Pub Date : 2025-04-07DOI: 10.1021/acssensors.5c00021
Xiaojing Zhang, Li Sin Wong, Zhenyuan Tang, Hangming Xiong, Jiaying Sun, Liubing Kong, Min Tu, Yanjie Hu, Yong Zhou, Wenwu Zhu, K. Jimmy Hsia, Hao Wan, Ping Wang
The COVID-19 pandemic has highlighted the importance of early screening and pathogen identification for the effective treatment of pneumonia. Exhaled breath condensate (EBC) provides a noninvasive and easily accessible method for early diagnosis of respiratory diseases, as it captures biomarkers from the airway lining fluid, offering a timely and reliable reflection of respiratory inflammation. Procalcitonin (PCT) is a biomarker widely used to assess infection type and severity, particularly for distinguishing between bacterial and nonbacterial pneumonia. However, detecting PCT especially in EBC is challenging due to its extremely low concentrations. In this work, we developed an ultrasensitive Love-type surface acoustic wave (Love-SAW) biosensor based on self-assembled gold nanoparticles on dendritic mesoporous silica nanoparticles (DMSN@AuNPs) with in situ amplification for PCT detection in EBC. Dendritic mesoporous silica nanoparticles (DMSNs), an emerging porous material with features of large surface area, high thermal stability, and ease of functionalization were employed to load a large amount of AuNPs that can spontaneously grow in situ to further enhance the sensing performance. An automatic detection system was also developed to integrate with the Love-SAW biosensor for multichannel detection of PCT in EBC for pneumonia screening. The DMSN@AuNPs based Love-SAW biosensor demonstrates remarkable performance with a detection range of 0.01–10 ng/mL and detection limit of 3.7 pg/mL, which is about 350 times higher than conventional AuNPs-based methods. These results validate the potential of DMSN@AuNPs based Love-SAW biosensors for ultrasensitive detection of low-concentration biomarkers, providing a promising platform for in vitro diagnostics.
{"title":"Ultrasensitive Love-SAW Biosensor Based on Self-Assembled DMSN@AuNPs with In Situ Amplification for Detecting Biomarker Procalcitonin in Exhaled Breath Condensate","authors":"Xiaojing Zhang, Li Sin Wong, Zhenyuan Tang, Hangming Xiong, Jiaying Sun, Liubing Kong, Min Tu, Yanjie Hu, Yong Zhou, Wenwu Zhu, K. Jimmy Hsia, Hao Wan, Ping Wang","doi":"10.1021/acssensors.5c00021","DOIUrl":"https://doi.org/10.1021/acssensors.5c00021","url":null,"abstract":"The COVID-19 pandemic has highlighted the importance of early screening and pathogen identification for the effective treatment of pneumonia. Exhaled breath condensate (EBC) provides a noninvasive and easily accessible method for early diagnosis of respiratory diseases, as it captures biomarkers from the airway lining fluid, offering a timely and reliable reflection of respiratory inflammation. Procalcitonin (PCT) is a biomarker widely used to assess infection type and severity, particularly for distinguishing between bacterial and nonbacterial pneumonia. However, detecting PCT especially in EBC is challenging due to its extremely low concentrations. In this work, we developed an ultrasensitive Love-type surface acoustic wave (Love-SAW) biosensor based on self-assembled gold nanoparticles on dendritic mesoporous silica nanoparticles (DMSN@AuNPs) with in situ amplification for PCT detection in EBC. Dendritic mesoporous silica nanoparticles (DMSNs), an emerging porous material with features of large surface area, high thermal stability, and ease of functionalization were employed to load a large amount of AuNPs that can spontaneously grow in situ to further enhance the sensing performance. An automatic detection system was also developed to integrate with the Love-SAW biosensor for multichannel detection of PCT in EBC for pneumonia screening. The DMSN@AuNPs based Love-SAW biosensor demonstrates remarkable performance with a detection range of 0.01–10 ng/mL and detection limit of 3.7 pg/mL, which is about 350 times higher than conventional AuNPs-based methods. These results validate the potential of DMSN@AuNPs based Love-SAW biosensors for ultrasensitive detection of low-concentration biomarkers, providing a promising platform for in vitro diagnostics.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"217 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789623","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 : 2025-04-07DOI: 10.1021/acssensors.4c03477
Mengfei Xu, Ziliang Song, Quan Peng, Qingda Xu, Zhiyuan Du, Tao Ruan, Bin Yang, Qingkun Liu, Xu Liu, Xumin Hou, Mu Qin, Jingquan Liu
