there were reductions in loss of 16.12%, 27.61% and 15.44%, respectively, in these instances.