Journal of Environmental Chemical Engineering
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2026
·Vol. 14
·期 3
·122819
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IF 7.5
Xinruo Wang,Zhonghua Shen,Zhonghua Yang,Jishi Wang,Jingjing Du,Wenjing Liu,Zeying He
Pesticide contamination in real agricultural environments often involves mixed residues, generating complex spectral interference that challenges conventional Raman-based detection. Rapid, accurate, and field-deployable analytical tools capable of identifying multi-component pesticide mixtures across diverse fruit matrices are therefore urgently required. Here, we present an integrated surface-enhanced Raman scattering–Transformer platform for on-site multi-pesticide screening. Reproducible gold nanoparticles provide a highly sensitive and stable surface-enhanced Raman scattering substrate, and a comprehensive dataset of 5979 spectra—covering single, binary, ternary, and quaternary mixtures in apple, peach, pear, and grape extracts—captures realistic variability. The Transformer achieves superior performance, with an accuracy of 98.56% on the standard test set and 97.16% on real fruit extracts, including matrix-specific accuracies of 98.18% for apple, 97.89% for pear, 96.59% for peach, and 96.57% for grape; notably, the precision, recall, and F1-score on the standard test set all exceeded 0.985, five-fold cross-validation yielded 97.74% ± 1.02%, confirming the robustness and reproducibility of the method. Attention-weight visualization offers qualitative interpretability by highlighting relevant spectral regions. On-site orchard trials in Tianjin, China, further validate the robustness and practical applicability of the workflow. Overall, this study advances surface-enhanced Raman scattering-based pesticide detection by establishing one of the largest multi-pesticide spectral datasets to date, demonstrating strong cross-matrix generalization, and delivering an integrated portable Raman–Transformer pipeline suitable for rapid field screening and routine regulatory surveillance.
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ENVIRONMENTAL SCIENCE & TECHNOLOGY
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2025
·
IF 11.3
Zhenhua Dai,Jihong Xu,Jian Guan,Mingyang Feng,Yang Liu,Cuili Xing,Xuanying Cai,Shuchen Wang,Lushi Lian,Hongyu Dong,Zhiyong Jason Ren,Wei Shi,Alicia Kyoungjin An,Shifa Zhong,Xiaohong Guan
Chemical oxidation is pivotal in remediating organic pollutants in aquatic systems; however, it frequently yields transformation products (TPs) with potential toxicological profiles surpassing those of the parent pollutants. Comprehensive identification of these TPs is imperative for environmental risk assessment and optimization of oxidation methodologies. Traditional experimental approaches for TP elucidation are often hindered by substantial financial and technical constraints, limiting their applicability in high-throughput scenarios. Here, we introduce TP-Transformer, an advanced deep learning framework designed to predict both the structures of TPs and their corresponding formation pathways. Trained on Chem_Oxi_2K, a meticulously curated data set comprising 2780 pollutant degradation reactions, TP-Transformer achieved a notable accuracy of 86.28% in TP prediction. The model adeptly reconstructs complete degradation pathways, addressing the intricate challenge of pathway elucidation. Attention analyses indicate that the TP-Transformer discerns reactive moieties within substrates and correlates them with specific reaction conditions, emulating expert-level chemical reasoning. Experimental validations corroborate the model’s robustness, with accurate TP predictions ranging from 80.20 to 92.86% for five pollutants absent from the training data set. These findings underscore TP-Transformer’s potential to transform environmental chemistry by offering a scalable, precise, and efficient alternative to traditional experimental methodologies, thereby enhancing water treatment strategies and safeguarding ecological and human health.
