Microchemical Journal最新文献

{"title":"A precision inhibitor screening strategy from complex herbal medicine for hyaluronidase based on carbon quantum dot sensing and targeted capture technology","authors":"Mei Wang ,&nbsp;Hui Yuan ,&nbsp;Xinmin He ,&nbsp;Lingling Yang ,&nbsp;Zhigang Yang ,&nbsp;Tao Gao ,&nbsp;Weibiao Wang ,&nbsp;Fen Ma ,&nbsp;Weiman Zhang ,&nbsp;Gidion Wilson Mening' oo ,&nbsp;Yuping Sa ,&nbsp;Xiaofei Chen ,&nbsp;Guoning Chen ,&nbsp;Xueqin Ma","doi":"10.1016/j.microc.2026.117295","DOIUrl":"10.1016/j.microc.2026.117295","url":null,"abstract":"<div><div>Hyaluronidase (HAase), an enzyme responsible for hyaluronic acid (HA) degradation, compromises the structural integrity of HA by cleaving anti-inflammatory high-molecular-weight HA into pro-inflammatory lower molecular weight forms. Consequently, developing simple and efficient strategies for screening HAase inhibitors is of critical importance. Herein, a novel biosensing-based targeted affinity screening method was established for the identification of HAase inhibitors from complex herbal medicines. In the biosensing system, carbon dots served as fluorescent probes, while HA-functionalized gold nanoparticles acted as quenchers, and the addition of HAase induced a marked fluorescence recovery. Under optimized conditions, HAase exhibited a good linear response over the concentration range of 5.3 to 343.8 U mL⁻¹. This method enables rapid and efficient detection of HAase inhibitory activity in complex herbal extracts. Subsequently, active components were selectively captured, separated, and identified using immobilized HAase affinity chromatography. Following validation with both negative and positive control drugs, the screening model successfully identified <em>Rhodiola rosea</em> L. among 43 herbal medicines as a potent HAase. Gallic acid (GA) was identified as the active constituent, exhibiting an IC₅₀ value of 111.7 μM. Results from molecular docking and molecular dynamics simulations demonstrated a strong binding affinity between GA and HAase. Additionally, cellular experiments confirmed the anti-inflammatory activity of GA, as evidenced by its significant inhibition of LPS-induced inflammatory markers IL-4, IL-5, IL-6, and TNF-α. Collectively, the integrated biosensing and affinity-based screening strategy established in this study provides a precise and efficient platform for the discovery of bioactive compounds from complex natural products.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117295"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New insights into sulfur dioxide absorption in deep eutectic solvents","authors":"Chaofan Hu ,&nbsp;Farag M.A. Altalbawy ,&nbsp;Krunal Vaghela ,&nbsp;V. Vivek ,&nbsp;Sarbeswara Hota ,&nbsp;Devendra Singh ,&nbsp;Mahesh Manchanda ,&nbsp;Prakhar Tomar ,&nbsp;Raed Alfilh ,&nbsp;Aseel Smerat ,&nbsp;Mehrdad Mottaghi","doi":"10.1016/j.microc.2026.117312","DOIUrl":"10.1016/j.microc.2026.117312","url":null,"abstract":"<div><div>This study intends to accurately predict the absorption capacity of sulfur dioxide (SO₂) in deep eutectic solvents (DESs) by developing robust machine learning (ML) models. A comprehensive dataset comprising 1382 experimental and 924 calculated data points was constructed, covering 156 unique DESs derived from 22 hydrogen bond acceptors (HBAs) and 42 hydrogen bond donors (HBDs). Input variables included sigma profile descriptors (s1–s10), water content, pressure, and temperature. Fifteen ML algorithms were evaluated, including Support Vector Regression (SVR), Convolutional Neural Networks (CNN), Random Forest, XGBoost, and LightGBM. Dataset reliability was confirmed using a Monte Carlo outlier detection algorithm. Among all models, SVR and CNN attained the highest level of performance on the testing dataset, with R² values of 0.9872 and 0.9904, mean squared errors (MSE) of 0.0015 and 0.0011, and Mean relative deviation (MRD) of 9.11% and 8.95%, respectively. The analysis identified pressure and structural descriptors (particularly s8) as key variables influencing SO₂ absorption. These results highlight the effectiveness of ML techniques in modeling gas absorption behavior in complex DES systems and support their application in the design of high-efficiency solvent systems.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117312"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical sensors used for identify autophagy-related hub genes and their correlation with immune infiltration in nasopharyngeal carcinoma: A bioinformatics analysis combined with experimental validation","authors":"Bo Zheng,&nbsp;Haiyong Jin,&nbsp;Xiaoqiong Wang,&nbsp;Xuejun Liu","doi":"10.1016/j.microc.2026.117353","DOIUrl":"10.1016/j.microc.2026.117353","url":null,"abstract":"<div><div>Electrochemical sensor technology converts biological recognition events into quantifiable electrical signals, enabling direct and efficient detection of trace proteins, nucleic acids, and exosomes in complex body fluids. It shows broad application prospects in cancer early