Pub Date : 2025-04-03DOI: 10.1021/acssensors.4c03131
Xiao Ma, Rong Tan, Hong Chen, Jiawei Zhang, Lei Ge, Tingting Zhao, Xinyu Wang, Kaikai Yuan, Hairui Fang, Dong Wang
Nitrogen dioxide (NO2), the main component of pollutants in atmospheric environments, causes and exacerbates respiratory diseases, especially during outdoor sports even at the 100 ppb level. Currently, environmental gas detection still faces challenges such as high detection limits, low SNR, and low sensitivity. A NO2 sensor based on In(OH)3-α-Fe2O3–ZnO was prepared using a hydrothermal method, featuring an ultralow detection limit of 82 ppt, an exceptionally high SNR of 574,000, and an ultrahigh sensitivity of 252.25 mV/ppm (100 ppb to 1 ppm). And this sensor exhibits excellent response, selectivity, and repeatability. These excellent sensing characteristics come from the adsorbed oxygen on the surface of the material with the formation of n–n heterojunctions. Additionally, a low-power, portable, and cost-effective environmental monitoring system (named Smart Breath Sentinel (SBS)) was designed to intelligently upgrade ordinary masks. SBS enables real-time wireless environmental gas monitoring with integrated humidity compensation to ensure accurate measurements in high-humidity environments. SBS has already been tested in multiple environments, and the test results have proven the feasibility of SBS. This sensor based on In(OH)3-α-Fe2O3–ZnO demonstrates significant potential for applications aimed at enhancing human safety.
{"title":"Smart Breath Sentinel: A NO2 Gas Sensor with ppt-Level Detection Lower Limit and High Signal-to-Noise Ratio Based on In(OH)3-α-Fe2O3–ZnO for an Application on Intelligent Upgrade of Ordinary Masks","authors":"Xiao Ma, Rong Tan, Hong Chen, Jiawei Zhang, Lei Ge, Tingting Zhao, Xinyu Wang, Kaikai Yuan, Hairui Fang, Dong Wang","doi":"10.1021/acssensors.4c03131","DOIUrl":"https://doi.org/10.1021/acssensors.4c03131","url":null,"abstract":"Nitrogen dioxide (NO<sub>2</sub>), the main component of pollutants in atmospheric environments, causes and exacerbates respiratory diseases, especially during outdoor sports even at the 100 ppb level. Currently, environmental gas detection still faces challenges such as high detection limits, low SNR, and low sensitivity. A NO<sub>2</sub> sensor based on In(OH)<sub>3</sub>-α-Fe<sub>2</sub>O<sub>3</sub>–ZnO was prepared using a hydrothermal method, featuring an ultralow detection limit of 82 ppt, an exceptionally high SNR of 574,000, and an ultrahigh sensitivity of 252.25 mV/ppm (100 ppb to 1 ppm). And this sensor exhibits excellent response, selectivity, and repeatability. These excellent sensing characteristics come from the adsorbed oxygen on the surface of the material with the formation of <i>n</i>–<i>n</i> heterojunctions. Additionally, a low-power, portable, and cost-effective environmental monitoring system (named Smart Breath Sentinel (SBS)) was designed to intelligently upgrade ordinary masks. SBS enables real-time wireless environmental gas monitoring with integrated humidity compensation to ensure accurate measurements in high-humidity environments. SBS has already been tested in multiple environments, and the test results have proven the feasibility of SBS. This sensor based on In(OH)<sub>3</sub>-α-Fe<sub>2</sub>O<sub>3</sub>–ZnO demonstrates significant potential for applications aimed at enhancing human safety.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"4 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766656","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 : 2025-04-03DOI: 10.1021/acssensors.5c00133
Yangyang Peng, Fengxin Sun, Ruru Pan
Wearable sensing textiles with multimodal mechanical stimulation detection capabilities have broad applications, such as sports guidance and rehabilitation training. However, current mainstream multimodal sensing textiles typically rely on combining discrete sensors with single functions intensively through sewing or adhesion to detect various mechanical stimuli. Herein, an all-in-one multiplexed sensing textile (MPST) capable of simultaneously detecting pressure and tensile strain is achieved by engineering an innovative hierarchical architecture of textiles. The functionality of MPST is directly derived from the reconfigurable sensing pathways of the woven meso-structures, enabling it to exhibit excellent pressure sensitivity (0.1 kPa–1), wide strain detection range (0–100%), superior durability, and desirable wearability. With the assistance of the Long Short-Term Memory (LSTM) algorithm, the MPST wearable system achieves a recognition accuracy of 97.5% in human kinematic postures of the elbow, knee, and foot. In addition, MPST demonstrates outstanding joule heating performance, which reaches 57.1 °C at a 2.5 V applied voltage. With its excellent multimodal sensing performance and joule heating ability, MPST holds great potential for applications in sports training guidance, assistive rehabilitation training, and soft robotics.
