Pub Date : 2024-11-14DOI: 10.1021/acssensors.4c02677
Hao Wang, Dijie Yao, Yibing Luo, Bizhang Zhong, Yiqun Gu, Hongjing Wu, Bo-Ru Yang, Chunwei Li, Kai Tao, Jin Wu
Ionic conductive hydrogel-based temperature sensors have emerged as promising candidates due to their good stretchability and biocompatibility. However, the unsatisfactory sensitivity, sluggish response/recovery speed, and poor environmental stability limit their applications for accurate long-term health monitoring and robot perception, especially in extreme environments. To address these concerns, here, the stretchable temperature sensors based on a double-side elastomer-encapsulated thin-film organohydrogel (DETO) architecture are proposed with impressive performance. It is found that the water-polyol binary solvent, organohydrogel film, and sandwiched device structure play important roles in the temperature sensing performance. By modifying the composition of binary solvent and thicknesses of organohydrogel and elastomer films, the DETO microsensors realize a thickness of only 380 μm, unprecedented temperature sensitivity (37.96%/°C), fast response time (6.01 s) and recovery time (10.53 s), wide detection range (25-95.7 °C), and good stretchability (40% strain), which are superior to those of conventional hydrogel-based sensors. Furthermore, the device displays good environmental stability with negligible dehydration and prolonged operation duration. With these attributes, the wearable sensor is exploited for the real-time monitoring of various physiological signals such as human skin temperature and respiration patterns as well as temperature perception for robots.
{"title":"Ultrasensitive, Fast-Response, and Stretchable Temperature Microsensor Based on a Stable Encapsulated Organohydrogel Film for Wearable Applications.","authors":"Hao Wang, Dijie Yao, Yibing Luo, Bizhang Zhong, Yiqun Gu, Hongjing Wu, Bo-Ru Yang, Chunwei Li, Kai Tao, Jin Wu","doi":"10.1021/acssensors.4c02677","DOIUrl":"https://doi.org/10.1021/acssensors.4c02677","url":null,"abstract":"<p><p>Ionic conductive hydrogel-based temperature sensors have emerged as promising candidates due to their good stretchability and biocompatibility. However, the unsatisfactory sensitivity, sluggish response/recovery speed, and poor environmental stability limit their applications for accurate long-term health monitoring and robot perception, especially in extreme environments. To address these concerns, here, the stretchable temperature sensors based on a double-side elastomer-encapsulated thin-film organohydrogel (DETO) architecture are proposed with impressive performance. It is found that the water-polyol binary solvent, organohydrogel film, and sandwiched device structure play important roles in the temperature sensing performance. By modifying the composition of binary solvent and thicknesses of organohydrogel and elastomer films, the DETO microsensors realize a thickness of only 380 μm, unprecedented temperature sensitivity (37.96%/°C), fast response time (6.01 s) and recovery time (10.53 s), wide detection range (25-95.7 °C), and good stretchability (40% strain), which are superior to those of conventional hydrogel-based sensors. Furthermore, the device displays good environmental stability with negligible dehydration and prolonged operation duration. With these attributes, the wearable sensor is exploited for the real-time monitoring of various physiological signals such as human skin temperature and respiration patterns as well as temperature perception for robots.</p>","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142612570","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 : 2024-11-14DOI: 10.1021/acssensors.4c02052
Lin He, Yan Zhou, Min Zhang, Mingjian Chen, Yuchen Wu, Lanlin Qi, Lamei Liu, Bin Zhang, Xiaohai Yang, Xiaoxiao He, Kemin Wang
Detection of slight pH changes in skin interstitial fluid (ISF) is crucial yet challenging for studying pathological processes and understanding personal health conditions. In this work, we construct an i-motif DNA based fluorescent ratiometric microneedle sensing patch (IFR-pH MN patch) strategy that enables minimally invasive, high-resolution, and sensitive transdermal monitoring of small pH variations in ISF. The IFR-pH MN patch with advanced integration of both ISF sampling and pH sensing was fabricated from the cross-linking of gelatin methacryloyl and methacrylated hyaluronic acid, wrapping with pH-sensitive hairpin-containing i-motif DNA based fluorescent ratiometric probes in the matrix. Because it is mechanically robust for skin penetration and has high swelling ability, the IFR-pH MN patch could be quickly extracted as sufficient liquid from agarose gel (∼56.4 μL in 10 min). Benefiting from conformation changes of the hairpin-containing i-motif DNA under pH variation and ratiometric fluorescence signal readout, the IFR-pH MN patch could quantitate pH over a small range between pH 6.2 and 6.9 with an accuracy of 0.2 pH units in the mimic skin model. Furthermore, in vivo testing on wound and tumor mouse models indicated the ability of the biocompatible IFR-pH MN patch to penetrate the skin for obtaining transdermal pH values, demonstrating the potential applications in monitoring and intervention of pathological states.