screening, therapeutic efficacy monitoring, and individualized treatment decision-making. The research obtained NPC transcriptome data from public databases (such as TCGA, GEO), screened differentially expressed autophagy-related genes using bioinformatics methods, and conducted functional enrichment, protein interaction network construction, and immune infiltration correlation analysis. The study did not conduct experimental verification; all results were derived from the re-examination and integrated analysis of existing data. Some autophagy genes were closely related to the patient's prognosis, suggesting their potential clinical value. The study revealed the important role of autophagy regulation in the shaping of the NPC immune microenvironment. The high-specificity electrochemical detection platform based on these targets is expected to achieve non-invasive and dynamic monitoring of the autophagy status and immune response of NPC patients, thereby promoting the implementation of precise diagnosis and treatment strategies.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117353"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anionic PbMOF based yellow-emitting ratiometric luminescence sensor for efficient detection of styrene biomarker","authors":"Liying Liu ,&nbsp;Jingrui Yin ,&nbsp;Yun Zhou,&nbsp;Jiayuan Liu,&nbsp;Xiaoting Li,&nbsp;Hu Ren,&nbsp;Honglin Wang,&nbsp;Liming Fan","doi":"10.1016/j.microc.2026.117327","DOIUrl":"10.1016/j.microc.2026.117327","url":null,"abstract":"<div><div>The establishment of reliable and non-invasive urinary phenylglyoxylic acid (PGA) detection remains a critical occupational health priority, given its role as the primary exposure biomarker for styrene (SM) in industrial workers. In this work, an anionic 3D lead(II) organic framework of {[Pb(TPTA)_0.5]·Me₂NH₂}_n (PbMOF) was fabricated under hydrothermal condition and displayed dual-emission features and emitted bright yellow luminescence under UV excitation, demonstrating exceptional potential as a highly efficient luminescent sensor for PGA. It exhibited remarkable selectivity, superior sensitivity, an ultralow detection limit (LOD) of 100.4 nM, and a limit of quantification (LOQ) of 334 nM. Leveraging the photochromic response of PbMOF toward PGA under UV irradiation, an integrated logic gates based intelligent detection system was established to simultaneously enhance operational convenience and intelligence. These results not only establish PbMOF as a promising intelligent platform for early diagnostic of SM exposure biomarker, but also provides a feasible strategy to construct non-rare earth pristine MOF based ratiometric luminescent sensor.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117327"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Qualitative analysis of aflatoxin B1 in soybeans using an anthocyanin-based colorimetric sensor array","authors":"Ziyue Chen ,&nbsp;Jingwen Zhu ,&nbsp;Jihong Deng ,&nbsp;Yuhan Ding ,&nbsp;Congli Mei ,&nbsp;Hui Jiang","doi":"10.1016/j.microc.2026.117331","DOIUrl":"10.1016/j.microc.2026.117331","url":null,"abstract":"<div><div>A colorimetric sensor array (CSA) constructed from nine natural anthocyanins was developed to enable qualitative analysis of aflatoxin B1 (AFB1) in soybeans. This CSA effectively captures information on volatile organic compounds (VOCs) released from soybeans exhibiting varying levels of AFB1 contamination. After dimensionality reduction of the collected data using t-SNE, classification was performed with support vector machine (SVM) models optimized by grey wolf optimization (GWO), particle swarm optimization (PSO) and rime-ice optimization (RIME). Among these, the RIME-SVM model achieved the best performance, reaching an accuracy of 96.67% in distinguishing whether the AFB1 content exceeded the maximum limit of 5 μg/kg specified by the National Food Safety Standard of China (GB 2761–2017). Overall, this study establishes a new method for differentiating AFB1 contamination levels in soybeans, addressing the challenges of conventional detection techniques—namely their operational complexity, high cost and limited suitability for rapid on-site screening—and demonstrating promising prospects for applications in food safety monitoring.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117331"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-waste device category classification combining LIBS and machine learning","authors":"Dennis S. Ferreira ,&nbsp;Raffaele Vitale ,&nbsp;Edenir R. Pereira-Filho","doi":"10.1016/j.microc.2026.117297","DOIUrl":"10.1016/j.microc.2026.117297","url":null,"abstract":"<div><div>The efficient management of electronic waste (e-waste) is a global challenge, limited by the high heterogeneity of the material, which hinders the recovery of valuable metals. This study presents an alternative approach to automated e-waste sorting, based on the hypothesis that different categories of devices have a distinctive elemental fingerprint, even after being ground and homogenized. Samples