Volatile organic compounds (VOCs) associated with lung cancer are key biomarkers for early noninvasive diagnosis, yet their adsorption behaviors on sensing materials remain highly complex and material-dependent. Efficient screening and accurate prediction of adsorption performance are therefore essential for designing next-generation gas sensors. Nanocomposites, with their superior surface reactivity and tunable properties, show great potential but lack a universal predictive framework that integrates computational simulations with intelligent algorithms. To overcome this limitation, this work constructs a comprehensive dataset of 336 adsorption cases and integrates first-principles calculations with machine learning to systematically predict VOC adsorption energies on nanocomposites. Eight algorithmsincluding SVR, GBR, GPR, XGBoost, MLP, KRR, and a small-sample Transformer modelwere benchmarked to identify the optimal predictive strategy. Among them, the KRR model achieved the best performance with an R2 of 0.8997 on the test set, exhibiting excellent generalization capability. This study provides the first comparative evaluation of deep learning and traditional ML methods for VOC adsorption prediction on nanocomposites based on first-principles data, revealing their respective strengths and limitations in gas-sensing research. The established universal predictive model offers a powerful tool for rapid screening of lung-cancer-related VOC biomarkers and lays a solid theoretical foundation for the rational design of high-performance gas sensors in medical diagnostics and health monitoring.
{"title":"Multiscale Analysis of Deep Learning and Machine Learning: New Insights into the Adsorption Mechanism of VOCs Gas-Sensitive Materials","authors":"Yujie Chen, Zexuan Wang, Xiao Wei, Wenhao Jiang, Yiyi Zhang, Xianfu Lin, Zengxi Wei, Pengfei Jia","doi":"10.1021/acssensors.5c03820","DOIUrl":"https://doi.org/10.1021/acssensors.5c03820","url":null,"abstract":"Volatile organic compounds (VOCs) associated with lung cancer are key biomarkers for early noninvasive diagnosis, yet their adsorption behaviors on sensing materials remain highly complex and material-dependent. Efficient screening and accurate prediction of adsorption performance are therefore essential for designing next-generation gas sensors. Nanocomposites, with their superior surface reactivity and tunable properties, show great potential but lack a universal predictive framework that integrates computational simulations with intelligent algorithms. To overcome this limitation, this work constructs a comprehensive dataset of 336 adsorption cases and integrates first-principles calculations with machine learning to systematically predict VOC adsorption energies on nanocomposites. Eight algorithmsincluding SVR, GBR, GPR, XGBoost, MLP, KRR, and a small-sample Transformer modelwere benchmarked to identify the optimal predictive strategy. Among them, the KRR model achieved the best performance with an <i>R</i><sup>2</sup> of 0.8997 on the test set, exhibiting excellent generalization capability. This study provides the first comparative evaluation of deep learning and traditional ML methods for VOC adsorption prediction on nanocomposites based on first-principles data, revealing their respective strengths and limitations in gas-sensing research. The established universal predictive model offers a powerful tool for rapid screening of lung-cancer-related VOC biomarkers and lays a solid theoretical foundation for the rational design of high-performance gas sensors in medical diagnostics and health monitoring.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"5 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146097875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1021/acssensors.5c03440
Xiaoqiu Zhong,Longxiang Zhu,Xin Zhang,Zhu-Bao Shao,Jianhui Qiu,Yu-Zhong Wang
Integrating high conductivity, stretchability, and mechanical and electrical performance in fibers remains challenging for wearables. This study develops highly stretchable, recyclable composite conductive fibers with exceptional electromechanical stability. Fibers were fabricated via wet-spinning by uniformly dispersing liquid metal particles (LMPs) and carboxylated carbon nanotubes (CNT-COOH) within a polyurethane matrix, forming an initial LMP-CNTNet island-bridge network. Subsequent ultrasound activation induced the assembly of a continuous LMP-dominated network (LMPNet), creating a hierarchical dual-network structure (LMPNet-CNTNet). This design achieves a conductivity of 3.22 × 103 S·m-1, a tensile strength of 6.6 MPa, strain-insensitive charge transport (ΔR < 1.3 Ω·cm-1 at 100% strain), and near-zero resistance drift (1.6% change over 2000 cycles). Programmatic modulation of the fiber spatial structure via ultrasonic activation enables the integration of high-power transmission, precision Joule heating, and real-time motion sensing. Moreover, the system enables closed-loop recycling via dissolution/respinning, retaining >80% original performance after five cycles. This work provides a sustainable and robust platform for next-generation multimodal smart textiles.
