ACS Sensors最新文献

{"title":"Analyzing Functionalized Carbon Quantum Dots in Electrode Engineering for the Precise Electrochemical Detection of a Neurotransmitter: Achieving Laboratory Accuracy with Portable Sensors","authors":"Kruthi K. Rao, Bhanu Prakash","doi":"10.1021/acssensors.4c03298","DOIUrl":"https://doi.org/10.1021/acssensors.4c03298","url":null,"abstract":"Point-of-care testing (POCT) devices are revolutionizing health monitoring by enabling rapid and accessible testing outside conventional laboratory environments. However, achieving laboratory-level accuracy in these portable devices continues to pose a significant challenge. This study introduces e-Patram, an innovative paper-based POCT sensor specifically designed for the precise and efficient monitoring of dopamine (DA) in urine, a critical biomarker for neurological health. Regular monitoring of DA is essential for identifying fluctuations that may signal neurodegenerative conditions, such as Parkinson’s disease, and e-Patram aspires to facilitate early detection through noninvasive testing. The sensor utilizes electrochemical methods to attain high selectivity for DA, even in the presence of common interfering substances like ascorbic acid (AA) and uric acid (UA), which are often present in urine and can complicate electrochemical detection due to the close value of their redox potential with DA. By overcoming these interference challenges with the modification of the electrode, e-Patram provides reliable DA measurements with accuracy comparable to the standard “catecholamine test.” Extensive validation with human urine samples confirms the device’s precision and consistency, underscoring its potential as a practical and accessible tool for routine DA monitoring in both clinical and everyday settings.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"128 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862244","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}

{"title":"Chemical Sensors and Biosensors for Point-of-Care Testing of Pets: Opportunities for Individualized Diagnostics of Companion Animals","authors":"Wilson Tiago Fonseca, Tatiana Parra Vello, Gabrielle Coelho Lelis, Ana Vitória Ferreira Deleigo, Regina Kiomi Takahira, Diego Stéfani Teodoro Martinez, Rafael Furlan de Oliveira","doi":"10.1021/acssensors.4c03664","DOIUrl":"https://doi.org/10.1021/acssensors.4c03664","url":null,"abstract":"Point-of-care testing (POCT) is recognized as one of the most disruptive medical technologies for rapid and decentralized diagnostics. Successful commercial examples include portable glucose meters, pregnancy tests, and COVID-19 self-tests. However, compared to advancements in human healthcare, POCT technologies for companion animals (pets) remain significantly underdeveloped. This Review explores the latest advancements in pet POCT and examines the challenges and opportunities in the field for individualized diagnostics of cats and dogs. The most frequent diseases and their respective biomarkers in blood, urine, and saliva are discussed. We examine key strategies for developing the next-generation POCT devices by harnessing the potential of selective (bio)receptors and high-performing transducers such as lateral flow tests and electrochemical (bio)sensors. We also present the most recent research initiatives and the successful commercial pet POCT technologies. We discuss future trends in the field, such the role of biomarker discovery and development of wearable, implantable, and breath sensors. We believe that advancing pet POCT technologies benefits not only animals but also humans and the environment, supporting the One Health approach.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"108 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857461","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}

{"title":"Electrochemically Active Materials for Tissue-Interfaced Soft Biochemical Sensing","authors":"Xiaoyan Qian, Zehua Chen, Feng Zhang, Zheng Yan","doi":"10.1021/acssensors.5c00029","DOIUrl":"https://doi.org/10.1021/acssensors.5c00029","url":null,"abstract":"Tissue-interfaced soft biochemical sensing represents a crucial approach to personalized healthcare by employing electrochemically active materials to monitor biochemical signals at the tissue interface in real time, either noninvasively or through implantation. These soft biochemical sensors can be integrated with various biological tissues, such as neural, gastrointestinal, ocular, cardiac, skin, muscle, and bone, adapting to their unique mechanical and biochemical environments. Sensors employing materials like conductive polymers, composites, metals, metal oxides, and carbon-based nanomaterials have demonstrated capabilities in applications, such as continuous glucose monitoring, neural activity mapping, and real-time metabolite detection, enhancing diagnostics and treatment monitoring across a range of medical fields. Next-generation tissue-interfaced biosensors that enable multimodal and multiplexed measurement of biochemical markers and physiological parameters could be transformative for personalized medicine, allowing for high-resolution, time-resolved historical monitoring of an individual’s health status. In this review, we summarize current trends in the field to provide insights into the challenges and future trajectory of tissue-interfaced soft biochemical sensors, highlighting their potential to revolutionize personalized medicine and improve patient outcomes.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"219 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853783","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}