of printed circuit boards (PCBs) from three categories (computers/laptops, cellphones, and tablets) were processed and analyzed by Laser-Induced Breakdown Spectroscopy (LIBS). A total of 58 samples were systematically analyzed, with 130 individual spectra acquired per sample, yielding 7540 individual LIBS spectra for comprehensive model development. A machine learning pipeline was developed, comparing feature selection/compression strategies (ANOVA, Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis (PCA), oversampling strategies (Random OverSampler, SMOTE, ADASYN), and four well-established classification algorithms (Logistic Regression, Random Forest, KNN, and RBF-SVM). Systematic adjustment revealed distinct advantages for two approaches: PCA-RF combined with SMOTE achieved the highest classification performance (F1-score 0.7611), while ANOVA-LR (F1-score 0.7323) demonstrated superior stability and direct chemical interpretability. In external validation (conducted on an independent test set), the model obtained an F1-score of 0.7611, demonstrating high robustness and generalization ability. In addition, a Leave-One-Device-Out (LODO) validation scheme consistently yielded an average F1-score of 0.7568 across 16 different devices, confirming the robustness of the model against brand and model variability. Chemical interpretation, supported by ANOVA variable selection, permitted to identify Al, Si, Sn, Ba, Ca, Cu, K, and Na as the most relevant discriminating markers, linking statistical separation to specific characteristics such as substrate composition, miniaturized capacitors, and soldering materials. It is concluded that the proposed method is a viable and promising pre-sorting strategy for the recycling industry, enabling rapid and chemically interpretable classification.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117297"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metal–complex assisted resonant SERS for trace-level detection of hazardous gaseous pyridine","authors":"Mariia V. Samodelova ,&nbsp;Nikita R. Yarenkov ,&nbsp;Igor K. Solontsov ,&nbsp;Irina A. Lemesh ,&nbsp;Olesya O. Kapitanova ,&nbsp;Irina A. Veselova","doi":"10.1016/j.microc.2026.117272","DOIUrl":"10.1016/j.microc.2026.117272","url":null,"abstract":"<div><div>Pyridine and its derivatives are highly toxic and strictly regulated class of compounds, making their sensitive monitoring essential for human health and environmental safety. Despite the high sensitivity of surface-enhanced Raman scattering (SERS) for detecting various analytes, analysing volatile compounds remains difficult due to the inability to retain the analyte on noble metal nanoparticle surfaces. Herein, we demonstrate for the first time that SERS enables the sensitive, rapid and simple detection of the toxicant pyridine in gaseous form. We introduce a novel strategy that provides SERS analysis of volatile analytes, thereby expanding the scope of molecules accessible to this method. Trapping pyridine in stable transition metal–pyridine complexes enables shifting the absorption maximum from ultraviolet to the visible spectral range (500–660 nm), which is closer in energy to the plasmon absorption band of the silver nanostructured substrate and the laser energy of the Raman spectrometer. Thus, we observed sensitivity down to 5.7 mg/m³ using benchtop Raman spectrometer with a 638 nm laser wavelength, achieving detection limits below the permissible exposure limit for gaseous pyridine, due to the additional resonant enhancement. The immobilisation of transition metal ions in a porous chitosan layer on the plasmonic surface efficiently captures and retains pyridine vapours near the surface. This approach demonstrates broad potential for the trace-level monitoring of a wide class of volatile analytes by SERS.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117272"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in design, and applications of electrochemical sensors focused on green screen-printed electrodes to monitor heavy metals in food and beverage","authors":"Zohre Eskandari Alughare ,&nbsp;Alireza Sanati ,&nbsp;Zahra Esfandiari ,&nbsp;Parham Joolaei Ahranjani","doi":"10.1016/j.microc.2026.117192","DOIUrl":"10.1016/j.microc.2026.117192","url":null,"abstract":"<div><div>Heavy metals (HMs) are known as toxic and non-biodegradable pollutants. It is essential to develop a quick, and cost-effective sensing platform for the detection of HMs. Electrochemical sensor based on screen-printed electrodes (SPEs) have obtained continuous consideration in recent years by offering sensitivity, selectivity, disposability, cost-effectiveness, portability, simplicity in pretreatment steps, eco-friendly methods, and improving signal-to-noise ratio due to using a small sample volume. Various researches are being conducted to develop green sensing platforms to minimize toxic effects of reagents, materials, and solvents utilized in the structure of electroanalytical sensors to monitor HMs. Therefore, this review represents