{"title":"Spatially Programmable Electromechanical Response Enabled by Designed Island-Bridge Conductive Fibers for Motion-Sensing Textiles.","authors":"Xiaoqiu Zhong,Longxiang Zhu,Xin Zhang,Zhu-Bao Shao,Jianhui Qiu,Yu-Zhong Wang","doi":"10.1021/acssensors.5c03440","DOIUrl":"https://doi.org/10.1021/acssensors.5c03440","url":null,"abstract":"Integrating high conductivity, stretchability, and mechanical and electrical performance in fibers remains challenging for wearables. This study develops highly stretchable, recyclable composite conductive fibers with exceptional electromechanical stability. Fibers were fabricated via wet-spinning by uniformly dispersing liquid metal particles (LMPs) and carboxylated carbon nanotubes (CNT-COOH) within a polyurethane matrix, forming an initial LMP-CNTNet island-bridge network. Subsequent ultrasound activation induced the assembly of a continuous LMP-dominated network (LMPNet), creating a hierarchical dual-network structure (LMPNet-CNTNet). This design achieves a conductivity of 3.22 × 103 S·m-1, a tensile strength of 6.6 MPa, strain-insensitive charge transport (ΔR < 1.3 Ω·cm-1 at 100% strain), and near-zero resistance drift (1.6% change over 2000 cycles). Programmatic modulation of the fiber spatial structure via ultrasonic activation enables the integration of high-power transmission, precision Joule heating, and real-time motion sensing. Moreover, the system enables closed-loop recycling via dissolution/respinning, retaining >80% original performance after five cycles. This work provides a sustainable and robust platform for next-generation multimodal smart textiles.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"5 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sweat, as a byproduct of human metabolism, can offer valuable insights for individual health monitoring and disease diagnosis. Traditional sweat detection devices face limitations including poor mechanical performance and low sensitivity. To overcome these challenges, we report a wearable surface-enhanced Raman scattering (SERS) sensor based on MXene-Au@Ag NPs core-shell structures integrated with a polyvinyl alcohol (PVA) hydrogel. The uniform deposition of Au@Ag NPs on MXene nanosheets generated high-density electromagnetic "hot spots," significantly enhancing SERS activity. The PVA hydrogel substrate not only endowed the sensor with excellent flexibility and mechanical stability but also facilitated efficient sweat collection and analyte enrichment. This sensor demonstrated ultrasensitive detection of creatinine (2.7 × 10-9 M) and uric acid (3.6 × 10-8 M), with strong linear correlations (R2 = 0.993 and 0.997), and could simultaneously monitor sweat pH. Practical trials with human volunteers confirmed the sensor's reliable, real-time quantification of biomarker concentrations and dynamic pH in sweat during exercise, validated using a portable Raman spectrometer. With its high uniformity (RSD = 7.02%), mechanical durability, and stable performance under repeated deformation, this wearable SERS sensor platform holds significant promise for point-of-care testing and continuous health monitoring.