{"title":"I-Motif DNA Based Fluorescent Ratiometric Microneedle Sensing Patch for Sensitive Response of Small pH Variations in Interstitial Fluid","authors":"Lin He, Yan Zhou, Min Zhang, Mingjian Chen, Yuchen Wu, Lanlin Qi, Lamei Liu, Bin Zhang, Xiaohai Yang, Xiaoxiao He, Kemin Wang","doi":"10.1021/acssensors.4c02052","DOIUrl":"https://doi.org/10.1021/acssensors.4c02052","url":null,"abstract":"Detection of slight pH changes in skin interstitial fluid (ISF) is crucial yet challenging for studying pathological processes and understanding personal health conditions. In this work, we construct an i-motif DNA based fluorescent ratiometric microneedle sensing patch (IFR-pH MN patch) strategy that enables minimally invasive, high-resolution, and sensitive transdermal monitoring of small pH variations in ISF. The IFR-pH MN patch with advanced integration of both ISF sampling and pH sensing was fabricated from the cross-linking of gelatin methacryloyl and methacrylated hyaluronic acid, wrapping with pH-sensitive hairpin-containing i-motif DNA based fluorescent ratiometric probes in the matrix. Because it is mechanically robust for skin penetration and has high swelling ability, the IFR-pH MN patch could be quickly extracted as sufficient liquid from agarose gel (∼56.4 μL in 10 min). Benefiting from conformation changes of the hairpin-containing i-motif DNA under pH variation and ratiometric fluorescence signal readout, the IFR-pH MN patch could quantitate pH over a small range between pH 6.2 and 6.9 with an accuracy of 0.2 pH units in the mimic skin model. Furthermore, <i>in vivo</i> testing on wound and tumor mouse models indicated the ability of the biocompatible IFR-pH MN patch to penetrate the skin for obtaining transdermal pH values, demonstrating the potential applications in monitoring and intervention of pathological states.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"1 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610175","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 : 2024-11-13DOI: 10.1021/acssensors.4c01642
Linlin Hou, Jian Duan, Feng Xiong, Carlo Carraro, Tielin Shi, Roya Maboudian, Hu Long
Gas sensors are pivotal across industries, encompassing environmental monitoring, industrial safety, and healthcare. Recently, a surge in demand for low power gas sensors has emerged, driven by the huge need for applications in portable devices, wireless sensor networks, and the Internet of things (IoT). The practical realization of a densely interconnected sensor network demands gas sensors to have low power consumption for energy-efficient operation. This Perspective offers a comprehensive overview of the progress of low-power sensors for gas and volatile organic compound detection, with a keen focus on the interplay between sensing materials (including metal oxide semiconductors, metal–organic frameworks, and two-dimensional materials), sensor structures, and power consumption. The main gas sensing mechanisms are discussed, and we delve into the mechanisms for achieving low power consumption including material properties and sensor design. Furthermore, typical applications of low power gas sensors are also presented, including wearable technology, food safety, and environmental monitoring. The review will end by discussing some open questions and ongoing needs.
