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.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3595-7
Cu(I) complexes exhibiting thermally activated delayed fluorescence (TADF) have emerged as promising alternatives to noble-metal-based emitters for organic light-emitting diodes (OLEDs). However, the development of red-emitting Cu(I) complexes has been hindered by slow radiative decay and fast nonradiative decay. In this study, a linear two-coordinate Cu(I) complex, ICuTMC, was designed and synthesized. By pairing a pyrazine-fused N-heterocyclic carbene and a tetra-methylcarbazolyl ligand, a strong ligand-to-ligand charge transfer excited state is generated. Single-crystal structure authenticates close intramolecular C–H···Cu contacts, providing good steric shielding to the metal center. C–H···π interactions between ligands are also revealed. The complex exhibits highly efficient red TADF with emission maximum at 622 nm, photoluminescence quantum yield of 76%, and short delayed fluorescence lifetime of 0.24 μs. This is enabled by a large oscillator strength from the coplanar donor-Cu-acceptor conformation, a small singlet-triplet energy gap from spatial separation of frontier molecular orbitals, and strong spin-orbit coupling from the metal center. Vacuum-deposited OLEDs based on ICuTMC achieve a peak external quantum efficiency of 25.9% and a significantly small roll-off of 1.9% at 10,000 cd m−2. These performances demonstrate a way to overcome the energy gap law for linear coinage metal complexes toward red OLEDs.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60635-4
A Cu-based carbon catalyst (H-Cu/C) with octahedral morphology was synthesized by pyrolyzing the metal-organic framework (MOF) precursor HKUST-1 under inert N2 atmosphere. Characterization via XPS, XRD, SEM, and HRTEM revealed that Cu(0) nanoparticles were uniformly dispersed in a carbon matrix, with island-like Cu2O structures serving as active sites. The carbon matrix effectively stabilized the metal nanoparticles, suppressing migration and sintering during reaction. Combined with TEMPO and using molecular oxygen as a green oxidant, the H-Cu/C catalyst exhibited high efficiency in the selective oxidation of aromatic alcohols to corresponding aldehydes under alkali-free conditions. Using benzyl alcohol as a model substrate, an alcohol conversion of 99.2% and a benzaldehyde yield of 94.1% were achieved under mild conditions (100 °C, 0.5 MPa O2, 1 h). The catalytic system demonstrated excellent universality for various mono- and ortho/para-disubstituted aromatic alcohols, affording conversions over 99% and aldehyde yields above 95%. The catalyst could be regenerated via H2 reduction and reused without significant loss of activity. This work provides a new strategy for designing green and efficient non-noble metal catalytic systems for oxidation reactions.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3820-5
Two novel N-heterocyclic carbene (NHC)-based ligands featuring rigid boron-oxygen (BO) fused-ring units, named Bpmi and Bpmb, and the two corresponding homoleptic meridianal iridium complexes, namely mer-Ir(Bpmi)3 and mer-Ir(Bpmb)3, were designed and synthesized. Single-crystal structures revealed a meridional coordination geometry for both complexes. Shorter Ir–C carbene bond lengths and rigid planar BO-fused ring units contribute to enhanced stability. Both complexes exhibit efficient green phosphorescence (λem = 536/521 nm in toluene, ΦPL > 78%) with short lifetimes (τ = 846/1083 ns), leading to high radiative rate constants (Kr = 10.04 × 10^5 and 7.29 × 10^5 s−1, respectively). Theoretical calculations indicate significantly increased metal-to-ligand charge transfer (MLCT) character (21.69% for mer-Ir(Bpmi)3; 17.30% for mer-Ir(Bpmb)3) compared to reference complexes (13.01% for mer-Ir(pmi)3; 15.99% for mer-Ir(pmb)3). Both complexes exhibit exceptional thermal stability with decomposition temperatures of 491°C (mer-Ir(Bpmi)3) and 540°C (mer-Ir(Bpmb)3). OLED devices using mer-Ir(Bpmb)3 and mer-Ir(Bpmi)3 as emitters demonstrate maximum external quantum efficiencies of 20.0% and 15.6%, respectively. This research pioneers boron-fused ring-containing NHCs and their phosphorescent iridium(III) complexes, establishing a novel design strategy for high-performance NHC-based OLED phosphorescent emitters.
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.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60651-2
Carbohydrates, derived from abundant biomass resources, hold great promise for conversion into fine platform chemicals and fuels, which is crucial for sustainable development. The processes for carbohydrate conversion are predominantly driven by catalysis, with active components such as Brønsted acids and Lewis acids. This review provides a comprehensive overview of the catalytic conversion of various carbohydrates (monosaccharides, disaccharides, and polysaccharides) into high-value-added compounds. It elaborates on the specific pathways and mechanisms involved in reactions like hydrolysis, isomerization, and dehydration for target molecules such as 5-hydroxymethylfurfural, lactic acid, and furfural. Furthermore, the subsequent derivatization of these platform compounds and their application prospects in energy-related fields, including bio-fuels and batteries, are discussed. Finally, the current challenges in research are summarized, and future directions for the development of low-cost and high-performance catalytic systems are outlined.