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Tungsten
·
2025
·
IF 11.2
Qian Qi-Ling,Zeng Zhen-Yun,Chen Wan-Ting,Cheng Yu,Zhang Jia,Li Chuan-Hua,Qian Guang-Ren
The promoter is very important for effective catalysts, and the relationship among active species, promoter, and support is a hot topic in the field of catalysis. This work aims to investigate the influence of the promoter on the formation of effective active species. For this purpose, V was leached out from V-loaded TiO 2 (V/Ti) step by step, and the loaded V was classified into different species based on their performances in the catalytic oxidation of chlorobenzene. As a result, when V/Ti was co-doped with Mo or W, the chlorobenzene conversion rate was increased from 48.1% to 54.0% (Mo) or 61.0% (W) at 210 °C. The enhancement was attributed to the redistribution of V species. Specifically, 7.8% of V was “Inactive” V on V/Ti. This “Inactive” V was transformed into an “Active” species. At the same time, “Highly-active V” amounts were increased from 86.0% (V 3.3 /Ti) to 88.1% (WV 3.3 /Ti) and 87.1% (MoV 3.3 /Ti). According to the mechanism investigation, when W and Mo coexisted with V during the synthesis, V 3+ was oxidized into V 5+ , which is easily invaded into the TiO 2 , created more strong acidic sites, and activated more active lattice oxygens. These are all in favor of the effective catalytic oxidation of chlorobenzene. The new understanding of the promoter effect will help to produce a more effective catalyst using the same amount of active species. Graphical abstract
研究领域:
催化化学
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材料科学
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环境化学
·
物理化学
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MICROCHEMICAL JOURNAL
·
2024
·
IF 4.9
Dingkun Yang,Yu Lei,Zhou Fan,Weigen Chen,Fu Wan
Surface-enhanced Raman scattering (SERS), functioning as a highly sensitive trace detection tool, has gained popularity as an analytical technique and is extensively employed in diverse domains such as electrical engineering. In this paper, the exploration is conducted regarding the application of surface-enhancement technology in the quantitative detection of the aging characteristics of transformer oil-paper insulation. Flower-like silver nanoparticles (AgNPs) were deposited in situ on a copper foil coated with porous graphene, thereby fabricating a Cu-graphene (porous)-AgNPs surface-enhanced substrate. This substrate effectively prevented the oxidation of nanoparticles while maintaining the SERS activity. By creating apertures in graphene, the adsorption capability of the substrate for organic small molecules like furfural was enhanced, and the size of silver nanoparticles was optimized to achieve better detection outcomes. The performance of the substrate was evaluated using R6G as the probe molecule, and the findings indicated that the substrate exhibited excellent sensitivity, uniformity (RSD values are below 10 %), and antioxidant properties (after 30 days, the peak strength decreased by 8.23 %). When this substrate was utilized for the detection of transformer aging characteristics, the detection limits for furfural, acetone, and methanol reached 0.04 mg/L, 0.18 mg/L, and 0.18 mg/L respectively, fulfilling the analytical requirements of oil-immersed transformers. This research holds significant importance in guaranteeing the stable operation of the power supply system and the smooth development of the national economy.
研究领域:
电气工程
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材料科学
·
分析化学
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Energies
·
2017
·Vol. 10
·期 7
·967
·
IF 2.262
Fu Wan,Lingling Du,Weigen Chen,Pinyi Wang,Jianxin Wang,Haiyang Shi
Analyzing the concentration of low molecular acids dissolved in oil is vital in the oil-paper insulation aging diagnostic procedure of power transformers. The existing methods cannot distinguish between different acid types and their strengths. In this study, an improved solution Raman detection platform is fabricated. The direct measurement of dissolved acetic acid, a kind of low molecular acids, is observed in transformer oil without extraction. The Raman shift line of oil-dissolved acetic acid at 891 cm−1corresponding to H–C–H symmetrical swing and O–H swing modes is taken as its characteristic value. Taking Raman shift line of pure oil at 932 cm−1as an internal standard, a linear regression curve for quantitative analysis is obtained with a slope of 0.19. The best platform parameter of accumulation number is 300, which is determined by Allan deviation analysis. The current concentration detection limit and accuracy for oil-dissolved acetic acid are obtained at about 0.68 mg/mL and 91.66%, separately. The results show that Raman spectroscopy could be a useful alternative method for evaluation insulation aging state of an operating power transformer in the future.Keywords:power transformer;oil-paper insulation aging state;acetic acid;Raman spectroscopy;internal standard
研究领域:
电气工程
·
分析化学
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POLYMER TESTING
·
2021
·
IF 4.282
Xiaocheng Hu,Zipeng Liang,Qiyang Li,Sideng Hu,Shaoxing Qu
The magnetic permeability of three kinds of inclusions (Carbonyl iron, Ferroferric oxide, NiFe-alloy) filled polymer (Polydimethylsiloxane) is investigated at room temperature by using a Buck circuit for the dynamic test and vibrating sample magnetometer (VSM) for the static test. Unlike the traditional measurement method using an impedance analyzer, the self-built circuit can generate a maximum test current of ampere amplitude over high frequencies to simulate the transformer's operation status. The present work considers the effects of inclusion content (from 0 VOL.% to 33 VOL.%) and testing frequencies(100 kHz-1MHz). The experimental results of permeability are compared with the theoretical predictions in the previous works. Three main conclusions can be drawn: First, the magnetic permeability increases with the inclusion addition; Second, as the testing frequency increases, the permeability of the composite material maintains constant, showing no evident dependence on the testing frequency; Third, type of magnetic inclusion will gently affect the permeability of the composite material. This work systematically investigated the permeability of polymer-based magnetic materials, and the results are helpful for the soft-core transformer application of magnetoactive elastomers.