{"title":"Low Power Gas Sensors: From Structure to Application","authors":"Linlin Hou, Jian Duan, Feng Xiong, Carlo Carraro, Tielin Shi, Roya Maboudian, Hu Long","doi":"10.1021/acssensors.4c01642","DOIUrl":"https://doi.org/10.1021/acssensors.4c01642","url":null,"abstract":"Gas sensors are pivotal across industries, encompassing environmental monitoring, industrial safety, and healthcare. Recently, a surge in demand for low power gas sensors has emerged, driven by the huge need for applications in portable devices, wireless sensor networks, and the Internet of things (IoT). The practical realization of a densely interconnected sensor network demands gas sensors to have low power consumption for energy-efficient operation. This Perspective offers a comprehensive overview of the progress of low-power sensors for gas and volatile organic compound detection, with a keen focus on the interplay between sensing materials (including metal oxide semiconductors, metal–organic frameworks, and two-dimensional materials), sensor structures, and power consumption. The main gas sensing mechanisms are discussed, and we delve into the mechanisms for achieving low power consumption including material properties and sensor design. Furthermore, typical applications of low power gas sensors are also presented, including wearable technology, food safety, and environmental monitoring. The review will end by discussing some open questions and ongoing needs.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"127 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142609839","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 : 2024-11-13DOI: 10.1021/acssensors.4c02073
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.
{"title":"TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses","authors":"Yingying Jian, Nan Zhang, Yunzhe Bi, Xiyang Liu, Jinhai Fan, Weiwei Wu, Taoping Liu","doi":"10.1021/acssensors.4c02073","DOIUrl":"https://doi.org/10.1021/acssensors.4c02073","url":null,"abstract":"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.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"158 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142609840","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 : 2024-11-13DOI: 10.1021/acssensors.4c01887
Miao Liu, Peng Song, Qi Wang, Mei Yan
The design of high-performance and low-power formaldehyde (HCHO) gas sensors is of great interest to researchers for environmental monitoring and human health. Herein, In2O3/CsPbBr3 composites were successfully synthesized through an electrospinning and self-assembly approach, and their ultraviolet-activated (UV-activated) HCHO gas-sensing properties were investigated. The measurement data indicated that the In2O3/CsPbBr3 sensor possesses an excellent selectivity toward HCHO. The response of the In2O3/CsPbBr3 sensor to 2 ppm of HCHO was 31.4, which was almost 11 times larger than that of In2O3 alone. Besides, the In2O3/CsPbBr3 sensor also displayed extraordinary linearity (R2 = 0.9696), stable reversibility, and ideal humidity resistance. Interestingly, the gas-sensing properties of the In2O3/CsPbBr3 sensor were further improved (Ra/Rg = 54.8) under UV light irradiation. Meanwhile, the response/recovery time was shortened to 7/9 s. The improvement of HCHO-sensing properties might be ascribed to the distinctive structure of In2O3 nanofibers, the adsorption capacity of cesium lead bromide quantum dots (CsPbBr3 QDs) for UV light, and the synergistic effect of heterostructures between the components. Density functional theory (DFT) was implemented to discuss the adsorption ability and electronic characteristics of HCHO at the surface of In2O3/CsPbBr3 composites. Especially, this research points out new constructive thoughts for the exploitation of UV light improved gas-sensing materials.