Pulse field ablation (PFA) has become a popular technique for treating tens of millions of patients with atrial fibrillation, as it avoids many complications associated with traditional radiofrequency ablation. However, currently, limited studies have used millimeter-scale rigid electrodes modified from radiofrequency ablation to apply electrical pulses of thousands of volts without integrated sensing capabilities. Herein, we combine fractal microelectronics with biomedical catheters for low-voltage PFA, detection of electrode–tissue contact, and interventional electrocardiogram recording. The fractal configuration increases the ratio of the microelectrode insulating edge to area, which facilitates the transfer of current from the microelectrode to the tissue, increasing the ablation depth by 38.6% at 300 V (a 10-fold reduction compared to current technology). In vivo ablation experiments on living beagles successfully block electrical conduction, as demonstrated by voltage mapping and electrical pacing. More impressively, this study provides the first evidence that microelectrodes can selectively ablate cardiomyocytes without damaging nerves and blood vessels, greatly improving the safety of PFA. These results are essential for the clinical translation of PFA in the field of cardiac electrophysiology.
{"title":"Catheter-Integrated Fractal Microelectronics for Low-Voltage Ablation and Minimally Invasive Sensing","authors":"Mengfei Xu, Ziliang Song, Quan Peng, Qingda Xu, Zhiyuan Du, Tao Ruan, Bin Yang, Qingkun Liu, Xu Liu, Xumin Hou, Mu Qin, Jingquan Liu","doi":"10.1021/acssensors.4c03477","DOIUrl":"https://doi.org/10.1021/acssensors.4c03477","url":null,"abstract":"Pulse field ablation (PFA) has become a popular technique for treating tens of millions of patients with atrial fibrillation, as it avoids many complications associated with traditional radiofrequency ablation. However, currently, limited studies have used millimeter-scale rigid electrodes modified from radiofrequency ablation to apply electrical pulses of thousands of volts without integrated sensing capabilities. Herein, we combine fractal microelectronics with biomedical catheters for low-voltage PFA, detection of electrode–tissue contact, and interventional electrocardiogram recording. The fractal configuration increases the ratio of the microelectrode insulating edge to area, which facilitates the transfer of current from the microelectrode to the tissue, increasing the ablation depth by 38.6% at 300 V (a 10-fold reduction compared to current technology). <i>In vivo</i> ablation experiments on living beagles successfully block electrical conduction, as demonstrated by voltage mapping and electrical pacing. More impressively, this study provides the first evidence that microelectrodes can selectively ablate cardiomyocytes without damaging nerves and blood vessels, greatly improving the safety of PFA. These results are essential for the clinical translation of PFA in the field of cardiac electrophysiology.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"37 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789633","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 : 2025-04-06DOI: 10.1021/acssensors.4c03560
Jintai Deng, Wang Wan, Rui Sun, Qiuxuan Xia, Jing Yan, Jialu Sun, Xiaomeng Jia, Hao Jin, Xueqing Wang, Kun Guo, Man Li, Yu Liu
Aggrephagy in cells is defined as the degradation of intracellular aggregated proteins via the macroautophagy process. This process sequesters protein aggregates into autolysosomes, which bear characteristic viscous and acidic microenvironments. Limited protein aggregation sensors are environmentally compatible with the cellular aggrephagy process. Here, we report an acid-resistant and viscosity-sensitive proteome aggregation sensor to detect cellular aggrephagy in stressed cells and clinical samples. This sensor fluoresces upon selectively and ubiquitously binding to different aggregated proteins. Importantly, unlike other reported protein aggregation sensors, our probe offers unique acid-resistant fluorescence inside aggregated proteins, enabling its application in the acidic autolysosome microenvironment. In live cells under various stressed conditions, the optimal probe (A6) successfully detects aggregated proteome in autolysosomes, as validated by colocalization with a lysosomal tracker. Additionally, we demonstrate that the sensor can detect proteome aggregation in heat-stressed clinical tissue samples biopsied from cancer patients undergoing thermal perfusion treatment. Together, the reported acid-resistant and viscosity-sensitive protein aggregation sensor facilitates the detection of cellular aggrephagy by chemically matching its microenvironmental characteristics.