the efforts on the scope of detection of HMs based on green SPEs as eco-friendly environmental sensing systems. Moreover, it examines green electrochemical sensor design, and sensor performance in important features to present insights about practical challenges and successful approaches regarding to determination of HMs in the real samples of food and beverages. Lastly, future trends focusing on green portable electrochemical sensor development combined with artificial intelligence (AI) are highlighted. It was found that applying green modifiers for preparation of sensors based- SPEs such as non-hazardous materials, reagents, substrates as well as green synthesis methodologies can decrease or prevent environmental impact for detection of HMs. Additionally, an efficient pretreatment process can improve sensitivity and selectivity of assessment of HMs through eliminating interfering compounds. Notably, the combination of portable devices, AI and deep learning algorithms can enable to produce devices with capability of multi-analytes detection and delivering accurate and reliable results toward safety assurance and commercialization as future developments.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117192"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bio-inspired interface engineering: hierarchical dendritic gold nanostructures for ultrasensitive electrochemical detection of fungal contamination biomarker","authors":"Wenting Li ,&nbsp;Yongqiang Shi ,&nbsp;Xinai Zhang ,&nbsp;Jiyong Shi ,&nbsp;Yuanyuan Zhu ,&nbsp;Yongjian Yu ,&nbsp;Dong Han ,&nbsp;Xiaobo Zou","doi":"10.1016/j.microc.2026.117346","DOIUrl":"10.1016/j.microc.2026.117346","url":null,"abstract":"<div><div>Peanuts are prone to fungal contamination during both production and storage, threatening food safety and public health. Ergosterol (Erg), a specific sterol in fungal cell membranes, functions as an effective biomarker for the early detection of fungal infections. This study introduces an electrochemical sensor using biomimetic hierarchical dendritic gold nanostructures (HDGNs) for rapid and sensitive Erg detection. Inspired by the hierarchical branching architecture of natural dendrites, HDGNs offer a high specific surface area and facilitate efficient electron transport. A bio-inspired sensing platform was developed by modifying indium tin oxide (ITO) electrode with HDGNs. Employing differential pulse voltammetry (DPV) method, the sensor exhibited a broad analytical range for Erg with a low detection limit of 3.8 ng/mL. Quantitative analysis between peanut samples was performed by the standard addition method to eliminate the sample matrix effects. Spike-and-recovery tests on peanut samples closely matched high-performance liquid chromatography (HPLC) results, with a relative error under 4.1%. Overall, this study introduces an efficient, cost-effective method for rapid Erg quantification and highlights the potential of biomimetic nanostructures in early food mildew detection and food safety monitoring.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117346"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SILAR-derived NiWO₄ and CoWO₄ Nanopebble electrocatalysts for non-enzymatic glucose detection in real blood samples","authors":"Rakesh A. Mohite ,&nbsp;Satish B. Jadhav ,&nbsp;Sohel Shaikh ,&nbsp;Rohan Mohite ,&nbsp;Sandip R. Sabale ,&nbsp;Vinayak G. Parale ,&nbsp;Minjae Kim ,&nbsp;Padmaja N. Pawaskar","doi":"10.1016/j.microc.2026.117220","DOIUrl":"10.1016/j.microc.2026.117220","url":null,"abstract":"<div><div>A highly sensitive and accurate measurement of blood glucose levels is essential for preventing and managing diabetes. In this study, nanopebble-structured nickel tungstate (NiWO₄) and cobalt tungstate (CoWO₄) thin film electrodes were synthesized on stainless steel (SS) substrates using the cost-effective successive ionic layer adsorption and reaction (SILAR) technique. This method enabled the formation of a highly porous, nanocrystalline, and binder-free adherent film, which significantly enhanced the electrochemical sensing performance. The NiWO₄ thin film electrode exhibited outstanding sensitivity of 4130 μA·mM⁻¹·cm⁻² within a linear detection range of 50–800 μM, surpassing that of the CoWO₄ electrode (3432 μA·mM⁻¹·cm⁻²). The LOD values for NiWO₄ and CoWO₄ were determined to be 14.9 μM and 16.7 μM, respectively, confirming that the NiWO₄ electrode offers superior detection capability and higher analytical reliability compared to the CoWO₄ film electrode. Furthermore, glucose concentrations in real blood samples were accurately quantified using the proposed nanocrystalline electrodes. The measurement accuracy was 95.68% for NiWO₄ and 93.20% for CoWO₄ when benchmarked against a commercial glucometer, confirming the practical applicability of these electrodes and highlighting their potential for the development of advanced non-enzymatic sensing technologies.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117220"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}