{"title":"CsPbBr3 Quantum Dot Modified In2O3 Nanofibers for Effective Detection of ppb-Level HCHO at Room Temperature under UV Illumination","authors":"Miao Liu, Peng Song, Qi Wang, Mei Yan","doi":"10.1021/acssensors.4c01887","DOIUrl":"https://doi.org/10.1021/acssensors.4c01887","url":null,"abstract":"The design of high-performance and low-power formaldehyde (HCHO) gas sensors is of great interest to researchers for environmental monitoring and human health. Herein, In<sub>2</sub>O<sub>3</sub>/CsPbBr<sub>3</sub> composites were successfully synthesized through an electrospinning and self-assembly approach, and their ultraviolet-activated (UV-activated) HCHO gas-sensing properties were investigated. The measurement data indicated that the In<sub>2</sub>O<sub>3</sub>/CsPbBr<sub>3</sub> sensor possesses an excellent selectivity toward HCHO. The response of the In<sub>2</sub>O<sub>3</sub>/CsPbBr<sub>3</sub> sensor to 2 ppm of HCHO was 31.4, which was almost 11 times larger than that of In<sub>2</sub>O<sub>3</sub> alone. Besides, the In<sub>2</sub>O<sub>3</sub>/CsPbBr<sub>3</sub> sensor also displayed extraordinary linearity (<i>R</i><sup>2</sup> = 0.9696), stable reversibility, and ideal humidity resistance. Interestingly, the gas-sensing properties of the In<sub>2</sub>O<sub>3</sub>/CsPbBr<sub>3</sub> sensor were further improved (<i>R</i><sub>a</sub>/<i>R</i><sub>g</sub> = 54.8) under UV light irradiation. Meanwhile, the response/recovery time was shortened to 7/9 s. The improvement of HCHO-sensing properties might be ascribed to the distinctive structure of In<sub>2</sub>O<sub>3</sub> nanofibers, the adsorption capacity of cesium lead bromide quantum dots (CsPbBr<sub>3</sub> QDs) for UV light, and the synergistic effect of heterostructures between the components. Density functional theory (DFT) was implemented to discuss the adsorption ability and electronic characteristics of HCHO at the surface of In<sub>2</sub>O<sub>3</sub>/CsPbBr<sub>3</sub> composites. Especially, this research points out new constructive thoughts for the exploitation of UV light improved gas-sensing materials.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"40 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601427","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}
Sulfur-containing gases, such as H2S and SO2, play significant roles in a multitude of biological processes affecting human life and health. Precise and efficient detection of these gases is therefore crucial for advancing one’s understanding of their biological roles and developing effective diagnostic strategies. Fluorescent sensing offers a highly sensitive and versatile approach for detecting these gases. This Review examines the recent advances in the fluorescent detection of H2S and SO2, highlighting the key mechanisms involved in fluorescence signal transduction, including changes in intensity and wavelength shifts. The diverse array of probe molecules employed for this purpose, including those utilizing mechanisms such as nucleophilic reactions, Förster resonance energy transfer (FRET), and sulfur affinity interactions are explored. In additional to organic sensors, the focus of the Review is particularly directed toward quantum dot (QD) systems, emphasizing their tunable optical properties that hold immense potential for fluorescence sensing. Beyond the traditional III–V QDs, we delve into the emerging fluorescence sensors based on halide perovskite QDs, upconversion nanocrystals, and other novel materials. These advanced QD systems hold promise for the development of highly sensitive and cost-effective gas detectors, paving the way for significant advances in biomedical and environmental monitoring. This Review provides a comprehensive overview of the current state-of-the-art in QD-based fluorescence sensing of sulfur-containing gases and provides a multifaceted discussion comparing organic fluorescent sensors with QD sensors, highlighting the key challenges and opportunities for the integration of fluorescence sensing as it evolves. The Review aims to facilitate further research and development of innovative sensing platforms to enable more accurate and sensitive detection of these important gases.