{"title":"MXene-Au@Ag Hydrogel Patch as a Wearable SERS Sensor for Multiplexed Sweat Analysis of Uric Acid, Creatinine, and pH.","authors":"Yuliang Zhao,Yanqiu Zou,Weidan Zhao,Changqing Huang,Yadong Zhou,Gang Li,Ming Li,Shangzhong Jin,Li Jiang","doi":"10.1021/acssensors.5c03960","DOIUrl":"https://doi.org/10.1021/acssensors.5c03960","url":null,"abstract":"Sweat, as a byproduct of human metabolism, can offer valuable insights for individual health monitoring and disease diagnosis. Traditional sweat detection devices face limitations including poor mechanical performance and low sensitivity. To overcome these challenges, we report a wearable surface-enhanced Raman scattering (SERS) sensor based on MXene-Au@Ag NPs core-shell structures integrated with a polyvinyl alcohol (PVA) hydrogel. The uniform deposition of Au@Ag NPs on MXene nanosheets generated high-density electromagnetic \"hot spots,\" significantly enhancing SERS activity. The PVA hydrogel substrate not only endowed the sensor with excellent flexibility and mechanical stability but also facilitated efficient sweat collection and analyte enrichment. This sensor demonstrated ultrasensitive detection of creatinine (2.7 × 10-9 M) and uric acid (3.6 × 10-8 M), with strong linear correlations (R2 = 0.993 and 0.997), and could simultaneously monitor sweat pH. Practical trials with human volunteers confirmed the sensor's reliable, real-time quantification of biomarker concentrations and dynamic pH in sweat during exercise, validated using a portable Raman spectrometer. With its high uniformity (RSD = 7.02%), mechanical durability, and stable performance under repeated deformation, this wearable SERS sensor platform holds significant promise for point-of-care testing and continuous health monitoring.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"34 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Light-addressable photoelectrochemical sensors (LAPECS) have gained increasing attention as a promising platform for multiplexed detection owing to their integration of photo-addressing and photoelectrochemical techniques. By using programmable illumination to confine photoinduced electron transfer to specific electrode regions, this straightforward strategy enables high-throughput and multiplexed detection and has shown promise in biomedical diagnostics, environmental monitoring, and food safety analysis. In this review, we provide a comprehensive overview of the fundamental principles and the structural and functional components of LAPECS. This review also summarizes three representative operational modes: multi-electrode parallel, single-electrode multi-channel partitioning, and microarray chip-based modes. We further discuss emerging solutions, including advanced recognition interfaces, miniaturized designs, and machine learning-assisted data processing, to address current challenges in LAPECS, such as limited specificity, signal interference, and integration complexity. Finally, the current research challenges and future prospects of LAPECS are discussed.
{"title":"Light-Addressable Photoelectrochemical Sensors for High-Throughput and Multiplex Detection: Principles, Applications, and Future Perspectives.","authors":"Yukun Yang,Fuguo Ge,Xiangyu Yao,Tao Bo,Jinhua Zhang,Xu Jing,Huilin Liu,Ying Zhang,Wenyan Yan,Baoqing Bai","doi":"10.1021/acssensors.5c03353","DOIUrl":"https://doi.org/10.1021/acssensors.5c03353","url":null,"abstract":"Light-addressable photoelectrochemical sensors (LAPECS) have gained increasing attention as a promising platform for multiplexed detection owing to their integration of photo-addressing and photoelectrochemical techniques. By using programmable illumination to confine photoinduced electron transfer to specific electrode regions, this straightforward strategy enables high-throughput and multiplexed detection and has shown promise in biomedical diagnostics, environmental monitoring, and food safety analysis. In this review, we provide a comprehensive overview of the fundamental principles and the structural and functional components of LAPECS. This review also summarizes three representative operational modes: multi-electrode parallel, single-electrode multi-channel partitioning, and microarray chip-based modes. We further discuss emerging solutions, including advanced recognition interfaces, miniaturized designs, and machine learning-assisted data processing, to address current challenges in LAPECS, such as limited specificity, signal interference, and integration complexity. Finally, the current research challenges and future prospects of LAPECS are discussed.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"81 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-29DOI: 10.1021/acssensors.6c00012
<styled-content style-type="dropcap">T</styled-content>he rapid proliferation of point-of-care diagnostics, wearable sensors, and continuous monitoring devices has revolutionized healthcare and environmental surveillance. However, advances in sensing technology come with an often-overlooked environmental cost. (1) Single-use plastic test strips, energy-intensive fabrication methods, and non-recyclable electronic components contribute significantly to global waste streams and carbon footprints. As the field of sensor technology expands, so too does our responsibility to address its ecological impact. <i>ACS Sensors</i>, as a leading journal at the forefront of diagnostic innovation, is uniquely positioned to promote the transition toward sustainable and green sensor technologies. This editorial will catalyze a critical dialogue and interdisciplinary collaboration on designing sensors or measurement systems that are not only sensitive and selective but also environmentally responsible, from material sourcing and fabrication to deployment and end-of-life management. The foundation of sustainable sensing lies in biodegradable substrates like cellulose, chitosan, and silk; (2) recyclable conductors like liquid metals and carbon-based inks; (3) and bio-sourced recognition elements. (4) Additionally, moving from energy-intensive cleanrooms to solar-powered 3D printing and roll-to-roll manufacturing using water-based inks or solvent-free processing can dramatically reduce