{"title":"Toward Graphene Field-Effect Transistor Array with Uniform Sensing Characteristics via a Clean Graphene Transfer Process","authors":"Xiaoqing Qi, Jingtao Chen, Kaicong Liu, Hongru Ma, Qi Shu, Donglin Ma, Teng Gao","doi":"10.1021/acssensors.4c02816","DOIUrl":"https://doi.org/10.1021/acssensors.4c02816","url":null,"abstract":"The synthesis of uniform, low-defect graphene on copper foil is approaching an industrial scale. However, its practical application remains challenging due to the lack of an appropriate method for its clean transfer to a device substrate. In this study, we demonstrate the use of a lift-off resist (LOR) photoresist as a transfer-supporting layer, resulting in a truly clean transfer of graphene. The surface cleanliness of graphene was assessed through optical microscopy, atomic force microscopy, and Raman spectroscopy. The uniform sensing characteristics of the cleanly transferred graphene were further evidenced by the first-ever implementation of high-throughput graphene field-effect transistors, distinct from those covered with a thin layer of amorphous carbon, such as residual poly(methyl methacrylate). This transfer method provides a novel alternative route for graphene transfer.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"108 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849919","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}

{"title":"Stable Magnetic Relaxation Switch Sensor Based on Fe3O4@Gel for Ultrafast Detection of Cd2+","authors":"Li Yao,&nbsp;Yudie Hu,&nbsp;Xingyu Yang,&nbsp;Shaoyi Yu,&nbsp;Liguang Xu,&nbsp;Wei Chen,&nbsp;Jia Tu,&nbsp;Yunhui Cheng and Zhou Xu*,&nbsp;","doi":"10.1021/acssensors.4c0355210.1021/acssensors.4c03552","DOIUrl":"https://doi.org/10.1021/acssensors.4c03552https://doi.org/10.1021/acssensors.4c03552","url":null,"abstract":"<p >To overcome the dual challenges of signal instability and prolonged detection in conventional magnetic relaxation switching (MRS) systems, a novel Fe₃O₄-encapsulated alginate hydrogel nanocomposite (Fe₃O₄@Gel) sensor was designed for rapid screening of the cadmium ion. Compared with the traditional Fe₃O₄-based sensors, the Fe₃O₄ was embedded in the gel network framework to avoid magnetic field-induced aggregation, which helped to improve the stability of MRS. On the other hand, compared with MRS based on gel, the Fe₃O₄ accelerated the relaxation process of water molecules inside the gel, obtaining a fast detection time of the sensor within 38 s, which is one-fifth of the detection time of the traditional magnetic relaxation switch sensor with pure hydrogel of 191 s. Mechanistically, target-induced immunocomplex formation modulates alkaline phosphatase activity, triggering cascade enzymatic reactions that precisely regulate hydrogel swelling dynamics. This stimuli-responsive behavior translates quantitative Cd²⁺ concentrations into reproducible transverse relaxation time (<i>T</i>₂) signal shifts (<i>R</i>² = 0.987), achieving sub-ppt sensitivity (6 pg/mL) across linearity (0.01–10 ng/mL). Practical validation in complex matrices demonstrated 96.62%–109.97% spike recoveries. This multifunctional nanoplatform establishes a new paradigm for high-fidelity, field-deployable hazard screening in complex systems.</p>","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"10 4","pages":"2802–2811 2802–2811"},"PeriodicalIF":8.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867360","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}