{"title":"Fluorescent Sensing for the Detection and Quantification of Sulfur-Containing Gases","authors":"Kehang Wang, Chenghao Bi, Lev Zelenkov, Xiuzhen Liu, Mingzhao Song, Wenxin Wang, Sergey Makarov, Wenping Yin","doi":"10.1021/acssensors.4c02033","DOIUrl":"https://doi.org/10.1021/acssensors.4c02033","url":null,"abstract":"Sulfur-containing gases, such as H<sub>2</sub>S and SO<sub>2</sub>, play significant roles in a multitude of biological processes affecting human life and health. Precise and efficient detection of these gases is therefore crucial for advancing one’s understanding of their biological roles and developing effective diagnostic strategies. Fluorescent sensing offers a highly sensitive and versatile approach for detecting these gases. This Review examines the recent advances in the fluorescent detection of H<sub>2</sub>S and SO<sub>2</sub>, highlighting the key mechanisms involved in fluorescence signal transduction, including changes in intensity and wavelength shifts. The diverse array of probe molecules employed for this purpose, including those utilizing mechanisms such as nucleophilic reactions, Förster resonance energy transfer (FRET), and sulfur affinity interactions are explored. In additional to organic sensors, the focus of the Review is particularly directed toward quantum dot (QD) systems, emphasizing their tunable optical properties that hold immense potential for fluorescence sensing. Beyond the traditional III–V QDs, we delve into the emerging fluorescence sensors based on halide perovskite QDs, upconversion nanocrystals, and other novel materials. These advanced QD systems hold promise for the development of highly sensitive and cost-effective gas detectors, paving the way for significant advances in biomedical and environmental monitoring. This Review provides a comprehensive overview of the current state-of-the-art in QD-based fluorescence sensing of sulfur-containing gases and provides a multifaceted discussion comparing organic fluorescent sensors with QD sensors, highlighting the key challenges and opportunities for the integration of fluorescence sensing as it evolves. The Review aims to facilitate further research and development of innovative sensing platforms to enable more accurate and sensitive detection of these important gases.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"72 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601429","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 : 2024-11-13DOI: 10.1021/acssensors.4c01867
Yilun Ma, Xingchang Qiu, Zaihua Duan, Lili Liu, Juan Li, Yuanming Wu, Zhen Yuan, Yadong Jiang, Huiling Tai
Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model’s accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.
{"title":"A Novel Calibration Scheme of Gas Sensor Array for a More Accurate Measurement Model of Mixed Gases","authors":"Yilun Ma, Xingchang Qiu, Zaihua Duan, Lili Liu, Juan Li, Yuanming Wu, Zhen Yuan, Yadong Jiang, Huiling Tai","doi":"10.1021/acssensors.4c01867","DOIUrl":"https://doi.org/10.1021/acssensors.4c01867","url":null,"abstract":"Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model’s accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"15 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601428","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 : 2024-11-13DOI: 10.1021/acssensors.4c01995
Ankita Pathak, S. Samanta, H. Donthula, Reshmi Thekke Parayil, Manmeet Kaur, Ajay Singh
Surface functionalization of semiconducting metal oxides has emerged as a highly effective approach for enhancing their sensing capabilities. In the present work, the surface of randomly oriented zinc oxide (ZnO) nanowires is modified with an optimized thickness (7 nm) of magnesium oxide (MgO), which exhibits an exceptionally sensitive and selective behavior toward NO2 gas, yielding a response of approximately 310 for 10 ppm concentration at room temperature. The synergistic interplay between ZnO and MgO leads to a remarkable 20-fold improvement in sensor response compared to a pristine ZnO film and allows the detection of concentrations as low as 50 ppb. The ZnO–MgO composite was characterized using X-ray diffraction (XRD), XPS, and SEM-EDS to gain structural, compositional, and morphological insights. The interaction of the NO2 molecule with the sensor film was investigated using density functional theory (DFT) simulations, revealing that oxygen vacant sites on the MgO surface are most favorable for NO2 adsorption, with an adsorption energy of −3.97 eV and a charge transfer of 1.74e toward NO2. The XPS, photoluminescence (PL), and EPR measurements experimentally verified the presence of oxygen vacancies in the sensing material. The introduction of localized levels within the band gap by oxygen vacancies significantly promotes the interaction of gas molecules with these sites, which enhances the charge transfer toward NO2 gas molecules. This augmentation has a profound influence on the space charge region at the ZnO–MgO interface, which is pivotal for modulating the charge transport in the ZnO layer, resulting in the substantial improvement of NO2 response at room temperature.