研究领域:
材料科学
·
电气工程
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Cell Reports Medicine
·
2025
·
IF 10.6
Dongjie Zhang,Zhaoyang Cheng,Yali Song,Huandi Li,Lin Shi,Nan Wang,Yingwen Peng,Renan Chen,Nianzheng Sun,Min Han,Fengjiao Hu,Chuntao Zong,Rui Zhang,Si Chen,Conghui Zhu,Xiaoli Zhang,Xiaobo Li,Xiaopeng Ma,Changbei Shi,Xiaofei Zhang,Rui Liu,Ziqi Ren,Lin Wang,Qi Zeng,Tingting Zeng,Xueli Chen
Rapid identification and accurate diagnosis are critical for individuals with acute leukemia (AL). Here, we propose a combined deep learning and surface-enhanced Raman scattering (DL-SERS) classification strategy to achieve rapid and sensitive identification of AL with various subtypes and genetic abnormalities. More than 390 of cerebrospinal fluid (CSF) samples are collected as targets, encompassing healthy control, AL patients, and individuals with other diseases. Sensitive SERS detection could be achieved within 5 min, using only 0.5 μL volume of CSF. Through integrated feature fusion (1D spectra and 2D image) with a transformer model, the classification method is developed to screen and diagnose AL patients, demonstrating exceptional classification performances of accuracy, sensitivity, specificity, or reliability. Also, this approach demonstrates remarkable versatility and could be extended to the classifications of meningitis diseases. The sensitive DL-SERS classification platform has the potential to be a powerful auxiliary in vitro diagnostic tool.
研究领域:
生物医学工程
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肿瘤学
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医学人工智能
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分析化学
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分子诊断
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Science of Advanced Materials
·
2024
·Vol. 16
·期 8
·891-898
·
IF 0.9
Shi Yurou,Dou Peng,Fu Gaojie,Xie Chenyan,Shen Bin
Adsorption is intensively considered to be the most feasible and economical option for aged transformer oil treatment. In this work, five aminated hyper-crosslinked polymers were prepared from chloromethylated polystyrene–co-divinylbenzene (Cl-PS-DVB) through a two-stage modification
of crosslinking and five different reagents aminating. After modification, the specific surface area (SBET) and nitrogen content (CN) of prepared resins were greatly improved. To evaluate the reclamation performance of resins for removing oxides and contaminants from
aged transformer oils, the total acid number (TAN) and color of oils were mainly examined. The results shown that the prepared resins have superior oil reclamation capacity compared to commercial resin XAD-4 due to their higher microporous specific surface area (Smic) and surface
polarity. Among them, the NHC-R3, which used dimethyltriamine (DETA) as an aminating agent, had the best deacidification and decolorization performance. A series of experiments were designed to investigate the optimal regeneration program conditions for NHC-R3, and the additional electrical
characteristics of the reclaimed oil, including breakdown voltage, dielectric dissipation factor (DDF), and interfacial tension, were measured as evidence that aged oil was successfully reclaimed.