{"title":"Multiplexed Sensing Textiles Enabled by Reconfigurable Weaving Meso-Structures for Intricate Kinematic Posture Recognition and Thermal Therapy Healthcare","authors":"Yangyang Peng, Fengxin Sun, Ruru Pan","doi":"10.1021/acssensors.5c00133","DOIUrl":"https://doi.org/10.1021/acssensors.5c00133","url":null,"abstract":"Wearable sensing textiles with multimodal mechanical stimulation detection capabilities have broad applications, such as sports guidance and rehabilitation training. However, current mainstream multimodal sensing textiles typically rely on combining discrete sensors with single functions intensively through sewing or adhesion to detect various mechanical stimuli. Herein, an all-in-one multiplexed sensing textile (MPST) capable of simultaneously detecting pressure and tensile strain is achieved by engineering an innovative hierarchical architecture of textiles. The functionality of MPST is directly derived from the reconfigurable sensing pathways of the woven meso-structures, enabling it to exhibit excellent pressure sensitivity (0.1 kPa<sup>–1</sup>), wide strain detection range (0–100%), superior durability, and desirable wearability. With the assistance of the Long Short-Term Memory (LSTM) algorithm, the MPST wearable system achieves a recognition accuracy of 97.5% in human kinematic postures of the elbow, knee, and foot. In addition, MPST demonstrates outstanding joule heating performance, which reaches 57.1 °C at a 2.5 V applied voltage. With its excellent multimodal sensing performance and joule heating ability, MPST holds great potential for applications in sports training guidance, assistive rehabilitation training, and soft robotics.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"107 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766658","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}
The intrinsic elastic resilience, fatigue resistance, and self-adhesion of human skin are highly desired merits. However, they are challenging to combine into a single mechanoreceptive electronic skin for healthcare monitoring and humanoid soft robots. We introduce an elastically resilient, crack-tolerant, amphibiously adhesive, and strain-sensitive electronic skin (ERCAS-skin) featuring a hierarchical and gradient design. ERCAS-skin has a skin-like binary structure of a carbon nanotube-coated thermoplastic polyurethane nanofibrous scaffold embedded in a gradient cross-linking polydimethylsiloxane (PDMS) matrix. The binary structure endows ERCAS-skin with mechanical compliance (Young’s modulus of 2.4 MPa) and crack tolerance (fatigue threshold of 1285 J m–2) through a matrix-to-scaffold stress transfer. The gradient cross-linking PDMS ensures not only high elastic resilience (recovery of 95%) but also strong wet adhesion (0.76 N cm–1) through a synergistic hydrophobic chain mobility effect. The crack generation mechanism of the embedded carbon nanotube polyurethane enables high sensitivity and a wide strain-sensing range. Owing to its excellent strain-sensing capability, ERCAS-skin was utilized as a self-adhesive strain sensor for hand gesture recognition both in the air and under water and as a fatigue-free motion sensor for robotic fish monitoring.