{"title":"Acid-Resistant and Viscosity-Sensitive Proteome Aggregation Sensor To Visualize Cellular Aggrephagy in Live Cells and Clinical Samples","authors":"Jintai Deng, Wang Wan, Rui Sun, Qiuxuan Xia, Jing Yan, Jialu Sun, Xiaomeng Jia, Hao Jin, Xueqing Wang, Kun Guo, Man Li, Yu Liu","doi":"10.1021/acssensors.4c03560","DOIUrl":"https://doi.org/10.1021/acssensors.4c03560","url":null,"abstract":"Aggrephagy in cells is defined as the degradation of intracellular aggregated proteins via the macroautophagy process. This process sequesters protein aggregates into autolysosomes, which bear characteristic viscous and acidic microenvironments. Limited protein aggregation sensors are environmentally compatible with the cellular aggrephagy process. Here, we report an acid-resistant and viscosity-sensitive proteome aggregation sensor to detect cellular aggrephagy in stressed cells and clinical samples. This sensor fluoresces upon selectively and ubiquitously binding to different aggregated proteins. Importantly, unlike other reported protein aggregation sensors, our probe offers unique acid-resistant fluorescence inside aggregated proteins, enabling its application in the acidic autolysosome microenvironment. In live cells under various stressed conditions, the optimal probe (A6) successfully detects aggregated proteome in autolysosomes, as validated by colocalization with a lysosomal tracker. Additionally, we demonstrate that the sensor can detect proteome aggregation in heat-stressed clinical tissue samples biopsied from cancer patients undergoing thermal perfusion treatment. Together, the reported acid-resistant and viscosity-sensitive protein aggregation sensor facilitates the detection of cellular aggrephagy by chemically matching its microenvironmental characteristics.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"18 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789634","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 : 2025-04-05DOI: 10.1021/acssensors.5c00126
Madeline L. Honig, Philippe Bühlmann
The way upper limits of detection (LODs) are typically reported in the ion-selective electrode (ISE) literature is unfortunately outdated. It is well understood that the upper LOD of a polymeric-membrane ISE is limited by Donnan failure, that is, the transfer of primary ions along with interfering ions of the opposite charge sign (commonly referred to as counterions) from the sample into the sensing membrane. However, it is often difficult to compare upper LODs for ISEs from different sources. The majority of publications on ISEs describe Donnan failure for one type of counterion only, making it impossible for end users to predict the interference for other counterions. Moreover, linear ranges for ISEs based on different ionophores cannot be compared to one another when Donnan failure was reported for different counterions. To this end, we introduce here selectivity coefficients, KI,XpotX, for interfering counterions. Using this new concept, the primary ion activity at which Donnan failure occurs can be readily predicted from measured KI,XpotX values by the use of the uncomplicated expression aXzI/zx/KI,XpotX. Consistent with the intuition that many ISE users have for conventional selectivity coefficients, large KI,XpotX values are characteristic for counterions that interfere strongly. We show experimentally that trends as predicted by the phase boundary model for Donnan failure, such as the effects of counterion hydrophobicity and ionophore complex stability, are often accurately predicted with the KI,XpotX approach. However, there are notable exceptions when the underlying assumptions made by users do not apply, such as when counterions unexpectedly form aggregates with other species in the sensing membranes. The empirically measured KI,XpotX coefficients enable the discovery of such phenomena, opening a rational path to improving upper LODs and, thereby, linear response ranges.
