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.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2022
·
IF 9.231
Huihui Yang,Xiaosen Bai,Baolong Feng,Qinghua Wang,Li Meng,Fengzhong Wang,Yutang Wang
Advanced glycation end products (AGEs) are associated with the occurrence of human chronic diseases, and exist commonly in thermally processed foods, such as infant formula. Existing research mainly focuses on the discrete simulation system, which is time-consuming and challenging, but accumulates of a large amount of valuable data. This study aimed to propose a specific Molecular Transformer-based model trained on the data curated from literature to predict the chemical reaction of AGEs, and apply it to infant formula to observe which new reactions could generate AGEs. The model achieved top-3 accuracy of 76.0% on the total dataset. Based on the model prediction results, five reactions were selected for experimental verification, and four of them were consistent with the model prediction results. This prospective study might potentially revolutionize the discovery of AGEs reactions and provide theoretical guidelines for designing a safer infant formula.
研究领域:
食品科学
·
生物信息学
·
医学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
·
2024
·
IF 8.1
Zijian Wang,Yajing Chen,Jishun Guo,Dongzhi Zhang,Mingcong Tang,Yuehang Sun,Xingyan Shao
Hydrogen (H 2 ) is one of the most critical gases in transformer oil. In this paper, an H 2 sensor based on spindle-shaped multilateral CeO 2 rods in micron size and TiO 2 quantum dots (QDs) with an average size of 3.09 nm was designed. The sensor based on CeO 2 with 1 wt%TiO 2 has the best performance and is labeled as Ce–Ti1 sensor. The Ce–Ti1 sensor operates at 250 °C and has the better response value of 52.51% to H 2 of 1000 ppm. The response time is less than 1 s for 2000 ppm H 2 and the response time is 1 s. The Ce–Ti1 sensor has low response values for several dissolved gases in transformer oils (5.78 times than 1000 ppm C 2 H 6 , 9.28 times than 50 ppm CO, and 18.04 times than 1000 ppm CH 4 ). The response of the Ce–Ti1 sensor is affected by humidity, while it shows good long-term stability and repeatability. The response intensity can decrease by 50.21% in RH = 85% compared with sensing performance in RH = 33%. First-principle calculations are used to analyze the H 2 -sensing properties of TiO 2 , CeO 2 , and their composite heterostructures from a microscopic perspective. The adsorption mechanism between the Ce–Ti1 heterojunction and H 2 molecules is analyzed by the adsorbed oxygen mechanism. The construction of DFT calculations and H 2 -sensitive mechanism models can provide guidance for the subsequent construction of high-performance CeO 2 -based gas sensors.
研究领域:
材料科学
·
化学
✓ 已验证
DOI
高影响因子
JOURNAL OF MOLECULAR MODELING
·
2026
·Vol. 32
·期 3
·59
·
IF 2.9
Shi Haiyang,Gan Guochao,Sun Shizheng,Han Xue,Ding Hao,Liu Shuang,Huang Yingzhou,Li Kai,Chen Weigen
Context The application of Raman spectroscopy for detecting furfural dissolved in transformer oil represents a highly promising approach for online monitoring for assessing the aging process of transformer insulating paper. Combining density functional theory (DFT) computational simulations with experimental measurements, the characteristic Raman spectra of the furfural molecule were analyzed, and the assignments of its key vibrational modes were determined. Furfural molecular clusters of varying sizes were constructed to investigate the effect of intermolecular hydrogen bonding forces on the Raman signals. The Raman spectra of furfural in solvents with different dielectric constants were measured. Comparison with simulation results indicates that enhanced solvent polarity induces a rearrangement of the electron cloud around the C=O bond, an increase in dipole moment, and a narrowing of the band gap. These changes thereby result in a red shift of the Raman peak position