{"title":"Skin-Mimicking Soft Strain Sensor with Elastic Resilience, Crack Tolerance, and Amphibious Self-Adhesion","authors":"Yunna Hao, Wei Ren, Qun Zhou, Bin Wang, Hongfang Liu, Peihua Zhang, Ranran Wang, Xiaohong Qin, Liming Wang, Yin Cheng","doi":"10.1021/acssensors.5c00555","DOIUrl":"https://doi.org/10.1021/acssensors.5c00555","url":null,"abstract":"The intrinsic elastic resilience, fatigue resistance, and self-adhesion of human skin are highly desired merits. However, they are challenging to combine into a single mechanoreceptive electronic skin for healthcare monitoring and humanoid soft robots. We introduce an elastically resilient, crack-tolerant, amphibiously adhesive, and strain-sensitive electronic skin (ERCAS-skin) featuring a hierarchical and gradient design. ERCAS-skin has a skin-like binary structure of a carbon nanotube-coated thermoplastic polyurethane nanofibrous scaffold embedded in a gradient cross-linking polydimethylsiloxane (PDMS) matrix. The binary structure endows ERCAS-skin with mechanical compliance (Young’s modulus of 2.4 MPa) and crack tolerance (fatigue threshold of 1285 J m<sup>–2</sup>) through a matrix-to-scaffold stress transfer. The gradient cross-linking PDMS ensures not only high elastic resilience (recovery of 95%) but also strong wet adhesion (0.76 N cm<sup>–1</sup>) through a synergistic hydrophobic chain mobility effect. The crack generation mechanism of the embedded carbon nanotube polyurethane enables high sensitivity and a wide strain-sensing range. Owing to its excellent strain-sensing capability, ERCAS-skin was utilized as a self-adhesive strain sensor for hand gesture recognition both in the air and under water and as a fatigue-free motion sensor for robotic fish monitoring.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"15 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766365","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 : 2025-04-03DOI: 10.1021/acssensors.5c00205
Yang Du, Hongbo Zhang, Jilong Zheng, Quanxin Li, Ruiqian Xu, Jingwen Xu, Yan-Yan Song, Pei Song, Zhida Gao, Chenxi Zhao
Exhaled breath is considered an important source of samples and a reservoir of biomarkers, especially for disease diagnosis. In this study, we developed an ultrasensitive point-of-care gas sensor for the analysis of hydrogen sulfide (H2S), which is a typical biomarker for periodontitis. A high-performance metal oxide semiconductor (MOS)-based chemiresistive H2S sensor was developed by integrating Fe-doped MoO3-x onto TiO2 nanotube arrays. The substitution of Fe atoms into MoO3-x not only induced oxygen vacancies, but also generated defect levels in the MoO3-x/TiO2 heterostructure, thus synergistically activating the gas sensing reaction at room temperature under ambient light. The resulting gas sensor exhibited ultrahigh sensitivity, fast response/recovery ability, and wide-range response to H2S concentrations up to 400 ppm. Furthermore, the sensing device maintained more than 95% of its original response at 70% relative humidity. With a subparts-per-billion limit of detection (the LOD for H2S was 0.34 ppb), the present sensor represents the most sensitive H2S chemiresistor reported to date for room-temperature, real-time monitoring of H2S concentration changes in the breath of healthy subjects, as well as for distinguishing breath samples of periodontitis patients and healthy individuals. This study utilizes the synergistic action of defects to provide an effective route for developing MOS-based ultrasensitive H2S sensors for periodontitis diagnosis.