{"title":"Ion-Selective Electrodes: Selectivity Coefficients for Interfering Ions of the Opposite Charge Sign","authors":"Madeline L. Honig, Philippe Bühlmann","doi":"10.1021/acssensors.5c00126","DOIUrl":"https://doi.org/10.1021/acssensors.5c00126","url":null,"abstract":"The way upper limits of detection (LODs) are typically reported in the ion-selective electrode (ISE) literature is unfortunately outdated. It is well understood that the upper LOD of a polymeric-membrane ISE is limited by Donnan failure, that is, the transfer of primary ions along with interfering ions of the opposite charge sign (commonly referred to as counterions) from the sample into the sensing membrane. However, it is often difficult to compare upper LODs for ISEs from different sources. The majority of publications on ISEs describe Donnan failure for one type of counterion only, making it impossible for end users to predict the interference for other counterions. Moreover, linear ranges for ISEs based on different ionophores cannot be compared to one another when Donnan failure was reported for different counterions. To this end, we introduce here selectivity coefficients, <i>K</i><sub><i>I</i>,<i>X</i></sub><sup>potX</sup>, for interfering counterions. Using this new concept, the primary ion activity at which Donnan failure occurs can be readily predicted from measured <i>K</i><sub><i>I</i>,<i>X</i></sub><sup>potX</sup> values by the use of the uncomplicated expression <i>a</i><sub>X</sub><sup><i>z</i><sub><i>I</i></sub>/<i>z</i><sub><i>x</i></sub></sup>/<i>K</i><sub><i>I</i>,<i>X</i></sub><sup>potX</sup>. Consistent with the intuition that many ISE users have for conventional selectivity coefficients, large <i>K</i><sub><i>I</i>,<i>X</i></sub><sup>potX</sup> values are characteristic for counterions that interfere strongly. We show experimentally that trends as predicted by the phase boundary model for Donnan failure, such as the effects of counterion hydrophobicity and ionophore complex stability, are often accurately predicted with the <i>K</i><sub><i>I</i>,<i>X</i></sub><sup>potX</sup> approach. However, there are notable exceptions when the underlying assumptions made by users do not apply, such as when counterions unexpectedly form aggregates with other species in the sensing membranes. The empirically measured <i>K</i><sub><i>I</i>,<i>X</i></sub><sup>potX</sup> coefficients enable the discovery of such phenomena, opening a rational path to improving upper LODs and, thereby, linear response ranges.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"37 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783018","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 : 2025-04-04DOI: 10.1021/acssensors.4c03260
Che-Lun Chin, Chia-En Chang, Ling Chao
Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural networks (CNNs) are widely used for spectrum classification due to their ability to capture local peak features. In this study, we introduce a multiscale CNN designed to detect weak biomolecule signals and differentiate spectra with features that cannot be statistically distinguished. The approach is further enhanced by a new visualization technique tailored for multiscale spectral analysis, providing clear insights into classification results. Using the classification of cholera toxin B subunit (CTB)-treated versus untreated cell membrane samples, whose spectra cannot be statistically differentiated, the optimized multiscale CNN achieved superior performance compared to traditional machine learning methods and existing multiscale CNNs, with accuracy (99.22%), sensitivity (99.27%), specificity (99.16%), and precision (99.20%). Our new visualization method, based on gradients of activation maps with respect to class scores, generates saliency scores that capture sample variations, with decision-making relying on consistently identified peak features. By visualizing the effects of different kernel sizes, Grad-AM highlights features at varying scales, aligning closely with spectral features and enhancing CNN interpretability in complex biomolecular analysis. These advancements demonstrate the potential of our method to improve spectral analysis and reveal previously hidden peaks in complex biological environments.