the carbon footprint of sensor production, enabling scalable green fabrication. (5,6) To eliminate battery waste, next-generation sensors are expected to harvest energy from their environment by using triboelectric nanogenerators for wearables, biofuel cells for implants and environment monitors, or passive and Radio-frequency identification-enabled low-power systems. These green power systems enable the truly autonomous and sustainable sensing systems. (7) Beyond that, sustainability requires to sensor design with their entire lifecycle in mind. This includes creating modular sensors that can be easily taken apart, planning for what happens when they’re no longer used (like composting or recycling), and carefully evaluating their environmental impact from production to disposal to reduce harm as much as possible. (8,9) Green sensors also need to be accessible and capable of performing non-invasive detection, such as in saliva. Using locally sourced materials, solar-powered readouts, and community-based recycling models ensures sustainable diagnostics reach low-resource settings without compromising performance or planetary health. (10) Accelerating adoption requires clear standards for green sensor certification, regulatory pathways that incentivize sustainable design, and strong industry partnerships to translate lab innovations into commercially viable, planet-positive products. This Editorial supports the journal’s commitment to publishing high-impact, transformative sensor research
{"title":"Sustainable Sensing Technologies toward a Greener Future","authors":"","doi":"10.1021/acssensors.6c00012","DOIUrl":"https://doi.org/10.1021/acssensors.6c00012","url":null,"abstract":"<styled-content style-type=\"dropcap\">T</styled-content>he rapid proliferation of point-of-care diagnostics, wearable sensors, and continuous monitoring devices has revolutionized healthcare and environmental surveillance. However, advances in sensing technology come with an often-overlooked environmental cost. (1) Single-use plastic test strips, energy-intensive fabrication methods, and non-recyclable electronic components contribute significantly to global waste streams and carbon footprints. As the field of sensor technology expands, so too does our responsibility to address its ecological impact. <i>ACS Sensors</i>, as a leading journal at the forefront of diagnostic innovation, is uniquely positioned to promote the transition toward sustainable and green sensor technologies. This editorial will catalyze a critical dialogue and interdisciplinary collaboration on designing sensors or measurement systems that are not only sensitive and selective but also environmentally responsible, from material sourcing and fabrication to deployment and end-of-life management. The foundation of sustainable sensing lies in biodegradable substrates like cellulose, chitosan, and silk; (2) recyclable conductors like liquid metals and carbon-based inks; (3) and bio-sourced recognition elements. (4) Additionally, moving from energy-intensive cleanrooms to solar-powered 3D printing and roll-to-roll manufacturing using water-based inks or solvent-free processing can dramatically reduce the carbon footprint of sensor production, enabling scalable green fabrication. (5,6) To eliminate battery waste, next-generation sensors are expected to harvest energy from their environment by using triboelectric nanogenerators for wearables, biofuel cells for implants and environment monitors, or passive and Radio-frequency identification-enabled low-power systems. These green power systems enable the truly autonomous and sustainable sensing systems. (7) Beyond that, sustainability requires to sensor design with their entire lifecycle in mind. This includes creating modular sensors that can be easily taken apart, planning for what happens when they’re no longer used (like composting or recycling), and carefully evaluating their environmental impact from production to disposal to reduce harm as much as possible. (8,9) Green sensors also need to be accessible and capable of performing non-invasive detection, such as in saliva. Using locally sourced materials, solar-powered readouts, and community-based recycling models ensures sustainable diagnostics reach low-resource settings without compromising performance or planetary health. (10) Accelerating adoption requires clear standards for green sensor certification, regulatory pathways that incentivize sustainable design, and strong industry partnerships to translate lab innovations into commercially viable, planet-positive products. This Editorial supports the journal’s commitment to publishing high-impact, transformative sensor research","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"388 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-29DOI: 10.1021/acssensors.5c02891
Lin-Min Zhong,Chun-Min Li,Jing Zhang,Yi-Xin Li,Jiayi Zhuang,Xin-Yi Zhong,Fen-Ying Kong,Shan-Wen Hu
The efficacy of multiple single nucleotide variants (SNVs) analysis is far from ideal due to the limitations in identification. This study introduced a novel strategy for multiple SNVs analysis at the single particle level, integrating molecular and nanomaterial confinement to significantly accelerate the kinetics of multiplex recognition processes. Leveraging DNA tetrahedra to enhance sample background tolerance, we developed a nano self-assembly approach for the microscopic visualization and single-particle detection of mutations. The incorporation of X-shaped probes on DNA tetrahedra formed high-stability recognition units, which were interconnected via a long-chain confinement mechanism. Upon recognition, the release of the X-probe loop triggered a hybridization chain reaction (HCR) cascade, confined to the surface of gold nanoparticles (AuNPs) to achieve secondary confinement acceleration. Following electrostatic adsorption onto polystyrene (PS) microspheres, the fluorescence signal on AuNPs became microscopically visible. Machine learning algorithms were employed to further enhance the effective discrimination of multiple genomic sites. This work presents a promising and practical approach for multiple SNVs detection with potential applications in genomics and precision medicine.