{"title":"Integrating Vacancies and Defect Levels in Heterojunctions to Synergistically Enhance the Performance of H2S Chemiresistors for Periodontitis Diagnosis","authors":"Yang Du, Hongbo Zhang, Jilong Zheng, Quanxin Li, Ruiqian Xu, Jingwen Xu, Yan-Yan Song, Pei Song, Zhida Gao, Chenxi Zhao","doi":"10.1021/acssensors.5c00205","DOIUrl":"https://doi.org/10.1021/acssensors.5c00205","url":null,"abstract":"Exhaled breath is considered an important source of samples and a reservoir of biomarkers, especially for disease diagnosis. In this study, we developed an ultrasensitive point-of-care gas sensor for the analysis of hydrogen sulfide (H<sub>2</sub>S), which is a typical biomarker for periodontitis. A high-performance metal oxide semiconductor (MOS)-based chemiresistive H<sub>2</sub>S sensor was developed by integrating Fe-doped MoO<sub>3-x</sub> onto TiO<sub>2</sub> nanotube arrays. The substitution of Fe atoms into MoO<sub>3-x</sub> not only induced oxygen vacancies, but also generated defect levels in the MoO<sub>3-x</sub>/TiO<sub>2</sub> heterostructure, thus synergistically activating the gas sensing reaction at room temperature under ambient light. The resulting gas sensor exhibited ultrahigh sensitivity, fast response/recovery ability, and wide-range response to H<sub>2</sub>S concentrations up to 400 ppm. Furthermore, the sensing device maintained more than 95% of its original response at 70% relative humidity. With a subparts-per-billion limit of detection (the LOD for H<sub>2</sub>S was 0.34 ppb), the present sensor represents the most sensitive H<sub>2</sub>S chemiresistor reported to date for room-temperature, real-time monitoring of H<sub>2</sub>S concentration changes in the breath of healthy subjects, as well as for distinguishing breath samples of periodontitis patients and healthy individuals. This study utilizes the synergistic action of defects to provide an effective route for developing MOS-based ultrasensitive H<sub>2</sub>S sensors for periodontitis diagnosis.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"224 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766659","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 : 2025-04-02DOI: 10.1021/acssensors.5c00196
Xuelian Lyu, Tao Liang, Jilin Zheng, Chengwen He, Dongxin Xu, Haote Han, Ling Zou, Jiaru Fang, Ning Hu
Bradyarrhythmia is a major cause of cardiovascular disease morbidity and mortality. Currently, medication and/or surgery are the conventional clinical therapeutic strategies for bradyarrhythmia, whereas drug side effects, invasive surgery, or potential complications limit their extensive application. Therefore, the development of alternative therapies for bradyarrhythmia is urgently needed. Herein, we propose a universal and efficient drug-mimicking strategy to treat bradyarrhythmia, which relies on the photothermal properties of near-infrared-triggered indocyanine green (ICG). An in situ integrated cell-based biosensing-regulating platform was developed to assess treatment efficacy by dynamically analyzing the cardiomyocyte electrophysiology activities. These findings indicate that the thermal vibration of ICG can efficiently enhance the electrophysiology of cardiomyocytes with bradyarrhythmia and maintain a rhythmic state for a long time, which is superior to that of Au nanorod plasmonic localized heating. Moreover, qualitative investigations confirmed that thermal stimulation is a pivotal factor in enhancing cardiomyocyte electrophysiological activity during photothermal treatment. This study provides a noninvasive drug-mimicking treatment strategy for bradyarrhythmia and establishes a reliable cell-based biosensing-regulating platform for electrophysiological assessment and drug screening, contributing to the further development of bradyarrhythmia therapies.
{"title":"High-Efficiency ICG Molecular Vibration Therapy for Bradyarrhythmia Using Cardiomyocyte-Based Biosensing","authors":"Xuelian Lyu, Tao Liang, Jilin Zheng, Chengwen He, Dongxin Xu, Haote Han, Ling Zou, Jiaru Fang, Ning Hu","doi":"10.1021/acssensors.5c00196","DOIUrl":"https://doi.org/10.1021/acssensors.5c00196","url":null,"abstract":"Bradyarrhythmia is a major cause of cardiovascular disease morbidity and mortality. Currently, medication and/or surgery are the conventional clinical therapeutic strategies for bradyarrhythmia, whereas drug side effects, invasive surgery, or potential complications limit their extensive application. Therefore, the development of alternative therapies for bradyarrhythmia is urgently needed. Herein, we propose a universal and efficient drug-mimicking strategy to treat bradyarrhythmia, which relies on the photothermal properties of near-infrared-triggered indocyanine green (ICG). An <i>in situ</i> integrated cell-based biosensing-regulating platform was developed to assess treatment efficacy by dynamically analyzing the cardiomyocyte electrophysiology activities. These findings indicate that the thermal vibration of ICG can efficiently enhance the electrophysiology of cardiomyocytes with bradyarrhythmia and maintain a rhythmic state for a long time, which is superior to that of Au nanorod plasmonic localized heating. Moreover, qualitative investigations confirmed that thermal stimulation is a pivotal factor in enhancing cardiomyocyte electrophysiological activity during photothermal treatment. This study provides a noninvasive drug-mimicking treatment strategy for bradyarrhythmia and establishes a reliable cell-based biosensing-regulating platform for electrophysiological assessment and drug screening, contributing to the further development of bradyarrhythmia therapies.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"37 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766369","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 : 2025-04-02DOI: 10.1021/acssensors.4c02989
