Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202507057
This study presents a novel electrochemical sensor for the rapid detection of trace lead ions (Pb(II)) in water, utilizing a rod-shaped bismuth-based electrode. The electrode was fabricated by modifying a glassy carbon electrode (GCE) with basic bismuth nitrate [Bi6O5(OH)3](NO3)5·3H2O, synthesized via a chemical precipitation method. The sensor was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), electron probe microanalysis (EPMA), and energy-dispersive X-ray spectroscopy (EDS), confirming the rod-like morphology and composition. Electrochemical detection was performed using differential pulse voltammetry (DPV) in a 0.1 mol·L−1 NaAc-HAc buffer (pH 4.3). The sensor exhibited a linear detection range for Pb(II) from 1 to 90 μg·L−1, with a detection limit of 0.34 μg·L−1 and a sensitivity of 106 μA·(μmol·L−1)−1. The electrode demonstrated excellent anti-interference capability and reproducibility. Recovery tests in real water samples (tap water and campus lake water) yielded high recovery rates, indicating practical applicability. This work provides a simple, cost-effective, and reliable method for monitoring trace Pb(II) in environmental water, particularly relevant for public swimming pools and similar aquatic facilities.
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.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025120801
The interaction between microplastic-derived dissolved organic matter (PSDOM) and iron oxides in soil environments can modulate its photosensitization effects, yet the underlying mechanisms remain elusive. This study investigated the influence of hematite with distinct morphologies—flake-shaped (HNPs) and cubic (HNCs)—on the photosensitization of polystyrene-derived dissolved organic matter (PSDOM). Under 500 W mercury lamp irradiation, both hematite morphologies promoted PSDOM degradation, with HNCs exhibiting superior performance: total organic carbon (TOC) decreased from 18.4 mg·L−1 to 12.3 mg·L−1 within 90 min, compared to 13.3 mg·L−1 for HNPs. Three-dimensional fluorescence spectroscopy indicated that hematite alters the humification process, thereby modifying photosensitization. Electron paramagnetic resonance (EPR) spectroscopy identified the generation of singlet oxygen (1O2), hydroxyl radicals (·OH), and carbon-centered radicals (CH3C(=O)OO·). HNCs significantly enhanced 1O2 production, while HNPs favored ·OH generation; both inhibited CH3C(=O)OO· formation. Quantitative analysis via high-performance liquid chromatography revealed that the steady-state concentration of 1O2 was highest with HNCs, reaching 2.80 times that of the PSDOM control, whereas ·OH concentration peaked with HNPs at 1.98 times the control. Notably, the steady-state concentration of 1O2 was approximately three orders of magnitude higher than that of ·OH. These findings elucidate the morphology-dependent role of hematite in PSDOM photosensitization, providing mechanistic insights into the environmental fate of microplastic-derived organic matter in complex soil systems.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3774-3
Lithium-air capacitor batteries (LACBs) integrate the rapid charge-discharge capability of supercapacitors into conventional lithium-oxygen batteries, significantly enhancing power density. However, their cycling stability remains unsatisfactory. In this study, we incorporated redox mediators (RMs) into an LACB featuring a dual-cathode configuration. This design facilitates sustained electron transfer between the electrode and Li2O2/Oxygen, thereby delaying RM deactivation caused by electrode passivation and improving overall electrochemical performance. The RM-enhanced battery achieved over 250 cycles at 2 mA cm−2 with a limited capacity of 0.5 mAh cm−2, while exhibiting a 0.54 V reduction in charging voltage at 0.1 mA cm−2 compared to the RM-free system. Furthermore, application of an aluminum foil sealing technique enabled a power density of 13.8 mW cm−2 at 6 mA cm−2, overcoming mass transport limitations inherent in open-cell configurations. We also investigated the influence of oxygen barrier films with varying barrier capabilities on LACB performance. Results indicate that films with superior oxygen resistance better maintain a clean capacitor electrode surface, thereby providing more stable electron supply to the RMs and enhancing rate capability and cycling performance. These findings underscore the potential of redox mediators in improving the performance and longevity of LACBs, offering a promising strategy for their future development.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202505104
This study investigated the synergistic remediation of aged oil-contaminated soil collected from an oil well in Yanchang, northern Shaanxi, China, with an initial total petroleum hydrocarbon (TPH) concentration of 17.1 g·kg⁻¹, exceeding the second-class land use screening value (4,500 mg·kg⁻¹) by approximately 3.8-fold. Indigenous high-efficiency degrading strains were screened and a microbial consortium was constructed. Pot experiments were conducted to compare the TPH degradation efficiencies and soil property changes under plant, microbial, and combined plant-microbial remediation. The consortium MC-5 (SDB1:SDB2:SDB3:SDB4 = 1:1:0:3) exhibited the highest TPH degradation rate of 83.46% in liquid culture. In soil, combined remediation with ryegrass (Lolium perenne) achieved a TPH degradation rate of 60.93%, significantly higher than the control (CK) by 54.26 percentage points. The consortium also degraded recalcitrant resins and asphaltenes by 49.44%. The microbial consortium played a dominant role, contributing 63%–69% to TPH removal, whereas plant contribution was only 2%–12%, primarily in the later stage. Addition of rhamnolipid biosurfactant enhanced the combined remediation, increasing TPH degradation by 7.05 percentage points compared to non-amended treatments. These findings provide insights into the mechanisms of plant-microbial synergy and offer theoretical and practical guidance for bioremediation of petroleum-contaminated soils.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2026020207
Although the production and use of hexabromocyclododecanes (HBCDs) have been completely banned in China since December 2021, historical production activities may still leave high-concentration residual contamination in localized areas. This study investigated a typical legacy site of historical HBCDs production in eastern China. Surface and core soil samples were systematically collected both inside and outside the former plant area to characterize the occurrence, spatial distribution, and environmental burden of HBCDs, and to evaluate associated human health risks. Results showed that HBCD concentrations in soils outside the plant area ranged from below detection limit to 6.90×10² ng·g⁻¹ dw, while those inside the plant area were substantially higher, reaching up to 1.18×10⁶ ng·g⁻¹ dw. γ-HBCD was the dominant isomer; however, its relative abundance was lower than that reported in commercial HBCD mixtures and in previous studies conducted near production facilities. Outside the plant, HBCDs concentrations in soil generally decreased with increasing distance from the site, yet remained detectable at a distance of approximately 10 km (15.2 ng·g⁻¹ dw). Within the plant area, HBCDs concentrations in soil cores decreased with depth, declining from 1.08×10⁴–1.18×10⁶ ng·g⁻¹ dw in surface soils to 1.05–93.5 ng·g⁻¹ dw at depths of about 4 m. Analysis of the relative cumulative environmental burden indicated that although HBCDs loads were highest in the near-source area, they gradually accumulated over a broader spatial scale. Approximately 23.7%, 40.1%, 60.0%, and 87.1% of the total estimated burden accumulated within 2 km, 2.81 km, 4 km, and 6 km from the site, respectively. Health risk assessment indicated that oral ingestion of soil was the primary exposure pathway for different populations. Localized high-contamination zones within the plant area contributed significantly to non-carcinogenic risks, while overall risks for children outside the plant area were at acceptable levels.