and an enhancement of scattering intensity. This work deepens the understanding, at the microscopic level, of the aggregation behavior of furfural in transformer oil and the influence of the solvent environment on its Raman characteristics. Methods All quantum chemical calculations were conducted using the Gaussian 16W program, and molecular structures were built using GaussView 6.0. The ground-state geometry of furfural was optimized using DFT with the B3LYP functional. The solvent effect caused by solvents with different dielectric constants was simulated and calculated using the polarized continuum model (PCM) of furfural. For all computations, the 6-311+G (2d, p) basis set was employed for C, H, and O atoms.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Coatings
·
2025
·Vol. 15
·期 11
·1319
·
IF 2.8
Wei Zhang,Qiwei Yang,Yuanyuan Jin,Yanzong Meng,Leyu Shen,Xuran Zhu,Haifeng Gao,Chuan Chen
Omniphobic membranes have gained extensive attention for mitigating membrane wetting in robust membrane separation owing to the super-repulsion toward water and oil. In this study, a Teflon/PAI composite membrane with omniphobic characteristics was prepared by a vacuum-assisted dip-coating strategy on the PAI hollow fiber membrane. A series of characterizations on morphological structure, surface chemical composition, wettability, permeability, mechanical properties, and stability were systematically investigated for pristine PAI and Teflon/PAI composite membranes. Subsequently, the experiment was conducted to explore the oil–gas separation performance of membranes, with standard transformer oil containing dissolved gas as the feed. The results showed that the Teflon AF2400 functional layer was modified, and C-F covalent bonds were introduced on the composite membrane surface. The Teflon/PAI composite membrane exhibited excellent contact angles of 156.3 ± 1.8° and 123.0 ± 2.5° toward DI water and mineral insulating oil, respectively, indicating omniphobicity. After modification, the membrane tensile stress at break increased by 23.0% and the mechanical performance of the composite membrane was significantly improved. In addition, the Teflon/PAI composite membrane presented satisfactory thermal and ultrasonic stability. Compared to the previous membranes, the Teflon/PAI composite membrane presented a thinner Teflon AF2400 separation layer. Furthermore, the omniphobic membrane demonstrated anti-wetting performance by reaching the dynamic equilibrium within 2 h for the dissolved gases separated from the insulating oil. This suggests an omniphobic membrane as a promising alternative for oil–gas separation in monitoring the operating condition of oil-filled electrical equipment online.
研究领域:
材料科学
·
膜技术
·
化学工程
·
电气绝缘
·
气体分离
✓ 已验证
产品关联
DOI
高影响因子
CHROMATOGRAPHIA
·
2020
·Vol. 83
·期 8
·927-932
·
IF 1.596
Liu Xiaobo,Zhang Ying,Shang Meijie,Zhu Liping,Yu Min
The effects of furoic acid and furfuryl alcohol on the detection of furfural in transformer oil were studied using the traditional liquid–liquid extraction combined with high-performance liquid chromatography (LLE-HPLC) technology, in which, the methanol used as extraction agent and the C18 column and ultraviolet−visible detector were used as the separation and detection units for the determination of the furfural. The results show that the presence of furfuryl alcohol or furoic acid increases the extraction efficiency of furfural from 42.07 to 73.21% and 65.33%, respectively. The extraction efficiency of furfural decreases when the concentrations of furfuryl alcohol increase from 0.2 to 0.6 mg L −1 , but the extraction efficiency of furfural increases with the concentrations of furoic acid from 0.2 to 0.6 mg L −1 . This indicated that the accuracy of detection and extraction efficiency of furfural in transformer oil by LLE-HPLC technology may be affected by the furfuryl alcohol or furoic acid dissolved in transformer oil.