Huijuan Chen, Li Lv, Kaifeng Xue, Pinhua Zhang, Lulu Du, Guangliang Cui
Developing a portable and compact sensor for room temperature detection of H2S in exhaled breath for health assessment presents considerable technical challenges. This work successfully synthesized Cu2O/ZnO heterostructures with excellent gas sensitivity to H2S at room and lower temperatures using a two-dimensional (2D) electrodeposition in situ assembly method with the application of a semisine wave voltage as well as CuZnO nanoarrays deposited under direct current voltage. The Cu2O/ZnO heterostructure sensors, with high response of 8.53 × 104 to 1 ppm of H2S and a minimum detection limit of 10 ppb at room temperature, exhibit a response of 42 for 10 ppm of H2S even at −20 °C, and its response to 50 ppm of H2S is approximately 3774 times greater than that of the CuZnO sensor, which is a significant challenge to achieve with sensors based on oxygen adsorption/desorption mechanisms. These outstanding gas-sensing properties are attributed to the formation of p–n heterojunctions in the Cu2O/ZnO heterostructures and the occurrence of the sulfuration reaction. In addition, we successfully employed the Cu2O/ZnO sensor to detect H2S in human exhaled breath, offering valuable insights for the monitoring of various chronic diseases and new directions for the development of portable room-temperature breath sensors.
{"title":"Oral Exhalation H2S Sensor Based on Cu2O/ZnO Heterostructures","authors":"Huijuan Chen, Li Lv, Kaifeng Xue, Pinhua Zhang, Lulu Du, Guangliang Cui","doi":"10.1021/acssensors.4c02989","DOIUrl":"https://doi.org/10.1021/acssensors.4c02989","url":null,"abstract":"Developing a portable and compact sensor for room temperature detection of H<sub>2</sub>S in exhaled breath for health assessment presents considerable technical challenges. This work successfully synthesized Cu<sub>2</sub>O/ZnO heterostructures with excellent gas sensitivity to H<sub>2</sub>S at room and lower temperatures using a two-dimensional (2D) electrodeposition in situ assembly method with the application of a semisine wave voltage as well as CuZnO nanoarrays deposited under direct current voltage. The Cu<sub>2</sub>O/ZnO heterostructure sensors, with high response of 8.53 × 10<sup>4</sup> to 1 ppm of H<sub>2</sub>S and a minimum detection limit of 10 ppb at room temperature, exhibit a response of 42 for 10 ppm of H<sub>2</sub>S even at −20 °C, and its response to 50 ppm of H<sub>2</sub>S is approximately 3774 times greater than that of the CuZnO sensor, which is a significant challenge to achieve with sensors based on oxygen adsorption/desorption mechanisms. These outstanding gas-sensing properties are attributed to the formation of p–n heterojunctions in the Cu<sub>2</sub>O/ZnO heterostructures and the occurrence of the sulfuration reaction. In addition, we successfully employed the Cu<sub>2</sub>O/ZnO sensor to detect H<sub>2</sub>S in human exhaled breath, offering valuable insights for the monitoring of various chronic diseases and new directions for the development of portable room-temperature breath sensors.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"58 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758452","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}
Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standard, is invasive, costly and provides limited information about the tumor and its molecular landscape. Exosomes, as promising biomarkers for lung cancer, are a heterogeneous collection of membranous vesicles containing tumor-specific information for liquid biopsy to identify lung cancer subtypes. However, the small size, complex structure, and heterogeneous molecular features of exosomes pose significant challenges for their effective isolation and analysis. Herein, we report a deep learning-driven microfluidic chip with surface-enhanced Raman scattering (SERS) readout to characterize the differences in exosomes for the early diagnosis and molecular subtyping of non-small cell lung cancer (NSCLC). This integration comprises a processing unit for exosome capture and enrichment using polystyrene microspheres (PS) binding gold nanocubes (AuNCs) and anti-CD-9 antibody (denoted as PACD), and an optical sensing unit to trap the PACD and detect SERS signals from these exosomes. This system achieved a maximum trapping efficiency of 85%, and could distinguish three different NSCLC cell lines from the normal cell line with an overall accuracy of 97.88% and an area under the curve (AUC) of over 0.95 for each category. This work highlights the combined power of deep learning, SERS, and microfluidics in realizing the capture, detection, and analysis of exosomes from biological matrices, which may pave the way for clinical exosome-based cancer diagnosis and prognostication in the future.