{"title":"Dual Confinement-Enhanced Multiple Single Nucleotide Variant Detection at the Single-Particle Level.","authors":"Lin-Min Zhong,Chun-Min Li,Jing Zhang,Yi-Xin Li,Jiayi Zhuang,Xin-Yi Zhong,Fen-Ying Kong,Shan-Wen Hu","doi":"10.1021/acssensors.5c02891","DOIUrl":"https://doi.org/10.1021/acssensors.5c02891","url":null,"abstract":"The efficacy of multiple single nucleotide variants (SNVs) analysis is far from ideal due to the limitations in identification. This study introduced a novel strategy for multiple SNVs analysis at the single particle level, integrating molecular and nanomaterial confinement to significantly accelerate the kinetics of multiplex recognition processes. Leveraging DNA tetrahedra to enhance sample background tolerance, we developed a nano self-assembly approach for the microscopic visualization and single-particle detection of mutations. The incorporation of X-shaped probes on DNA tetrahedra formed high-stability recognition units, which were interconnected via a long-chain confinement mechanism. Upon recognition, the release of the X-probe loop triggered a hybridization chain reaction (HCR) cascade, confined to the surface of gold nanoparticles (AuNPs) to achieve secondary confinement acceleration. Following electrostatic adsorption onto polystyrene (PS) microspheres, the fluorescence signal on AuNPs became microscopically visible. Machine learning algorithms were employed to further enhance the effective discrimination of multiple genomic sites. This work presents a promising and practical approach for multiple SNVs detection with potential applications in genomics and precision medicine.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"42 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydrogel flexible optical fibers (HFOFs) have great potential in environmental monitoring and biomedical applications. However, current HFOFs face significant challenges, including the presence of large amounts of free water and low fiber crosslinking density, which result in poor water retention and environmental tolerance. These issues hinder the stable use of HFOFs as sensing waveguides. This study proposes for the first time a high-water-retention, fine-core HFOF. HFOFs are spun from a polyacrylamide hydrogel material containing glycerol using the drawing spinning method. During the drawing and stretching process, the fiber network porosity decreases, and self-assembly induced by water evaporation generates numerous hydrogen bonds. The hydroxyl groups in the glycerol molecules form hydrogen bonds with water molecules, while a large amount of bound water is generated, thereby enhancing the water retention performance of the HFOFs. HFOF demonstrates excellent water retention (water loss rate: <0.1%/day), stretching performance (500%), light-guiding ability (1.45 dB/cm), and recovery properties (1 min). The highly water-retentive HFOFs are applied in various scenarios, including bionic sensors for vibration detection and flexible sensors for monitoring human respiration and heartbeat. This work offers a new solution for improving the stable use of HFOFs and provides new inspiration for bionic monitoring and wearable human sensors.