生物应用中的拉曼光谱面临着复杂光谱的挑战，其特点是峰值宽度不一，生物背景噪声很大。卷积神经网络（CNN）能够捕捉局部峰值特征，因此被广泛用于光谱分类。在本研究中，我们引入了一种多尺度 CNN，旨在检测微弱的生物分子信号，并区分具有无法从统计学角度区分的特征的光谱。为多尺度光谱分析量身定制的新型可视化技术进一步增强了该方法，为分类结果提供了清晰的洞察力。通过对霍乱毒素 B 亚基（CTB）处理过与未处理过的细胞膜样本进行分类，优化的多尺度 CNN 在准确度（99.22%）、灵敏度（99.27%）、特异度（99.16%）和精确度（99.20%）方面均优于传统的机器学习方法和现有的多尺度 CNN。我们的新可视化方法基于激活图相对于类得分的梯度，可生成捕捉样本变化的显著性得分，决策制定依赖于一致识别的峰值特征。通过可视化不同内核大小的影响，Grad-AM 突出了不同尺度的特征，与光谱特征紧密结合，增强了复杂生物分子分析中 CNN 的可解释性。这些进步证明了我们的方法在改进光谱分析和揭示复杂生物环境中以前隐藏的峰值方面的潜力。
{"title":"Interpretable Multiscale Convolutional Neural Network for Classification and Feature Visualization of Weak Raman Spectra of Biomolecules at Cell Membranes","authors":"Che-Lun Chin, Chia-En Chang, Ling Chao","doi":"10.1021/acssensors.4c03260","DOIUrl":"https://doi.org/10.1021/acssensors.4c03260","url":null,"abstract":"Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural networks (CNNs) are widely used for spectrum classification due to their ability to capture local peak features. In this study, we introduce a multiscale CNN designed to detect weak biomolecule signals and differentiate spectra with features that cannot be statistically distinguished. The approach is further enhanced by a new visualization technique tailored for multiscale spectral analysis, providing clear insights into classification results. Using the classification of cholera toxin B subunit (CTB)-treated versus untreated cell membrane samples, whose spectra cannot be statistically differentiated, the optimized multiscale CNN achieved superior performance compared to traditional machine learning methods and existing multiscale CNNs, with accuracy (99.22%), sensitivity (99.27%), specificity (99.16%), and precision (99.20%). Our new visualization method, based on gradients of activation maps with respect to class scores, generates saliency scores that capture sample variations, with decision-making relying on consistently identified peak features. By visualizing the effects of different kernel sizes, Grad-AM highlights features at varying scales, aligning closely with spectral features and enhancing CNN interpretability in complex biomolecular analysis. These advancements demonstrate the potential of our method to improve spectral analysis and reveal previously hidden peaks in complex biological environments.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"6 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783019","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 : 2025-04-04DOI: 10.1021/acssensors.4c02896
Glauco Meireles Mascarenhas Morandi Lustosa, Agnes Nascimento Simões, Eugênio de Souza Morita, André Nunes de Souza, Floriano Torres Neto, Waldir Antonio Bizzo, Talita Mazon
The current research aims to synthesize zinc oxide decorated with palladium nanoparticles and develop a stable sensor with high sensitivity to hydrogen gas dissolved in oil. ZnO nanorods (NR) were synthesized by a hydrothermal method directly onto a commercial sensor board with gold interdigital electrodes, followed by functionalization with Pd nanoparticles (NP) by drop casting. SEM images show ZnO NRs with an average diameter of ∼220 nm and Pd spherical NPs with diameters of 35–75 nm. Finally, the sensing properties were examined by immersing the sensor into insulating mineral oil in a closed system, where different H2 concentrations (from 0 up to 500 ppm) were injected into the headspace and then dissolved in the mineral oil, according to the Ostwald coefficient. All measurements were carried out at room temperature. The electrical characterization showed that our sensor had good repeatability, stability, and sensitivity to detect lower concentrations (less than 10 ppm). Additionally, a nanoengineered porous layer of PDMS was prepared over the sensor board through spin coating and heat treatment, and then the sensitivity of our sensor board reached ∼2.8 ppm of H2 gas. Our findings indicate that the methodology applied improves gas detection performance in industrial applications and its potential use for real-time monitoring.