{"title":"Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes","authors":"Hui Chen, Hongyi Liu, Longqiang Xing, Dandan Fan, Nan Chen, Pei Ma, Xuedian Zhang","doi":"10.1021/acssensors.4c03621","DOIUrl":"https://doi.org/10.1021/acssensors.4c03621","url":null,"abstract":"Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standard, is invasive, costly and provides limited information about the tumor and its molecular landscape. Exosomes, as promising biomarkers for lung cancer, are a heterogeneous collection of membranous vesicles containing tumor-specific information for liquid biopsy to identify lung cancer subtypes. However, the small size, complex structure, and heterogeneous molecular features of exosomes pose significant challenges for their effective isolation and analysis. Herein, we report a deep learning-driven microfluidic chip with surface-enhanced Raman scattering (SERS) readout to characterize the differences in exosomes for the early diagnosis and molecular subtyping of non-small cell lung cancer (NSCLC). This integration comprises a processing unit for exosome capture and enrichment using polystyrene microspheres (PS) binding gold nanocubes (AuNCs) and anti-CD-9 antibody (denoted as PACD), and an optical sensing unit to trap the PACD and detect SERS signals from these exosomes. This system achieved a maximum trapping efficiency of 85%, and could distinguish three different NSCLC cell lines from the normal cell line with an overall accuracy of 97.88% and an area under the curve (AUC) of over 0.95 for each category. This work highlights the combined power of deep learning, SERS, and microfluidics in realizing the capture, detection, and analysis of exosomes from biological matrices, which may pave the way for clinical exosome-based cancer diagnosis and prognostication in the future.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"38 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745559","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 : 2025-04-01DOI: 10.1021/acssensors.5c00432
Haocheng Qianzhu, Yi Jiun Tan, Elwy H. Abdelkader, Thomas Huber, Gottfried Otting
Substituting a single hydrogen atom in a protein by fluorine provides a probe for site-specific sensing by 19F nuclear magnetic resonance (NMR) spectroscopy with minimal impact on the properties of the protein. Genetic encoding systems are presented for five different fluorinated analogues of phenylalanine: 2-, 3-, 4-fluorophenylalanine, 2,6-difluorophenylalanine, and 3,5-difluorophenylalanine. The systems allow the installation of each of these amino acids with high fidelity during in vivo bacterial protein synthesis in response to an amber stop codon. The respective target proteins are obtained in high yield. At the site of Phe116 in different constructs of the dengue virus and Zika virus NS2B-NS3 proteases, the fluorinated phenylalanine analogues reveal evidence of significant conformational heterogeneity in 19F NMR spectra and demonstrate conformational dynamics. The availability of different 19F NMR probes allows discriminating between impacts arising from the fluorine atoms and the properties intrinsic to the protein.