研究领域:
材料科学
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环境工程
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JOURNAL OF CHEMICAL AND ENGINEERING DATA
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2025
·
IF 2.1
Yutong Gong,Feng Liu,Jun Sui,Yanlong Lv,Yuhao Wang,Yunze Hao,Xiaodong Wang,Hongguang Jin
Absorption heat pumps are categorized into two main types: absorption heat pumps (AHPs) and absorption heat transformers (AHTs). AHT is defined as a system that upgrades medium-temperature heat to higher temperatures for industrial applications. So far, the majority of research on working pairs has been concentrated in the medium-to-low temperature range of AHPs, this research delves into the thermodynamic evaluation and performance metrics of ternary working pairs designed for high-temperature absorption heat transformers. The integration of ionic liquids (ILs) with conventional H2O/LiBr mixtures aims to overcome the drawbacks of traditional working pairs, including issues like corrosion and toxicity. The corrosion rates of eight ternary IL working pairs were measured under high-temperature conditions. After a preliminary evaluation of short-term corrosion measurements, four working pairs with lower corrosiveness were selected for further thermodynamic analysis. The results indicate that the ternary working pairs containing [EPy][Br] and [BMIM][I] exhibited lower viscosity, but their specific heat capacities were notably lower compared to the working pair containing [EMIM][Ac]. Furthermore, the ternary working pair containing [EMIM][MeSO3] showed significantly higher corrosiveness than the other three pairs. Based on this analysis, the ternary working pair formed by adding [EPy][Br] to the conventional LiBr solution demonstrates greater potential for application in high-temperature AHT systems.
研究领域:
工程热物理
·
化学工程
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ACS Sensors
·
2024
·
IF 8.2
Yingying Jian,Nan Zhang,Yunzhe Bi,Xiyang Liu,Jinhai Fan,Weiwei Wu,Taoping Liu
Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.
研究领域:
医学
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人工智能
·
生物标志物研究
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Advanced Materials Interfaces
·
2023
·Vol. 10
·期 7
·2202216
·
IF 5.4
Chengcheng Xu,Jingjing Yang,Yang Wang,Xing Zheng,Yin Long,Hongfei Du,Xian Li,Chunjiang Bai,Xiaosong Du
Hydrocarbons and carbon oxides are typical dissolved gases in transformer oil that reflect the latent pitfalls, on-line monitoring of their concentrations can effectively evaluate the operating status of the power transformer. However, these low-concentration targets (especially for CH 4 ) show high chemical inertness at room temperature, challenging the sensitive performance of current commonly used chemiresistive gas sensors. Herein, a strategy by combining traditional inorganic semiconductors to carbon nanofiber (CNF) via electrospinning–annealing route is described. Three optimized 3D mosaic films, CNFs scaffold incorporated with WO 3 , SnO 2 and MoS 2 nanoparticles, are obtained. Due to the large specific surface area of the 3D network, and the synergic and heterojunction effects between nanoparticles and CNFs, all three sensors exhibit high response to CH 4 at room temperature, and also record distinguishable signals toward H 2 , C 2 H 4 , CO and CO 2 , revealing the three sensors are cross-sensitive to the five analytes. Accordingly, preliminary discrimination of five dissolved gases is realized by principle component analysis. This study provides an effective and extendable solution of preparing room-temperature chemiresistive sensors for the detection of CH 4 and other gases, and offers a strategy for the construction of sensor array to achieve a high discrimination capability.
研究领域:
材料科学
·
电气工程
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高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2026
·
IF 4.6
Mintong Zhao,Zhilong Tang,Mingyang Zhou,Xiaohan Zhang,Xinyu Wang,Xingchu Gong
The manufacturing of Chinese medicines often faces challenges such as poor product consistency, high solvent consumption, and long processing times. The percolation process is a commonly used technique for extracting medicinal herbs. Significant variation in percolate concentration and low concentration near the endpoint make it difficult for existing online detection technologies to accurately determine target component concentrations. To address this, the study developed an online monitoring system integrating multi-modal sensors for physical quantity, image, and spectral data. Using Xiaochaihu capsules, real-time multimodal data were collected, including over 20,000 physical quantity points, 14,000 spectra, and 14,000 images. A Transformer-based framework, PMFormer, was proposed, with interpolation-based data augmentation to alleviate the “data-rich but label-scarce” problem. PMFormer achieved R 2 values of 0.96, 0.94, and 0.91 for 6-gingerol, 8-gingerol, and adenine, with RMSEs below 2.4, 0.4, and 1.8 μg/mL, respectively. A quantitative extraction control strategy was developed, determining the percolation endpoint when the accumulated total mass of collection (ATMC) met quality control limits. Validation showed improved consistency, reduced solvent use, and enhanced efficiency, aligning with Lean Six Sigma concepts. This study provides a reference for online monitoring of TCM percolation processes and demonstrates the potential of multimodal data fusion in pharmaceutical manufacturing.
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