{"title":"Fine-Core Highly Water-Retentive Hydrogel Flexible Optical Fiber for Long-Term Stable Sensing Application.","authors":"Chunbiao Liu,Zhihai Liu,Yu Zhang,Yifan Qin,Mengyao Zhang,Wei Jin,Wenxuan Gu,Chen Shi,Libo Yuan","doi":"10.1021/acssensors.5c03604","DOIUrl":"https://doi.org/10.1021/acssensors.5c03604","url":null,"abstract":"Hydrogel flexible optical fibers (HFOFs) have great potential in environmental monitoring and biomedical applications. However, current HFOFs face significant challenges, including the presence of large amounts of free water and low fiber crosslinking density, which result in poor water retention and environmental tolerance. These issues hinder the stable use of HFOFs as sensing waveguides. This study proposes for the first time a high-water-retention, fine-core HFOF. HFOFs are spun from a polyacrylamide hydrogel material containing glycerol using the drawing spinning method. During the drawing and stretching process, the fiber network porosity decreases, and self-assembly induced by water evaporation generates numerous hydrogen bonds. The hydroxyl groups in the glycerol molecules form hydrogen bonds with water molecules, while a large amount of bound water is generated, thereby enhancing the water retention performance of the HFOFs. HFOF demonstrates excellent water retention (water loss rate: <0.1%/day), stretching performance (500%), light-guiding ability (1.45 dB/cm), and recovery properties (1 min). The highly water-retentive HFOFs are applied in various scenarios, including bionic sensors for vibration detection and flexible sensors for monitoring human respiration and heartbeat. This work offers a new solution for improving the stable use of HFOFs and provides new inspiration for bionic monitoring and wearable human sensors.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"35 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Norepinephrine (NE), a key neurotransmitter, plays a crucial role in Parkinson's disease (PD). However, precise tracking NE dynamics during PD pathology remains a significant challenge. Herein, we develop a novel near-infrared chemiluminescent probe (CL-NE) that can visualize the dynamic changes of NE levels in brains experiencing PD. Benefiting from its excellent sensitivity and selectivity, the CL-NE probe is applied to monitor NE dynamics in neuronal cells. Notably, CL-NE successfully observed aberrantly expressed NE in MPTP-induced Parkinson's mouse brains. Overall, our study provides a powerful tool for in vivo NE monitoring, offering valuable insights to improve the pathogenesis, diagnosis, and therapy of PD.
{"title":"A Near-Infrared Chemiluminescent Probe for Visualizing Norepinephrine in Parkinson's Disease.","authors":"Zhuang Lv,Jiayi Li,Pei Zhang,Zhennan Zhang,Hualong Zhou,Jiamei Liu,Yunsheng Xue,Ling Zhang","doi":"10.1021/acssensors.5c03087","DOIUrl":"https://doi.org/10.1021/acssensors.5c03087","url":null,"abstract":"Norepinephrine (NE), a key neurotransmitter, plays a crucial role in Parkinson's disease (PD). However, precise tracking NE dynamics during PD pathology remains a significant challenge. Herein, we develop a novel near-infrared chemiluminescent probe (CL-NE) that can visualize the dynamic changes of NE levels in brains experiencing PD. Benefiting from its excellent sensitivity and selectivity, the CL-NE probe is applied to monitor NE dynamics in neuronal cells. Notably, CL-NE successfully observed aberrantly expressed NE in MPTP-induced Parkinson's mouse brains. Overall, our study provides a powerful tool for in vivo NE monitoring, offering valuable insights to improve the pathogenesis, diagnosis, and therapy of PD.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"74 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We electrochemically synthesized molecularly imprinted polymer (MIP) films and simultaneously deposited them onto an interdigitated electrode array (IDEA) and a classical Pt disk electrode to devise chemosensors for the selective determination of gamma-aminobutyric acid (GABA), a biomarker of autism spectrum disorder. p-Bis(2,2'-bithien-5-yl)methyl phenol 2-hydroxy acetamide ether was used as the functional monomer due to its ability to form a stable prepolymerization complex with the GABA template in solution. The highest stability of the prepolymerization complex of GABA with different functional monomers directed the choice of the above functional monomer. The structures of these complexes were optimized using DFT calculations. Potentiodynamic electropolymerization was performed to deposit prepolymerization template and functional monomer complexes on different electrodes. After removing template molecules to generate selective molecular cavities in the resulting MIPs, we evaluated the analytical performance of these MIP films when integrated into electrochemical sensing platforms. We integrated differential pulse voltammetry (DPV) or electrochemical impedance spectroscopy (EIS) transductions with the MIP film-coated electrodes and identified EIS as the most effective for point-of-care GABA determinations. Using EIS, an MIP-film-coated platinum disk electrode detected GABA in a linear dynamic concentration range of 0.19-1.6 μM, with a limit of detection (LOD) of 0.13 μM. The MIP film deposited on the IDEA enabled GABA determination with EIS over a broader range of 8-240 μM, with an LOD of 0.39 μM, highlighting its potential for clinical applications. The EIS-determined imprinting factor was 2.7. The chemosensors were selective with respect to structural analogues of GABA. Finally, we successfully measured GABA concentrations in human serum samples, confirming the clinical applicability of the developed GABA determination method.
