Nano Energy
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2024
·
IF 16.8
Hao Zhang,Dongzhi Zhang,Ruiyuan Mao,Lina Zhou,Chunqing Yang,Yan Wu,Yukun Liu,Yuncheng Ji
Self-powered sensing technology and smart perception technology have broad application prospects in flexible and wearable electronics . In this work, a flexible triboelectric nanogenerator (RMP-TENG) based on a room-temperature vulcanized silicone rubber (RTV)@(Molybdenum disulfide (MoS 2 )/Polyvinyl chloride (PVC)) functional layer is developed using a layer-by-layer self-assembly and material doping strategy. The RTV@(MoS 2 /PVC) functional layer is divided into a charge-generating layer (RTV/PVC) and a charge-trapping layer (RTV/MoS 2 ). The synergistic effect of PVC and MoS 2 has improved the surface roughness and charge transfer efficiency of RMP-TENG, doubling its output performance to 1055 V and 112 μA. In order to further improve the tactile sensing accuracy of RMP-TENG, a deep learning model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is developed. It predicts the type of contact material based on the features of tactile signals, achieving a prediction accuracy of 93.975%. Additionally, by combining mobile smart terminals, the CNN-GRU model, and RMP-TENG, a wireless access control system based on self-powered tactile sensing and intelligent material recognition is developed. Through the optimization of these experiments and algorithms, RMP-TENG has achieved real-time material recognition capabilities. This demonstrates the broad application prospects of RMP-TENG in wearable energy supply, intelligent sensing, human-computer interaction, and other areas.
研究领域:
材料科学
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人工智能
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柔性电子学
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RSC Advances
·
2026
·Vol. 16
·期 16
·14159-14169
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IF 6.1
Qiujiang Wei,Fuhuan Chu,Chunyan Tian,Hua Ke,Xiya Wang,Xiaomin Chen,Linhui Qiang,Shiqi Xu
Although flexible hydrogel sensors hold great promise in health monitoring, their reliability in long-term wearable scenarios is severely compromised by intrinsic hydrophilicity-induced uncontrollable swelling and signal drift. To address this critical bottleneck, we developed an MXene-poly(acrylic acid) (PAAc)-gelatin (MPG) composite hydrogel featuring superior anti-swelling properties. By incorporating Zr4+ coordination crosslinking, a robust “covalent-physical-coordination” triple network was constructed. This design not only endows the hydrogel with excellent mechanical toughness but also significantly restricts water uptake, achieving an ultralow 30-day cumulative swelling degree of 1.74%, thereby ensuring structural stability in humid environments. Based on this advanced material, a smart insole sensing system was fabricated. Benefiting from the high signal-to-noise-ratio and temporal stability of the MPG sensors, the proposed CNN-LSTM hybrid deep-learning model effectively extracts spatiotemporal features from complex gait signals. The system achieves a gait phase recognition accuracy of 88.0%, significantly outperforming conventional machine learning algorithms. This work demonstrates that the integration of high-performance anti-swelling materials with advanced deep learning algorithms represents an effective strategy for achieving next-generation precision wearable diagnostics.
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SENSORS AND ACTUATORS B-CHEMICAL
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2025
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IF 7.7
Youwei Wang,Hehu Zhang,Enbo Nian,Weili Wang,Jiayi Qiao,Haoyang Yu,Lingling Lu,Ying Chen,Ang Wu,Chuanan Yao,Xiushan Wang
To address the issues of significant subjective errors and high susceptibility to interference from environmental factors and application scenarios in traditional colorimetry, and digital colorimetry for detecting pH and free chlorine. The present study innovatively proposes a detection method. This integrating digital image colorimetry with convolutional neural network (CNN). A portable, accurate, and efficient multi-angle rapid detection platform was developed, and the visualization of the detection process and results was realized via an APP with a built-in CNN model. Compared with the traditional fixed-angle detection method, multi-angle rotation detection effectively reduces the maximum detection error. For water sample detection within pH range of 6–9 and a free chlorine range of 0–5 ppm, the maximum detection error was 0.06 for pH and 0.09 ppm for free chlorine, with a response time of 1.2 s for both. The regression coefficients reached 0.9989 and 0.9987, respectively. With low detection errors, this method enables the rapid and accurate detection of water pH value and free chlorine content. This study provides new insights and a theoretical basis for the design and development of water quality detection equipment, and offers guiding significance for promoting technological upgrading and equipment innovation in the field of water quality detection.