✓ 已验证
产品关联
DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2022
·
IF 4.831
Fu Wan,Yu Lei,Changding Wang,Xinyuan Zhang,Honglin He,Lei Jia,Tingting Wang,Weigen Chen
Accurately evaluating the aging state of oil paper insulation in electrical equipment is a key to ensure the safe operation of the power transformer. For achieving highly sensitive in-situ detection of dissolved furfural in transformer oil with good reproducibility, flower-like silver nanoparticles modified with carbon nanotubes (CNTs@Ag-F-AgNPs) was synthesized by a combination of electroless silver plating and redox method. The large specific surface area and strong adsorption capacity of CNTs@Ag promoted the formation of more “hot spots”. CNTs@Ag-F-AgNPs were adsorbed on Si-Au substrate via mercapto groups on the coupling agent 1′4 phenyldimercaptan molecule (BDT). Using rhodamine 6G (R6G) as probe molecule, the enhanced factor reached 6.96 × 10 9 . Then, the substrate was used for in-situ SERS detection of transformer oil-dissolved furfural at different concentrations and the detection limit was 2.25 mg/L at 1703 cm −1 (Stretching vibration of C = O in furfural molecule), fulfilling requirements of furfural content detection after severe aging of transformer (4 mg/L). Besides, the relative standard deviation (RSD) of characteristic peak intensity at ten different positions was only 1.74%. These results exhibite that three-dimensional nanostructure with high sensitivity and good reproducibility exhibited a wide application range for in situ detection of dissolved trace furfural in transformer oil.
研究领域:
材料科学
·
电力设备检测
·
分析化学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2026
·
IF 10.4
Wenlong Liao,Juan Wu,Qian Long,Ying Zhu,Junling Shao,Yue Ma,Qiuzuo Shen,Huiming Li,Lingpu Jia,Kunping Liu
A MoS 2 @Ag nanoflower SERS substrate with 1T/2H mixed-phase is synthesized. • The prepared substrate achieving a high SERS enhancement factor of 1.76 × 10 8 . • The Transformer network achieved 99.82% classification accuracy for four antibiotics. Fluoroquinolone antibiotics (FQs) residues in dairy products pose a persistent challenge to food safety. This work establishes an integrated analytical platform combining a three-dimensional molybdenum disulfide‑silver nanoflower (MoS 2 @Ag NFs) SERS substrate with a transformer network based deep learning model for the sensitive detection and intelligent classification of FQs in milk. The prepared MoS 2 @Ag NFs substrate with a 1T/2H mixed-phase MoS 2 and a hierarchical structure densely decorated with silver nanoparticles (Ag NPs), exhibits a high enhancement factor and achieves nanomolar-level detection limits for four representative FQs. To overcome the discrimination challenge posed by the highly similar spectral signatures of different FQs, a transformer network is employed to automatically extract decisive spectral features via its self-attention mechanism, which results in an exceptional classification accuracy exceeding 99.82%. The integrated platform enables sensitive and intelligent monitoring of FQs in milk, providing a promising strategy for on-site detection of antibiotic residues in complex food matrices. Download: Download high-res image (125KB) Download: Download full-size image
✓ 已验证
DOI
高影响因子
MICROCHEMICAL JOURNAL
·
2025
·
IF 5.1
Zengshan Yu,Xuemei Lin,Zelong Li,Shan Guo,Jiahao Cui,Jianjun Gu,Mingli Wang,Guochao Shi
Carcinoembryonic antigen (CEA), a critical colorectal cancer biomarker, playing a pivotal role in the early diagnosis, therapeutic intervention and longitudinal monitoring of malignancies. Conventional detection methods such as ELISA exhibit inherent limitations including inadequate analytical sensitivity and prolonged assay duration. This study proposes the development of a SERS-based immunosensor integrated with artificial intelligence (AI) frameworks, aiming to achieve precise and quantitative detection of CEA. The antibody-conjugated gold nanoprobes (Au NPs/4-MBA/Ab 1 ) were immobilized onto the surface of another antibody-functionalized Ag/Cu/PSS substrate (Ab 2 /Ag/Cu/PSS) via specific biorecognition. This sandwich-structured immunosensor, engineered with blockage of non-specific binding sites, demonstrated enhanced capacity to capture CEA antigens through dual antibody recognition layers. The immunosensor enabled efficient amplification of SERS signals and exhibited excellent linearity within the CEA concentration range of 0.1–1000 ng/mL. Importantly, Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Transformer frameworks, were employed for hierarchical processing of CEA Raman data. The Transformer model demonstrated superior performance with a classification accuracy of 99.06 % and a low loss value of 0.1056. These results underscore the model's exceptional capability to discriminate CEA concentrations with high precision and robustness. The integration of AI-driven signal processing boosts analytical sensitivity of SERS-based immunosensors. This synergy between nanophotonic sensing and deep learning algorithms represents a transformative leap in early cancer detection via non-invasive biofluid analysis.
