New Carbon Materials•2026•DOI: 10.1016/S1872-5805(25)61012-2
We report a method for increasing the mechanical strength of carbon nanotube (CNT) fibers while enabling the uniform adhesion of cerium oxide (CeO2) abrasive particles to them using polyethyleneimine (PEI). Results show that 5% of PEI increases the tensile strength of CNT fibers by approximately 175%. CeO2 particles were uniformly deposited on the reinforced CNT fibers by electrophoretic deposition. A flexible polishing tool was fabricated by weaving the CeO2-CNT fibers into a non-woven fabric substrate. When used to polish potassium dihydrogen phosphate crystals, the tool reduced the surface roughness from 200 to 7.6 nm within 10 min. This approach has potential use for the development of new precision processing tools.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202604001
This study systematically investigated the occurrence, spatial distribution, sources, and ecological risks of 160 pesticides in Dianchi Lake, a typical plateau lake impacted by agricultural activities. A total of 37 pesticides were detected in the water, with total concentrations ranging from 64.2 to 1132.8 ng/L (average 610.0 ng/L). Fungicides, including boscalid (BOS), fluopicolide (FPC), and dimethomorph (DMM), were dominant, contributing up to 65.0% of the total concentration. Spatially, the southern lake region exhibited significantly higher concentrations (672.5 ng/L) than the north, attributed to intensive facility agriculture. Highly hydrophobic pesticides, such as penconazole (PEN), showed a tendency to enrich in bottom layers. Source apportionment identified inflowing rivers and wastewater treatment plant effluents as primary input sources, with average concentrations 7 and 9 times higher than lake water, respectively. Ecological risk assessment revealed that pesticides posed the highest risk to algae, followed by daphnia and fish. Prometryn (PMT) was identified as a high-risk factor for algae, while profenofos (PFF) and carbendazim (CBD) posed potential threats to higher trophic levels. These findings provide fundamental data and technical support for understanding pesticide pollution in plateau lake ecosystems.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202509086
Groundwater contamination by nitrate and antibiotics has become a global concern. This study evaluated the continuous performance of permeable reactive barrier (PRB) columns packed with zero-valent iron (ZVI) and pyrite (FeS2) combined with denitrifying microorganisms (ZFM) for simultaneous removal of nitrate and ofloxacin (OFL). Control columns included soil (S), microorganisms (M), and ZVI/FeS2 (ZF). Over 30 days of continuous operation, the ZFM column achieved average removal efficiencies of 88% for nitrate and 78% for OFL, significantly higher than controls. The ZFM system maintained higher active iron concentration (0.68 mg·L−1) compared to ZF (0.48 mg·L−1), mitigated pH increase, and sustained lower oxidation-reduction potential (ORP), favoring stable performance. XRD and XPS analyses revealed that microbial involvement promoted FeS formation (2θ=30.1°) and reduced ZVI passivation, extending material lifespan. High-throughput sequencing showed that while overall microbial diversity remained stable, key functional populations including norank_f_Fermentibacteraceae, norank_f_Anaerolineaceae, Longilinea, and Anaerolinea increased in abundance by 2.93%, 0.55%, 1.53%, and 0.62%, respectively, enhancing nitrate and OFL removal. These findings demonstrate that integrating microorganisms with ZVI/FeS2 in PRB systems offers a promising approach for remediating combined nitrate and antibiotic contamination in groundwater.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025021902
Polyacrylonitrile (PAN) ultrafiltration membranes are widely used in water treatment, yet their anti-fouling performance remains a challenge. In this work, PAN was first reacted with sodium azide via click chemistry to synthesize 1,2,3,4-tetrazolium polyacrylonitrile (PAN-N). Subsequently, PAN-N was reacted with iodoacetamide (IAM), 2-iodoethanol (IH), iodoacetic acid (IA), and chlorosulfonic acid (CSA) to introduce hydrophilic groups, and anti-fouling PAN ultrafiltration membranes were fabricated via phase inversion. The membranes were characterized by Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance, X-ray diffraction, scanning electron microscopy, and contact angle measurements. Results showed that the PAN-N membrane exhibited superior performance to pristine PAN, with water flux increasing from 0.9233 to 1.232 L·(m2·h·kPa)−1 and bovine serum albumin (BSA) rejection from 69.23% to 82.4%. Hydrophilic modification further enhanced performance; the PAN-N-IA membrane achieved the highest water flux of 1.7347 L·(m2·h·kPa)−1 and rejection of 93.57%. Anti-fouling tests revealed that modified membranes followed the order: PAN-N-CSA > PAN-N-IA > PAN-N-IH > PAN-N-IAM > PAN-N > PAN. PAN-N-CSA and PAN-N-IA showed comparable anti-fouling performance, with total fouling indices of 56.1% and 58.47%, reversible fouling indices of 47.17% and 46.97%, and irreversible fouling indices of 8.97% and 11.47%, respectively. This work demonstrates that PAN-N-IA membranes combine high flux, high rejection, and excellent anti-fouling properties, making them promising for water treatment applications.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202606005
In the context of carbon peaking and carbon neutrality, urban reclaimed water plants must adopt measures such as energy conservation, consumption reduction, and enhanced resource and energy utilization to achieve carbon neutrality. This study developed a carbon emission balance model and accounting method for such plants, incorporating strategies of carbon emission reduction, carbon substitution, and carbon sink. The optimal pathway towards carbon neutrality was evaluated based on the carbon emission balance ratio. Using a 1×10⁵ m³/d urban reclaimed water plant as a case study, the results showed total carbon emissions of 20,934 t CO2e. The carbon emission reduction from reclaimed water source heat pumps for heating and cooling was 21,701 t CO2e, yielding a carbon emission balance ratio of 103.7%. In contrast, other carbon reduction measures contributed 15,424 t CO2e, with a balance ratio of 73.7%, highlighting the pivotal role of reclaimed water source heat pumps. When the heat pump extracted 27% and 36% of residual thermal energy, coupled with reclaimed water reuse or sludge anaerobic digestion-cogeneration, respectively, both pathways achieved a 100% balance ratio. Assuming year-round extraction, the balance ratio reached 213%. The carbon reduction ratio between utilizing residual thermal energy and chemical energy was 8.76:1. This study demonstrates that urban reclaimed water plants can achieve carbon neutrality through multiple pathways, with residual thermal energy recovery exhibiting significant potential.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60642-1