研究领域:
环境科学
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分析化学
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传感器与检测技术
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检测中的机器学习
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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
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生物标志物研究
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CONSTRUCTION AND BUILDING MATERIALS
·
2026
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IF 8.9
Xiang Tian,Yan Zhan,Jie Wu,Shanchang Yi,Tianyong Jiang,Lei Wang
Silane coupling agents (SCAs) are widely recognized for improving the mechanical performance and crack resistance of geopolymers. However, previous studies have primarily focused on strength enhancement, while the influence of SCAs on crack mode evolution and micro-damage development remains poorly quantified. This study investigates the fracture regulation and damage evolution of methyltrimethoxysilane (MTMS)-modified metakaolin-based geopolymers through an integrated analysis of mechanical behavior, acoustic emission (AE), digital image correlation (DIC), and convolutional neural networks (CNNs). MTMS was incorporated at dosages ranging from 0 to 0.4 wt%, and CNN-based crack mode identification model was developed using a balanced AE dataset containing 1200 tensile and shear cracking signals. The results show that MTMS regulates both strength and fracture behavior in a dosage-dependent manner. The optimum strength enhancement was achieved at 0.2 wt% MTMS, increasing compressive and flexural strengths by 22.9% and 30.8%, respectively, while 0.3 wt% MTMS produced the highest peak displacement and longest time to peak load. AE analysis revealed a transition from instantaneous brittle damage to progressive energy-dissipating fracture with increasing MTMS dosage, whereas excessive MTMS induced defect-related brittleness recovery. The 2D-CNN achieved a validation accuracy of 96.67%, enabling reliable quantification of tensile and shear cracking evolution. CNN classification showed that 0.2 wt% MTMS promoted mixed-mode fracture by reducing tensile crack dominance and increasing shear-related damage activity. Combined DIC-AE analysis identified three fracture stages, including microcrack initiation, stable crack propagation, and unstable fracture. This study provides an intelligent framework for quantitatively linking SCA-induced fracture regulation with micro-damage evolution in geopolymers.
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FOOD CHEMISTRY
·
2023
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IF 8.8
Huanhuan Li,Xiaofeng Luo,Suleiman A. Haruna,Muhammad Zareef,Quansheng Chen,Zhen Ding,Yiyong Yan
Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu 2 O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference ( P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea.
研究领域:
分析化学
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食品科学
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机器学习
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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
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2011
·Vol. 12
·期 06
·769-781
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IF 0.215
ÁKOS ZARÁNDY,CSABA REKECZKY,PÉTER FÖLDESY,ISTVÁN SZATMÁRI
The first CNN technology-based, high performance industrial visual computer called Aladdin is reported. The revolutionary device is the world premier of the ACE4k Cellular Visual Microprocessor (CVM) chip powering an industrial visual computer. One of the most important features of the Aladdin system is the image processing library. The library reduces algorithm development time, provides efficient codes, error free operation in binary, and accurate operation in grayscale nodes. Moreover the library provides an easy way to use the Aladdin system for those who are not familiar with the CNN technology.