研究领域:
生物传感器
·
纳米光子学
·
癌症诊断
·
人工智能
·
免疫传感器
·
分析化学
·
生物医学工程
·
机器学习
·
早期疾病检测
·
生物医学信号处理
✓ 已验证
产品关联
DOI
高影响因子
FOOD RESEARCH INTERNATIONAL
·
2026
·
IF 8
Jingyi Han,Yao Li,Jiakang Liang,Lihong Chen,Xiaojie Yu,Li Chen,Otu Phyllis,Hafida Wahia,Hua Yang,Cunshan Zhou
This study aimed to optimize ultrasound-assisted enzymatic hydrolysis for umami peptide preparation from shiitake mushrooms, develop a reliable predictive screening model for umami peptides, and clarify the molecular mechanism underlying the umami taste of these peptides. Results indicated that ultrasonic pretreatment (200 W, 20 min) combined with dual-enzyme hydrolysis (complex protease and flavourzyme, at 1:1 ratio, 2400 U/g, 4 h) markedly enhanced hydrolysis efficiency. The α-amino nitrogen content reached 0.20 ± 0.00 mg/mL, with 98.31 % of the resulting peptides having molecular weights below 1 kDa. A triple-stream Umami-Transformer model was constructed by integrating peptide sequence, acid-base properties, and hydrophobicity. This model achieved an umami peptide prediction accuracy of 94 %, outperforming well-established models including Umami-BERT (90.5 %) and Umami-MRNN (93.2 %), thereby confirming its superior predictive performance. Four novel umami peptides (LPFQTE, MIPLLL, EVLPNL, and EVENVI) were successfully identified and validated. Molecular docking and molecular dynamics simulations revealed key binding sites (Asp147, Lys155, Ser217, Lys379, Asn150) on the T1R1 receptor and demonstrated that hydrogen bonding and ionic interactions are critical for stable binding and umami taste initiation. The findings provide a robust framework for the efficient production and identification of umami peptides from shiitake mushrooms, together with mechanistic insights that contribute to the development of novel flavor enhancers and functional foods.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
JOURNAL OF SEPARATION SCIENCE
·
2017
·Vol. 40
·期 24
·4805-4812
·
IF 2.557
Yifan Wang,Haiyan Li,Zhen Yang,Weijie Zhang,Jia Hua
To explore why the use of furfural as a transformer oil-paper insulation aging characteristic is problematic in real world application, we developed a method for the simultaneous determination of furfural, furoic acid, and maleic acid in transformer oil by reversed-phase vortex-assisted liquid–liquid microextraction combined with high-performance liquid chromatography. The conditions for the proposed method were optimized, and the obtained extract can be directly analyzed by high-performance liquid chromatography. The detection limits (signal-to-noise ratio = 3) of the method ranged from 1.0 to 4.6 μg/L, the enrichment factors for furfural, furoic acid, maleic acid, and fumaric acid were 4.6, 25.1, 15.6, and 17.5, respectively, and the recovery rates for three analytes (fumaric acid was undetected) range from 82.1 to 106.2%. The contents of furfural, furoic acid, and maleic acid resulted from accelerated aging of transformer insulation oil-paper were measured using the present method for the first time, and the aging samples were analyzed by liquid chromatography with mass spectrometry for the identification of furoic acid and maleic acid in the aging transformer oil samples. Using the optimal method, the target products of samples at different aging time were tracked and measured.
研究领域:
分析化学
·
电力工程
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）