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/s40843-025-3967-x
Metal-halide perovskites exhibit exceptional optical gain, narrow emission linewidths, and high emission efficiency, positioning them as promising candidates for next-generation lasers. Thermal evaporation, a mature semiconductor fabrication technique, offers scalability, yet monitoring phase distribution during deposition remains challenging. This study systematically investigates and regulates thermally evaporated FAxCs0.8PbBr3 perovskite films by tuning formamidinium (FA) content to optimize phase distribution. At intermediate FA content, films achieve a balanced distribution of n=2 to n=5 quantum-well phases, facilitating ultrafast carrier transfer (<0.31 ps) and suppressing nonradiative recombination. FA+ actively incorporates as an A-site cation, promoting ordered crystallization and reducing defect densities. The optimized films exhibit a net modal gain of 1041 cm−1 and a gain lifetime of 129 ps. Benefiting from efficient internal scattering, the threshold for cavity-free random lasing is reduced to below 5 μJ/cm2 at room temperature. The low spatial coherence of random lasing enables speckle-free imaging with a speckle contrast as low as 0.011 and improved contrast-to-noise ratios across all spatial frequencies. This work provides a scalable strategy for perovskite composition-phase engineering, advancing speckle-free laser imaging systems compatible with semiconductor-grade, large-area manufacturing.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-4003-7
The escalating electromagnetic (EM) pollution necessitates the development of high-performance microwave absorbers (MAs) with integrated functionalities. However, it is still a difficult problem to integrate more related high performances into the designed MAs. Herein, a sustainable strategy was reported for fabricating three-dimensional (3D) porous magnetic Ni@C-anchored carbon foams (Ni@C/CFs) with abundant heterointerfaces and magnetic Ni@C nanoparticles using 3D porous chitosan foams and Ni-nitrilotriacetic acid chelate (Ni-NAC) as precursors. The modulation of carbonization temperature and concentration of Ni-NAC solution contributed to the tunable carbon graphitization, Ni crystallinity and magnetic Ni@C nanoparticles loading, which effectively improved their EM properties and EM wave absorption performances (EMWAPs). The optimized 3D porous magnetic Ni@C/CFs not only exhibited exceptional EMWAPs with a minimum reflection loss (RL min) of −27.58 dB and an ultra-wide effective absorption bandwidth (EAB) of 7.20 GHz, but also presented efficient thermal insulation and strong antibacterial activity (>95% inhibition against E. coli), which mainly originated from their excellent magnetic-dielectric synergies and unique 3D hierarchical porous structures. Consequently, this work delivers a coherent design strategy for next-generation multifunctional absorbers with potential applications in EM protection, thermal management, and adaptive stealth technologies.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4223-2
Flexible wearable electronics require materials that simultaneously exhibit high conductivity, mechanical flexibility, and environmental robustness. Polyoxometalate (POM)-based conductive hydrogels are promising candidates but suffer from poor interfacial compatibility with polymer matrices and severe conductivity loss at subzero temperatures. Here, we report a POM-based proton-conductive hydrogel (PVA/P(SBMA-AM)/PW12/PA, denoted PSAWA) engineered by incorporating zwitterionic sulfobetaine methacrylate (SBMA), phytic acid (PA), and H3PW12O40 (PW12) into a poly(vinyl alcohol)-polyacrylamide dual-network. SBMA enhances PW12 loading and dispersion via an electrostatic–steric synergistic mechanism, while PA cooperates with PW12 to construct low-energy-barrier proton-conduction pathways, enabling fast proton migration even at −40 °C. The resulting PSAWA hydrogel achieves ultrahigh proton conductivities of 2.71 × 10−1 S cm−1 at 25 °C and 1.06 × 10−2 S cm−1 at −40 °C, alongside high stretchability, self-healing capability, antibacterial activity, and biocompatibility. Flexible biosensors and supercapacitors fabricated from PSAWA maintain outstanding performance at −40 °C. This work provides a viable strategy for developing low-temperature-tolerant proton-conductive hydrogels for advanced wearable electronics.