{"title":"Nanoengineered PDMS/Pd/ZnO-Based Sensor to Improve Detection of H2 Dissolved Gas in Oil at Room Temperature","authors":"Glauco Meireles Mascarenhas Morandi Lustosa, Agnes Nascimento Simões, Eugênio de Souza Morita, André Nunes de Souza, Floriano Torres Neto, Waldir Antonio Bizzo, Talita Mazon","doi":"10.1021/acssensors.4c02896","DOIUrl":"https://doi.org/10.1021/acssensors.4c02896","url":null,"abstract":"The current research aims to synthesize zinc oxide decorated with palladium nanoparticles and develop a stable sensor with high sensitivity to hydrogen gas dissolved in oil. ZnO nanorods (NR) were synthesized by a hydrothermal method directly onto a commercial sensor board with gold interdigital electrodes, followed by functionalization with Pd nanoparticles (NP) by drop casting. SEM images show ZnO NRs with an average diameter of ∼220 nm and Pd spherical NPs with diameters of 35–75 nm. Finally, the sensing properties were examined by immersing the sensor into insulating mineral oil in a closed system, where different H<sub>2</sub> concentrations (from 0 up to 500 ppm) were injected into the headspace and then dissolved in the mineral oil, according to the Ostwald coefficient. All measurements were carried out at room temperature. The electrical characterization showed that our sensor had good repeatability, stability, and sensitivity to detect lower concentrations (less than 10 ppm). Additionally, a nanoengineered porous layer of PDMS was prepared over the sensor board through spin coating and heat treatment, and then the sensitivity of our sensor board reached ∼2.8 ppm of H<sub>2</sub> gas. Our findings indicate that the methodology applied improves gas detection performance in industrial applications and its potential use for real-time monitoring.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"58 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775816","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 : 2025-04-04DOI: 10.1021/acssensors.4c03750
Yongsheng Yang, Qinqi Ren, Zixuan Zhang, Dexing Liu, Yang Zhu, Yufeng Jin, Min Zhang
Flexible and stretchable sensors have garnered significant attention in the fields of human–computer interaction, motion capture, and health monitoring. Presently, most sensors are limited to capturing motion in a single direction and lack the capability to analyze multidirectional deformations in real world. A single device capable of detecting multidirectional deformations has always been a high expectation and a daunting challenge. In this work, we realize the idea of using a single sensor for multidirectional sensing by adopting a “one-step” rolling process to transfer vertically aligned carbon nanotubes grown on a silicon wafer onto a flexible Ecoflex substrate. The entire preparation process is simple and efficient. Distinct conductive paths form along different directions controlled by the rolling process and the pattern design of carbon nanotubes, thus resulting in a sensitive directional dependence. The sensor exhibits remarkable performance, including a wide operating range (0–120%), high sensitivity (GF = 126.6), short response time (64 ms), and good stability (over 4000 cycles under strain 40%). The sensors are demonstrated for detecting motion signals and monitoring human health, ranging from subtle motion signals to large deformation. These sensor characteristics fulfill the requirements of various practical scenarios and have an immense potential for applications in human–computer interaction interfaces, intelligent robots, and in situ health monitoring.