研究领域:
计算机视觉
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工业计算
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嵌入式系统
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图像处理
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人工智能
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Journal of Materials Chemistry B
·
2019
·Vol. 7
·期 43
·6822-6827
·
IF 5.047
Fanpeng Kong,Ying Li,Chao Yang,Xiao Li,Junlin Wu,Xiaojun Liu,Xiaonan Gao,Kehua Xu,Bo Tang
Identifying cancer at the cellular level during an early stage offers the hope of greatly improved outcomes for cancer patients. As potential cancer biomarkers, nitroreductase (NTR) and human quinine oxidoreductase 1 (hNQO1) are overexpressed in many type of cancer cells. Simultaneous detection of these two biomarkers would benefit diagnostic precision in related cancers without yielding false positive results. Herein, based on a dye generated in situ strategy, a dual-enzyme-responsive probe, CNN, was rationally designed and synthesized by installing p-nitrobenzene and trimethyl-locked quinone propionic acid groups, which are specific for NTR and hNQO1, respectively, into a single fluorophore. This probe is only activated in the presence of both NTR and hNQO1 and produces a large fluorescence response, enabling the detection of both endogenous NTR and hNQO1 activity in living cells. The imaging results indicate that the CNN probe differentiates cancer cells (HeLa, MDA-MB-231 and HepG2 cells) from normal liver HL-7702 cells owing to the existence of relatively high endogenous levels of both biomarkers in these cancer cells.
研究领域:
癌症生物学
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生物化学
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分子生物学
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细胞生物学
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生物医学工程
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Journal of Environmental Chemical Engineering
·
2025
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IF 7.2
Zhenle Qin,Xiaohui Fang,Yang Li,Zhilin Feng,Xinping Zhang
Micro/nanoplastics particles (M/NPPs) have been increasingly detected in various organisms, including humans, raising growing concerns about their environmental and health impacts. This study presents a simple and effective capillary tube-based surface-enhanced Raman scattering (SERS) method for the rapid and ultra-sensitive direct detection of M/NPPs in water without drying. A high-performance SERS substrate was fabricated by uniformly modifying the capillary inner wall with a monolayer of silver nanoparticles (MAgNPs), creating densely distributed “hot spots”. Using rhodamine 6 G, the substrate achieved a detection limit of 10 −13 M with an enhancement factor of 5.81 × 10 10 . Capillary action drew polystyrene (PS) NPPs into the capillary tube, where flotation and enrichment at the tube top significantly improved detection efficiency. Both experiments and simulations confirmed this enrichment mechanism. The method detected 70 and 300 nm PS NPPs in lake water samples using spiking experiments at concentrations as low as 0.001 mg/mL. By combining SERS spectra with a convolutional neural network (CNN), mixed M/NPPs were accurately classified and identified, achieving 99.05 % recognition accuracy. This work offers a promising approach for rapid detection and precise identification of M/NPPs in environmental monitoring.
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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2026
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IF 4.8
Xin Li,Xinghua Yang,Rui Wang,Pingping Teng,Shuai Gao,Nan Lu,Xiaoyu Li,Adam Jones,Kang Li
Surface-enhanced Raman scattering (SERS) is a promising tool for sweat analysis, but signal noise, nonlinear responses, and spectral overlap limit its quantitative accuracy in complex samples. In this work, an intelligent SERS sensing strategy was developed by combining an Ag NP fractal substrate with a Gradient Kernel Size Convolutional Neural Network (GKS-CNN). The self-assembled Ag NP substrate provided abundant electromagnetic hotspots, enabling sensitive detection of lactate (LA) and glucose (Glu) from 10 −4 to 10 1 mM, with limits of detection of 108 nM and 224 nM, respectively. PCA results showed that the preprocessed SERS spectra contained distinguishable concentration-related information. Compared with linear regression, traditional machine learning methods, and standard CNNs, GKS-CNN achieved better prediction performance by extracting multi-scale features from nonlinear and overlapping spectra. For pure-component samples, the R 2 values of LA and Glu reached 0.99 and 0.98, respectively; for the joint dataset containing pure and mixed samples, the R 2 values were 0.98 and 0.97, respectively. In real sweat analysis, sample-level splitting was used to reduce data leakage, and the model classified four exercise-related states with an independent test accuracy of 95.00%. The average accuracies of 20 repeated random splits and 5-fold cross-validation were 94.50% and 93.50%, respectively. These results suggest that the proposed SERS-GKS-CNN strategy has potential for non-invasive sweat metabolite analysis and preliminary physiological-state recognition.