{"title":"Unleashing the Potential of Tailored ZnO–MgO Nanocomposites for the Enhancement of NO2 Sensing Performance at Room Temperature","authors":"Ankita Pathak, S. Samanta, H. Donthula, Reshmi Thekke Parayil, Manmeet Kaur, Ajay Singh","doi":"10.1021/acssensors.4c01995","DOIUrl":"https://doi.org/10.1021/acssensors.4c01995","url":null,"abstract":"Surface functionalization of semiconducting metal oxides has emerged as a highly effective approach for enhancing their sensing capabilities. In the present work, the surface of randomly oriented zinc oxide (ZnO) nanowires is modified with an optimized thickness (7 nm) of magnesium oxide (MgO), which exhibits an exceptionally sensitive and selective behavior toward NO<sub>2</sub> gas, yielding a response of approximately 310 for 10 ppm concentration at room temperature. The synergistic interplay between ZnO and MgO leads to a remarkable 20-fold improvement in sensor response compared to a pristine ZnO film and allows the detection of concentrations as low as 50 ppb. The ZnO–MgO composite was characterized using X-ray diffraction (XRD), XPS, and SEM-EDS to gain structural, compositional, and morphological insights. The interaction of the NO<sub>2</sub> molecule with the sensor film was investigated using density functional theory (DFT) simulations, revealing that oxygen vacant sites on the MgO surface are most favorable for NO<sub>2</sub> adsorption, with an adsorption energy of −3.97 eV and a charge transfer of 1.74e toward NO<sub>2</sub>. The XPS, photoluminescence (PL), and EPR measurements experimentally verified the presence of oxygen vacancies in the sensing material. The introduction of localized levels within the band gap by oxygen vacancies significantly promotes the interaction of gas molecules with these sites, which enhances the charge transfer toward NO<sub>2</sub> gas molecules. This augmentation has a profound influence on the space charge region at the ZnO–MgO interface, which is pivotal for modulating the charge transport in the ZnO layer, resulting in the substantial improvement of NO<sub>2</sub> response at room temperature.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"29 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601468","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 : 2024-11-13DOI: 10.1021/acssensors.4c02117
Lydia Colvin, Diana Al Husseini, Dandan Tu, Darin Dunlap, Tyler Lalonde, Muhammed Üçüncü, Alicia Megia-Fernandez, Mark Bradley, Wenshe Liu, Melissa A. Grunlan, Gerard L. Coté
Deep-red fluorescence was implemented in this fully injectable, nonenzymatic glucose biosensor design to allow for better light penetration through the skin, particularly for darker skin tones. In this work, a novel method was developed to synthesize Cy5.5 labeled mannose conjugates (Cy5.5-mannobiose, Cy5.5-mannotriose, and Cy5.5-mannotetraose) to act as the fluorescent competing ligand in a competitive binding assay with the protein Concanavalin A acting as the recognition molecule. Using fluorescence anisotropy (FA) data, a computational model was developed to determine optimal concentration ratios of the assay components to allow for sensitive glucose measurements within the physiological range. The model was experimentally validated by measuring the glucose response via FA of the three Cy5.5-labeled mannose conjugates synthesized with Cy5.5-mannotetraose demonstrating the most sensitive response to glucose across the physiological range. The developed method may be broadly applied to a vast range of commercially available fluorescent dyes and opens up opportunities for glucose measurements using nonenzymatic assays.
在这种完全可注射的非酶葡萄糖生物传感器设计中采用了深红色荧光，使光线更好地穿透皮肤，尤其是深肤色皮肤。在这项工作中，开发了一种新方法来合成 Cy5.5 标记的甘露糖共轭物（Cy5.5-甘露双糖、Cy5.5-甘露三糖和 Cy5.5-甘露四糖），作为竞争性结合试验中的荧光竞争配体，而蛋白质 Concanavalin A 则作为识别分子。利用荧光各向异性（FA）数据建立了一个计算模型，以确定测定成分的最佳浓度比，从而在生理范围内进行灵敏的葡萄糖测量。通过 FA 测量三种 Cy5.5 标记的甘露糖共轭物的葡萄糖反应，实验验证了该模型，Cy5.5-甘露四糖在生理范围内对葡萄糖的反应最为灵敏。所开发的方法可广泛应用于各种市售荧光染料，并为使用非酶测定法测量葡萄糖提供了机会。