{"title":"Genetic Encoding of Fluorinated Analogues of Phenylalanine for 19F NMR Spectroscopy: Detection of Conformational Heterogeneity in Flaviviral NS2B-NS3 Proteases","authors":"Haocheng Qianzhu, Yi Jiun Tan, Elwy H. Abdelkader, Thomas Huber, Gottfried Otting","doi":"10.1021/acssensors.5c00432","DOIUrl":"https://doi.org/10.1021/acssensors.5c00432","url":null,"abstract":"Substituting a single hydrogen atom in a protein by fluorine provides a probe for site-specific sensing by <sup>19</sup>F nuclear magnetic resonance (NMR) spectroscopy with minimal impact on the properties of the protein. Genetic encoding systems are presented for five different fluorinated analogues of phenylalanine: 2-, 3-, 4-fluorophenylalanine, 2,6-difluorophenylalanine, and 3,5-difluorophenylalanine. The systems allow the installation of each of these amino acids with high fidelity during in vivo bacterial protein synthesis in response to an amber stop codon. The respective target proteins are obtained in high yield. At the site of Phe116 in different constructs of the dengue virus and Zika virus NS2B-NS3 proteases, the fluorinated phenylalanine analogues reveal evidence of significant conformational heterogeneity in <sup>19</sup>F NMR spectra and demonstrate conformational dynamics. The availability of different <sup>19</sup>F NMR probes allows discriminating between impacts arising from the fluorine atoms and the properties intrinsic to the protein.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"56 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758453","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 : 2025-03-31DOI: 10.1021/acssensors.4c03325
Bharath Somalapura Prakasha, Peng Xiao, María José Esplandiu, JiaQi Yang, Daniel Navarro-Urrios, Javier Rodríguez-Viejo, Marianna Sledzinska
Semiconducting two-dimensional (2D) materials have emerged as promising candidates for gas sensors due to their exceptional sensitivity and rapid response/recovery times. However, these sensors often face significant challenges, including baseline drift, nonlinearity, cross-sensitivity to multiple gases, and early response saturation, all of which compromise their accuracy and reliability. Conventional resistive sensing approaches, which rely on a single output signal for gas concentration estimation, fail to capture the complex interactions inherent to 2D materials, such as charge carrier generation, transport, and polarization. This work addresses these limitations by utilizing impedance measurements across multiple frequencies for MoS2- and WS2-based sensors, coupled with machine learning-assisted data processing for accurate relative humidity (RH) quantification. By leveraging the impedance domain, we effectively mitigated baseline drift over extended periods and identified mutually exclusive phase behavior for the WS2-based sensor. The MoS2-based sensor exhibited long-term stability, motivating the application of a neural network-based multilayer perceptron (MLP), one-dimensional convolutional network (1D-CNN), and long short-term memory (LSTM) models to interpret multifrequency impedance data for precise RH measurements. Our approach enabled robust humidity sensing over a wide range (0–90%) with significantly faster response and recovery times than commercial sensors. Additionally, the neural network-assisted WS2 sensor effectively minimized cross-sensitivity between humidity and CO2. This work showcases the potential of multifrequency impedance-based sensing, combined with machine learning, to overcome the traditional limitations of 2D material-based sensors, offering a pathway toward more reliable, stable, and precise gas-sensing technologies.
{"title":"Impedance-Assisted Multivariate Analysis Technique for Enhanced Gas Sensing with 2D Dichalcogenides","authors":"Bharath Somalapura Prakasha, Peng Xiao, María José Esplandiu, JiaQi Yang, Daniel Navarro-Urrios, Javier Rodríguez-Viejo, Marianna Sledzinska","doi":"10.1021/acssensors.4c03325","DOIUrl":"https://doi.org/10.1021/acssensors.4c03325","url":null,"abstract":"Semiconducting two-dimensional (2D) materials have emerged as promising candidates for gas sensors due to their exceptional sensitivity and rapid response/recovery times. However, these sensors often face significant challenges, including baseline drift, nonlinearity, cross-sensitivity to multiple gases, and early response saturation, all of which compromise their accuracy and reliability. Conventional resistive sensing approaches, which rely on a single output signal for gas concentration estimation, fail to capture the complex interactions inherent to 2D materials, such as charge carrier generation, transport, and polarization. This work addresses these limitations by utilizing impedance measurements across multiple frequencies for MoS<sub>2</sub>- and WS<sub>2</sub>-based sensors, coupled with machine learning-assisted data processing for accurate relative humidity (RH) quantification. By leveraging the impedance domain, we effectively mitigated baseline drift over extended periods and identified mutually exclusive phase behavior for the WS<sub>2</sub>-based sensor. The MoS<sub>2</sub>-based sensor exhibited long-term stability, motivating the application of a neural network-based multilayer perceptron (MLP), one-dimensional convolutional network (1D-CNN), and long short-term memory (LSTM) models to interpret multifrequency impedance data for precise RH measurements. Our approach enabled robust humidity sensing over a wide range (0–90%) with significantly faster response and recovery times than commercial sensors. Additionally, the neural network-assisted WS<sub>2</sub> sensor effectively minimized cross-sensitivity between humidity and CO<sub>2</sub>. This work showcases the potential of multifrequency impedance-based sensing, combined with machine learning, to overcome the traditional limitations of 2D material-based sensors, offering a pathway toward more reliable, stable, and precise gas-sensing technologies.