{"title":"A Strain Sensor for Multidirectional Deformation Detection Realized by Rolling Patterned Vertically Aligned Carbon Nanotubes","authors":"Yongsheng Yang, Qinqi Ren, Zixuan Zhang, Dexing Liu, Yang Zhu, Yufeng Jin, Min Zhang","doi":"10.1021/acssensors.4c03750","DOIUrl":"https://doi.org/10.1021/acssensors.4c03750","url":null,"abstract":"Flexible and stretchable sensors have garnered significant attention in the fields of human–computer interaction, motion capture, and health monitoring. Presently, most sensors are limited to capturing motion in a single direction and lack the capability to analyze multidirectional deformations in real world. A single device capable of detecting multidirectional deformations has always been a high expectation and a daunting challenge. In this work, we realize the idea of using a single sensor for multidirectional sensing by adopting a “one-step” rolling process to transfer vertically aligned carbon nanotubes grown on a silicon wafer onto a flexible Ecoflex substrate. The entire preparation process is simple and efficient. Distinct conductive paths form along different directions controlled by the rolling process and the pattern design of carbon nanotubes, thus resulting in a sensitive directional dependence. The sensor exhibits remarkable performance, including a wide operating range (0–120%), high sensitivity (GF = 126.6), short response time (64 ms), and good stability (over 4000 cycles under strain 40%). The sensors are demonstrated for detecting motion signals and monitoring human health, ranging from subtle motion signals to large deformation. These sensor characteristics fulfill the requirements of various practical scenarios and have an immense potential for applications in human–computer interaction interfaces, intelligent robots, and in situ health monitoring.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"68 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783020","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.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}
Pub Date : 2025-04-03DOI: 10.1021/acssensors.5c00184
Yuanxiang Li, Ju Luo, Ferdinand Ndikuryayo, Yuxuan Chen, Guozhen Liu, Wen-Chao Yang
Abiotic stress poses significant challenges to the ecological environment and global food security. Early and accurate diagnosis of abiotic stress is essential for modern agriculture. Recently, fluorescence sensing technology has emerged as a valuable tool for monitoring abiotic stress due to its ease of use and capability for spatiotemporal visualization. These probes specifically bind to abiotic stress biomarkers, facilitating the detection of stress responses and advancing related biological research. However, there is a lack of comprehensive reviews on fluorescence probe for abiotic stress, which limits progress in this area. This review outlines the biological markers of abiotic stress, discusses the types and design principles of fluorescence probe, and reviews research on detecting plant responses to such stress. Its goal is to inspire the rational design of fluorescence probe for plant bioimaging, promote early diagnosis of abiotic stress, and enhance the understanding of plant defense mechanisms at the molecular level, ultimately providing a scientific basis for stress management in agriculture.
{"title":"Advances in Fluorescence-based Probes for Abiotic Stress Detection in Plants","authors":"Yuanxiang Li, Ju Luo, Ferdinand Ndikuryayo, Yuxuan Chen, Guozhen Liu, Wen-Chao Yang","doi":"10.1021/acssensors.5c00184","DOIUrl":"https://doi.org/10.1021/acssensors.5c00184","url":null,"abstract":"Abiotic stress poses significant challenges to the ecological environment and global food security. Early and accurate diagnosis of abiotic stress is essential for modern agriculture. Recently, fluorescence sensing technology has emerged as a valuable tool for monitoring abiotic stress due to its ease of use and capability for spatiotemporal visualization. These probes specifically bind to abiotic stress biomarkers, facilitating the detection of stress responses and advancing related biological research. However, there is a lack of comprehensive reviews on fluorescence probe for abiotic stress, which limits progress in this area. This review outlines the biological markers of abiotic stress, discusses the types and design principles of fluorescence probe, and reviews research on detecting plant responses to such stress. Its goal is to inspire the rational design of fluorescence probe for plant bioimaging, promote early diagnosis of abiotic stress, and enhance the understanding of plant defense mechanisms at the molecular level, ultimately providing a scientific basis for stress management in agriculture.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"62 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775818","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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