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OPTICS AND LASER TECHNOLOGY
·
2026
·
IF 5.2
Hao Chen,Min Li,Jichuan Gai,Jiyuan Wei,Zengshan Yu,Shan Guo,Zelong Li,Yanjun Wu,Xue Han,Kuihua Li,Guochao Shi
A double-antibody sandwich SERS immunoassay was developed for the highly specific capture of NSE in clinical serum. • The interpretable 1D-CNN model achieved an AUC of 0.9704, significantly outperforming DNN and RNN architectures. • SHAP and saliency maps deciphered the “black box”, identifying key Raman shifts at 607 and 1078 cm -1 as biomarkers. • A high-performance PSS@Cu-Ag@Ag SERS substrate with core-shell structure was fabricated using magnetron sputtering. • The periodic array provided dense “hot spots” with superior reproducibility, achieving RSD values below 6.4%. To address the issues of low imaging sensitivity in the clinical diagnosis of ischemic stroke (IS) and the cumbersome procedures associated with traditional Neuron-Specific Enolase (NSE) testing methods, this study proposes a rapid diagnostic approach which integrates Surface-enhanced Raman scattering (SERS) immunonanosensing technology with deep learning. Using magnetron sputtering, a periodic core–shell structured PSS@Cu-Ag@Ag active substrate was fabricated on a periodic microsphere array (PSS). The NSE biomarker in serum was specifically captured via a double-antibody sandwich immunoassay, and the spectra were subjected to deep feature extraction using a one-dimensional convolutional neural network (1D-CNN). Results showed that the model achieved an area under the curve average AUC of 0.9704 under five-fold cross-validation, and the SHapley Additive exPlanations (SHAP) interpreter successfully identified key differential Raman shift such as 607 cm −1 and 1078 cm −1 , enhancing the interpretability of the algorithm. This approach enables robust and transparent classification of clinically heterogeneous samples, providing a highly sensitive and intelligent medical solution with great potential for practical clinical implementation in the auxiliary diagnosis of IS. Download: Download high-res image (125KB) Download: Download full-size image
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CHEMICAL ENGINEERING JOURNAL
·
2024
·
IF 13.3
Yadan Luo,Chenguang Li,Zhen Liu,Wei Guo,Cuizhu Sun,Shasha Zhao,Qian Wang,Yuanyuan Li,Lingyun Chen,Hao Zheng,Fengmin Li
Phthalates (PAEs) are known to pose a serious threat to the environment and human health duo to their endocrine disrupting effects, thus a great deal of effort has been made to develop destruction technologies for PAEs treatment in the water environment, such as peroxymonosulfate (PMS)-based advanced oxidation technology. Here we prepared an environmentally friendly visible light catalyzer, a heterojunction of carbonitride nanosheets (CNN) doped with NH 2 -iron metal organic frameworks (NH 2 -MIL-101(Fe)), for the activation of PMS to efficaciously remove two representative PAEs—diisobutyl phthalate (DIBP) and diethyl phthalate (DEP)—from the aquatic environment. The CNN@NH 2 -MIL-101(Fe) photocatalyst possesses an excellent visible light harvesting performance with a narrow band gap (1.66 eV), low recombination ratio of photoinduced carriers, and long charge lifetime. The pore volume and surface area of the composite material was increased compared with any single component. The optimum CNN@NH 2 -MIL-101(Fe) catalyst manifested the high performance to activate PMS to degrade DIBP and DEP under visible irradiation with maximum removal of 94.5 ± 2.2 % and 83.4 ± 0.9 %, respectively. Four main active species (•OH, O 2 − •, SO 4 − •, and h + ) were involved in PAEs photodegradation, with h + and SO 4 − • playing a key role in the degradation of DIBP, and h + in the degradation of DEP. The degradation mechanism of DIBP and DEP mainly involved the ester bond cleavage and oxidation by active species. Additionally, the CNN@NH 2 -MIL-101(Fe) heterojunction exhibited high and stable catalytic performance to activate PMS for PAEs destruction under pH 3 – 9. Therefore, this study provides a new strategy for designing and selecting environment-friendly treatment approach for the effective eradication of PAEs via a PMS-based advanced oxidation process.
研究领域:
环境科学
·
材料科学
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危险品化学品经营许可证（带存储）