Inherent minerals significantly influence the thermal conversion of coal, yet the interaction mechanisms among minerals affecting tar generation during pyrolysis remain unclear. This study investigates the effect of acidic mineral components on the behavior of ion-exchangeable Ca2+ during coal pyrolysis. Coal samples were prepared via HCl and HCl-HF acid washing followed by Ca2+ ion exchange. Pyrolysis was conducted in a fixed-bed reactor. Acid washing effectively reduced ash content but also decreased organic element contents (carbon, hydrogen). Loading ion-exchangeable calcium enhanced the thermal weight loss rate in the 500–550 °C range, shifting the peak temperature from 530 °C to 514 °C. At a final pyrolysis temperature of 600 °C with slow heating, kaolinite in acidic minerals underwent dehydroxylation to form metakaolin. The content of small aromatic rings (<6 rings) in char from Ca-loaded coal was lower than that from acid-washed coal without Ca. Coexistence of acidic minerals with ion-exchangeable Ca increased aliphatic hydrocarbon content in tar: YL-HCl-Ca reached 21.98% versus 13.60% for YL-De-Ca. Acidic mineral components inhibit the adverse effect of ion-exchangeable Ca2+ on tar lightening. These findings provide insights into mineral interactions during pyrolysis, aiding in optimizing coal conversion processes for improved tar quality.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607021
The continuous expansion of urban sewage treatment capacity has led to a sustained increase in sludge generation, making efficient treatment, disposal, and resource recovery critical in environmental engineering. Machine learning (ML) offers substantial potential for prediction and optimization in sludge treatment by extracting non-linear features from complex operational data. This review systematically examines the application of ML across typical sludge treatment processes, including dewatering, resource recovery (anaerobic digestion), and terminal disposal (incineration and landfill). The general modeling workflow is summarized across three dimensions: dataset preparation, algorithm selection, and model evaluation. A comparative analysis evaluates the applicability and limitations of support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and other deep learning models. SVMs demonstrate greater stability with small-to-medium sample sizes and high-dimensional data, while RFs exhibit strong generalization and provide variable importance insights. ANNs and deep learning models excel in large-scale data and time-series or image tasks but require high data quality. Key findings from the literature include ANN achieving R²=0.99 and RMSE=0.02 in dewatering prediction, and R²=0.86 with NRMSE=0.31 in anaerobic digestion, while gradient boosting reached R²=0.90 and RMSE=0.33. Future directions emphasize multi-source data fusion, model interpretability (e.g., SHAP), and coupling ML with mechanistic models to enhance predictive accuracy and generalization, supporting intelligent and refined sludge treatment management.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607022
Anaerobic sludge digestion is the core process for achieving energy recovery and sludge reduction in wastewater treatment plants. However, its complex biological reaction mechanisms and multivariable coupling characteristics pose persistent challenges to process optimization and stable control. Traditional mechanistic models, while theoretically clear, suffer from parameter calibration difficulties and insufficient adaptability under dynamic and nonlinear conditions. Machine learning (ML) has gained attention for its powerful data modeling capabilities. This review systematically examines ML applications in sludge anaerobic digestion, focusing on biogas production prediction, process monitoring and early warning, and process parameter optimization. For gas production, hybrid models and deep learning achieve high-precision methane yield predictions. Soft-sensing models using easy-to-measure parameters enable real-time estimation of volatile fatty acids and total ammonia nitrogen. At the optimization level, coupling surrogate models with optimization algorithms provides dynamic regulation strategies for co-digestion ratios and pretreatment conditions. Interpretable methods address the 'black-box' issue, enhancing engineering acceptability. Deep integration of these methods with dynamic optimization supports an intelligent decision-making framework. However, translation from laboratory to engineering faces constraints including data quality, model generalization, and implementation. This paper provides an analytical framework combining predictive capability with engineering reliability for sludge treatment optimization.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3960-0
Dry electrode processing offers a solvent-free and scalable pathway toward high-energy lithium metal batteries (LMBs), yet its practical implementation is constrained by tortuous ion/electron transport and weak mechanical cohesion in ultra-thick electrodes. Here, we construct a carbon-coated NASICON-type Li1.3Al0.3Ti1.7(PO4)3 nanofiber network (LATP@C) that serves as an integrated ionic-electronic scaffold within dry-processed Ni-rich cathodes. The one-dimensional LATP@C fibers form a continuous 3D percolation architecture that couples fast Li+ conduction from the NASICON core with efficient electron transport through the conformal carbon shell. Their rough, oxygen-functionalized surfaces further enhance electrolyte affinity, while the mechanically robust fibrous network bridges NCM811 secondary particles, suppressing crack initiation and preserving structural integrity during cycling. Benefiting from these collective effects, the LATP@C cathode with 100 mg cm−2 loading delivers 203 mA h g−1 at 0.1 C and maintains 96.7% capacity over 35 cycles at 0.2 C. Pouch cells incorporating 60 mg cm−2 LATP@C cathodes retain 80.5% capacity after 50 cycles, highlighting the practical viability of this design.