{"title":"Chemosensing of an Autism Biomarker, Gamma-Aminobutyric Acid, by Electropolymerized Molecularly Imprinted Polymers.","authors":"Nabila Yasmeen,Piyush Sindhu Sharma,Joanna Piechowska,Wojciech Lisowski,Krzysztof Noworyta,Francis D'Souza,Wlodzimierz Kutner","doi":"10.1021/acssensors.5c02424","DOIUrl":"https://doi.org/10.1021/acssensors.5c02424","url":null,"abstract":"We electrochemically synthesized molecularly imprinted polymer (MIP) films and simultaneously deposited them onto an interdigitated electrode array (IDEA) and a classical Pt disk electrode to devise chemosensors for the selective determination of gamma-aminobutyric acid (GABA), a biomarker of autism spectrum disorder. p-Bis(2,2'-bithien-5-yl)methyl phenol 2-hydroxy acetamide ether was used as the functional monomer due to its ability to form a stable prepolymerization complex with the GABA template in solution. The highest stability of the prepolymerization complex of GABA with different functional monomers directed the choice of the above functional monomer. The structures of these complexes were optimized using DFT calculations. Potentiodynamic electropolymerization was performed to deposit prepolymerization template and functional monomer complexes on different electrodes. After removing template molecules to generate selective molecular cavities in the resulting MIPs, we evaluated the analytical performance of these MIP films when integrated into electrochemical sensing platforms. We integrated differential pulse voltammetry (DPV) or electrochemical impedance spectroscopy (EIS) transductions with the MIP film-coated electrodes and identified EIS as the most effective for point-of-care GABA determinations. Using EIS, an MIP-film-coated platinum disk electrode detected GABA in a linear dynamic concentration range of 0.19-1.6 μM, with a limit of detection (LOD) of 0.13 μM. The MIP film deposited on the IDEA enabled GABA determination with EIS over a broader range of 8-240 μM, with an LOD of 0.39 μM, highlighting its potential for clinical applications. The EIS-determined imprinting factor was 2.7. The chemosensors were selective with respect to structural analogues of GABA. Finally, we successfully measured GABA concentrations in human serum samples, confirming the clinical applicability of the developed GABA determination method.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"117 1","pages":"XXX"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1021/acssensors.5c03331
Yang Cheng,Xinyuan Bi,Bo Liu,Zhou Chen,Linley Li Lin,Yuling Wang,Jiahua Pan,Jian Ye
Prostate cancer (PCa) remains a major global health burden, yet current screening tools often lead to overdiagnosis due to low specificity, highlighting the urgent need for more precise diagnostic approaches. Prostatic fluid (PSF) represents a promising but underexplored biofluid with exceptional diagnostic potential due to its direct contact with the PCa microenvironment. Here, we employed molecule-level interpretable surface-enhanced Raman spectroscopy (SERS) to comprehensively investigate PCa-associated alterations in two PSF components including metabolites and small extracellular vesicles (sEVs) and explored their potential interrelations via correlation analysis. Through molecule-resolvable SERS spectral set (MORE SERSome) technique, we identified ergothioneine and deoxyguanosine as differential metabolites between PCa and benign prostatic hyperplasia patients. We further constructed a fusion diagnostic model by integrating metabolites and sEVs information. The fusion model significantly outperformed the diagnostic accuracy by applying any single component, suggesting diagnostic complementarity between PSF metabolites and sEVs. Integration with clinical variables such as age and plasma prostate-specific antigen concentration further enhanced performance with the area under the curve as high as 0.93 for PCa diagnosis, substantially surpassing existing screening methods. These findings strengthen the importance of in-depth analysis of specific PSF components and further promise the potential of SERS-based PSF profiling as a noninvasive strategy for PCa diagnosis and biopsy guidance.
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