{"title":"Stable Magnetic Relaxation Switch Sensor Based on Fe3O4@Gel for Ultrafast Detection of Cd2+","authors":"Li Yao, Yudie Hu, Xingyu Yang, Shaoyi Yu, Liguang Xu, Wei Chen, Jia Tu, Yunhui Cheng, Zhou Xu","doi":"10.1021/acssensors.4c03552","DOIUrl":"https://doi.org/10.1021/acssensors.4c03552","url":null,"abstract":"To overcome the dual challenges of signal instability and prolonged detection in conventional magnetic relaxation switching (MRS) systems, a novel Fe₃O₄-encapsulated alginate hydrogel nanocomposite (Fe₃O₄@Gel) sensor was designed for rapid screening of the cadmium ion. Compared with the traditional Fe₃O₄-based sensors, the Fe₃O₄ was embedded in the gel network framework to avoid magnetic field-induced aggregation, which helped to improve the stability of MRS. On the other hand, compared with MRS based on gel, the Fe₃O₄ accelerated the relaxation process of water molecules inside the gel, obtaining a fast detection time of the sensor within 38 s, which is one-fifth of the detection time of the traditional magnetic relaxation switch sensor with pure hydrogel of 191 s. Mechanistically, target-induced immunocomplex formation modulates alkaline phosphatase activity, triggering cascade enzymatic reactions that precisely regulate hydrogel swelling dynamics. This stimuli-responsive behavior translates quantitative Cd²⁺ concentrations into reproducible transverse relaxation time (<i>T</i>₂) signal shifts (<i>R</i>² = 0.987), achieving sub-ppt sensitivity (6 pg/mL) across linearity (0.01–10 ng/mL). Practical validation in complex matrices demonstrated 96.62%–109.97% spike recoveries. This multifunctional nanoplatform establishes a new paradigm for high-fidelity, field-deployable hazard screening in complex systems.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"17 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847016","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}

{"title":"Ultrasensitive Room-Temperature NO2 Gas Sensor Based on MXene–Cu2O Composites","authors":"Wenbin Ren, Jinfeng Luan, Liang Yin, Huijuan Chen, Changchun Wang, Pinhua Zhang, Guangliang Cui, Li Lv","doi":"10.1021/acssensors.5c00215","DOIUrl":"https://doi.org/10.1021/acssensors.5c00215","url":null,"abstract":"The development of real-time trace-level NO₂ quantification platforms that can be operated at room temperature constitutes a critical advancement for occupational safety and public health monitoring systems. This study demonstrates a room-temperature NO₂ sensor using MXene–Cu₂O composites prepared via a hydrothermal method. Systematic evaluation of MXene-introduced effects identified the 0.84 wt % MXene–Cu₂O composite as optimal, exhibiting 4-fold enhanced sensitivity and shorter response (55 s)/recovery (35 s) time compared to pure Cu₂O. Additionally, the sensor exhibits a low detection limit (10 ppb), high selectivity, great reversibility, and long-term stability. The enhanced sensing performance originates from precisely engineered interfacial architectures between MXene and Cu₂O, which effectively adjust the charge-transfer behavior through the conduction tunnel in the sensing material. Furthermore, oxygen vacancy engineering creates defect-mediated adsorption centers that promote selective NO₂ chemisorption through charge polarization effects. This research offers a novel strategy for designing optimized structures to enhance the sensitivity of MOS-based materials for NO₂ gas detection.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"7 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849916","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}

{"title":"Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models","authors":"Reza Mohammadi Dashtaki, Saeed Mohammadi Dashtaki, Esmaeil Heydari-Bafrooei, Md Jalil Piran","doi":"10.1021/acssensors.5c00364","DOIUrl":"https://doi.org/10.1021/acssensors.5c00364","url":null,"abstract":"The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approaches to enhance sensitivity, selectivity, and reliability. Machine learning (ML) techniques facilitate the analysis and predictive modeling of sensor performance by establishing quantitative relationships between parameters and their effects. This work presents a case study on developing a molecularly imprinted polymer (MIP)-based sensor for detecting doxorubicin (Dox), emphasizing the use of ML-based ensemble models to improve performance and reliability. Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), are used to evaluate the effect of each parameter on prediction performance, using the SHapley Additive exPlanations (SHAP) method to determine feature importance. Based on the analysis, removing a less influential feature and introducing a new feature significantly improved the model’s predictive capabilities. By applying the min–max scaling technique, it is ensured that all features contribute proportionally to the model learning process. Additionally, multiple ML models─Linear Regression (LR), KNN, DT, RF, Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Support Vector Regression (SVR), XGBoost, Bagging, Partial Least Squares (PLS), and Ridge Regression─are applied to the data set and their performance in predicting the sensor output current is compared. To further enhance prediction performance, a novel ensemble model is proposed that integrates DT, RF, GB, XGBoost, and Bagging regressors, leveraging their combined strengths to offset individual weaknesses. The main benefit of this work lies in its ability to enhance MIP-based sensor performance by developing a novel stacking regressor ensemble model, which improves prediction performance and reliability. This methodology is broadly applicable to the development of other sensors with different transducers and sensing elements. Through extensive simulation results, the proposed stacking regressor ensemble model demonstrated superior predictive performance compared to individual ML models. The model achieved an <i>R</i>-squared (<i>R</i>²) of 0.993, significantly reducing the root-mean-square error (RMSE) to 0.436 and the mean absolute error (MAE) to 0.244. These improvements enhanced sensitivity and reliability of the MIP-based electrochemical sensor, demonstrating a substantial performance gain over individual ML models.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"24 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841611","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}