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"32 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745561","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 : 2025-03-31DOI: 10.1021/acssensors.5c00263
Tharmaraj Vairaperumal, Po-Tseng Lee, Ping-Yen Liu
Dengue virus (DENV) infection is a major global public health problem, particularly in tropical and subtropical regions where Aedes mosquitoes are prevalent. The clinical spectrum of dengue ranges from mild febrile illness to severe conditions such as dengue hemorrhagic fever and dengue shock syndrome. Early prediction of dengue progress is crucial for timely therapeutic medications, which can reduce both morbidity and mortality. Traditional diagnostic methods such as serological tests and polymerase chain reactions are often time-consuming and require sophisticated infrastructure and skilled personnel. To overcome these limitations, the development of point-of-care (POC) diagnosis platforms and novel predictive biomarkers is crucial to providing rapid, real-time diagnostic tools that can be used in low-resource settings and at the patient’s bedside. Predictive biomarkers enable the identification of disease risk in the early stages and can reduce hospitalization visits. This review offers a comprehensive overview of portable POC diagnosis platforms and emerging predictive biomarkers for the rapid diagnosis of severe DENV infection. Its provides an overview of its epidemiology, discusses the global burden of DENV, and explores DENV infection with different serotypes, as well as the clinical spectrum and severity of dengue. The key focus is on the latest advancements in POC diagnosis readout methods and portable POC devices for DENV diagnosis, including colorimetric assay, electrochemical method, lateral flow strip, and microfluidic chip platforms. In addition, the review article explores various emerging predictive biomarkers for the rapid detection of DENV, while also highlighting the limitations associated with protein, nucleic acid, and metabolic biomarkers. Finally, we address the current challenges, limitations, and potential future directions of POC diagnosis platforms for the diagnosis of severe DENV infection.
{"title":"Portable Point-of-Care Diagnosis Platforms and Emerging Predictive Biomarkers for Rapid Detection of Severe Dengue Viral Infection","authors":"Tharmaraj Vairaperumal, Po-Tseng Lee, Ping-Yen Liu","doi":"10.1021/acssensors.5c00263","DOIUrl":"https://doi.org/10.1021/acssensors.5c00263","url":null,"abstract":"Dengue virus (DENV) infection is a major global public health problem, particularly in tropical and subtropical regions where Aedes mosquitoes are prevalent. The clinical spectrum of dengue ranges from mild febrile illness to severe conditions such as dengue hemorrhagic fever and dengue shock syndrome. Early prediction of dengue progress is crucial for timely therapeutic medications, which can reduce both morbidity and mortality. Traditional diagnostic methods such as serological tests and polymerase chain reactions are often time-consuming and require sophisticated infrastructure and skilled personnel. To overcome these limitations, the development of point-of-care (POC) diagnosis platforms and novel predictive biomarkers is crucial to providing rapid, real-time diagnostic tools that can be used in low-resource settings and at the patient’s bedside. Predictive biomarkers enable the identification of disease risk in the early stages and can reduce hospitalization visits. This review offers a comprehensive overview of portable POC diagnosis platforms and emerging predictive biomarkers for the rapid diagnosis of severe DENV infection. Its provides an overview of its epidemiology, discusses the global burden of DENV, and explores DENV infection with different serotypes, as well as the clinical spectrum and severity of dengue. The key focus is on the latest advancements in POC diagnosis readout methods and portable POC devices for DENV diagnosis, including colorimetric assay, electrochemical method, lateral flow strip, and microfluidic chip platforms. In addition, the review article explores various emerging predictive biomarkers for the rapid detection of DENV, while also highlighting the limitations associated with protein, nucleic acid, and metabolic biomarkers. Finally, we address the current challenges, limitations, and potential future directions of POC diagnosis platforms for the diagnosis of severe DENV infection.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"58 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745562","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}
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