{"title":"Computational Model-Assisted Development of a Nonenzymatic Fluorescent Glucose-Sensing Assay","authors":"Lydia Colvin, Diana Al Husseini, Dandan Tu, Darin Dunlap, Tyler Lalonde, Muhammed Üçüncü, Alicia Megia-Fernandez, Mark Bradley, Wenshe Liu, Melissa A. Grunlan, Gerard L. Coté","doi":"10.1021/acssensors.4c02117","DOIUrl":"https://doi.org/10.1021/acssensors.4c02117","url":null,"abstract":"Deep-red fluorescence was implemented in this fully injectable, nonenzymatic glucose biosensor design to allow for better light penetration through the skin, particularly for darker skin tones. In this work, a novel method was developed to synthesize Cy5.5 labeled mannose conjugates (Cy5.5-mannobiose, Cy5.5-mannotriose, and Cy5.5-mannotetraose) to act as the fluorescent competing ligand in a competitive binding assay with the protein Concanavalin A acting as the recognition molecule. Using fluorescence anisotropy (FA) data, a computational model was developed to determine optimal concentration ratios of the assay components to allow for sensitive glucose measurements within the physiological range. The model was experimentally validated by measuring the glucose response via FA of the three Cy5.5-labeled mannose conjugates synthesized with Cy5.5-mannotetraose demonstrating the most sensitive response to glucose across the physiological range. The developed method may be broadly applied to a vast range of commercially available fluorescent dyes and opens up opportunities for glucose measurements using nonenzymatic assays.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"98 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142609842","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 : 2024-11-12DOI: 10.1021/acssensors.4c01399
Shiwei Liu, Weiyu Zhang, Guojie Zhang, Jun Sun, Ning Tian, Qihua Sun, Zhaofeng Wu
N-methylpyrrolidone (NMP) is an excellent advanced solvent that can be easily absorbed by the human body and has the characteristics of flammability and explosion. To reduce the risk, the environmental concentrations of NMP need to be measured. A series of covalent organic frameworks (COF) connected by an imine bond have been successfully prepared at room temperature by changing the synthesis time catalyzed by scandium(III) trifluoromethanesulfonate (Sc(OTf)3). The effect of the synthesis time on the sample properties was compared by XRD, FT-IR, XPS, SEM, TEM, and BET. The results showed that synthesis time had almost no effect on the morphology, specific surface area, and functional groups of the COF samples but had a significant impact on the pore size distribution, residual bonds, and other defects, which in turn affected the gas sensing performance. The sensor results showed that all samples had good sensing performance for NMP, among which the sample synthesized for 48 h had the best sensing performance, with a limit of detection of 692 ppb and good stability and repeatability. The excellent performance of the COF samples benefits from the large specific surface area, hydrogen bonding interactions, electrostatic attraction, and high defects. This study provides an effective method for NMP detection and expands the application range of the COF materials.
{"title":"Controlled Sensor Derived from COF Materials for the Effective Detection of N-Methylpyrrolidone","authors":"Shiwei Liu, Weiyu Zhang, Guojie Zhang, Jun Sun, Ning Tian, Qihua Sun, Zhaofeng Wu","doi":"10.1021/acssensors.4c01399","DOIUrl":"https://doi.org/10.1021/acssensors.4c01399","url":null,"abstract":"<i>N</i>-methylpyrrolidone (NMP) is an excellent advanced solvent that can be easily absorbed by the human body and has the characteristics of flammability and explosion. To reduce the risk, the environmental concentrations of NMP need to be measured. A series of covalent organic frameworks (COF) connected by an imine bond have been successfully prepared at room temperature by changing the synthesis time catalyzed by scandium(III) trifluoromethanesulfonate (Sc(OTf)<sub>3</sub>). The effect of the synthesis time on the sample properties was compared by XRD, FT-IR, XPS, SEM, TEM, and BET. The results showed that synthesis time had almost no effect on the morphology, specific surface area, and functional groups of the COF samples but had a significant impact on the pore size distribution, residual bonds, and other defects, which in turn affected the gas sensing performance. The sensor results showed that all samples had good sensing performance for NMP, among which the sample synthesized for 48 h had the best sensing performance, with a limit of detection of 692 ppb and good stability and repeatability. The excellent performance of the COF samples benefits from the large specific surface area, hydrogen bonding interactions, electrostatic attraction, and high defects. This study provides an effective method for NMP detection and expands the application range of the COF materials.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"2 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601431","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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