{"title":"Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models","authors":"Reza Mohammadi Dashtaki,&nbsp;Saeed Mohammadi Dashtaki,&nbsp;Esmaeil Heydari-Bafrooei* and Md Jalil Piran*,&nbsp;","doi":"10.1021/acssensors.5c0036410.1021/acssensors.5c00364","DOIUrl":"https://doi.org/10.1021/acssensors.5c00364https://doi.org/10.1021/acssensors.5c00364","url":null,"abstract":"<p >The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approaches to enhance sensitivity, selectivity, and reliability. Machine learning (ML) techniques facilitate the analysis and predictive modeling of sensor performance by establishing quantitative relationships between parameters and their effects. This work presents a case study on developing a molecularly imprinted polymer (MIP)-based sensor for detecting doxorubicin (Dox), emphasizing the use of ML-based ensemble models to improve performance and reliability. Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), are used to evaluate the effect of each parameter on prediction performance, using the SHapley Additive exPlanations (SHAP) method to determine feature importance. Based on the analysis, removing a less influential feature and introducing a new feature significantly improved the model’s predictive capabilities. By applying the min–max scaling technique, it is ensured that all features contribute proportionally to the model learning process. Additionally, multiple ML models─Linear Regression (LR), KNN, DT, RF, Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Support Vector Regression (SVR), XGBoost, Bagging, Partial Least Squares (PLS), and Ridge Regression─are applied to the data set and their performance in predicting the sensor output current is compared. To further enhance prediction performance, a novel ensemble model is proposed that integrates DT, RF, GB, XGBoost, and Bagging regressors, leveraging their combined strengths to offset individual weaknesses. The main benefit of this work lies in its ability to enhance MIP-based sensor performance by developing a novel stacking regressor ensemble model, which improves prediction performance and reliability. This methodology is broadly applicable to the development of other sensors with different transducers and sensing elements. Through extensive simulation results, the proposed stacking regressor ensemble model demonstrated superior predictive performance compared to individual ML models. The model achieved an <i>R</i>-squared (<i>R</i>²) of 0.993, significantly reducing the root-mean-square error (RMSE) to 0.436 and the mean absolute error (MAE) to 0.244. These improvements enhanced sensitivity and reliability of the MIP-based electrochemical sensor, demonstrating a substantial performance gain over individual ML models.</p>","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"10 4","pages":"3123–3133 3123–3133"},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867297","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}

{"title":"Lychee-like Bi2MoO6 Spheres for Highly Sensitive Room-Temperature Phosphine Sensing","authors":"Xiaoxi He, Jinyong Xu, Yixiang Bian, Jean-Marc Tulliani, Chao Zhang","doi":"10.1021/acssensors.4c03135","DOIUrl":"https://doi.org/10.1021/acssensors.4c03135","url":null,"abstract":"The increasing use of phosphine in various industries demands the development of reliable sensors. However, progress in this area has been slow, particularly for room-temperature detection. In this study, bismuth molybdate microspheres with a lychee-like structure (Lyc-Bi₂MoO₆) were prepared via a one-step solvothermal method, which can be employed for the detection of trace concentrations of phosphine. The solvent used was a mixture of isopropanol and ethylene glycol in a 3:1 ratio. Various characterization techniques and gas sensing performance tests demonstrated that Lyc-Bi₂MoO₆ is a potential phosphine-sensing material for room-temperature application. Sensing performance tests revealed that Lyc-Bi₂MoO₆ exhibited an impressive ability to detect trace concentrations of phosphine, with a practical detection limit as low as 150 ppb (response = 8.11), rapid response (around 1 min), and excellent long-term stability (a maximum response attenuation of 9.46% over 10 weeks). Density functional theory calculations further aided in the in-depth analysis and interpretation of the behavior and response mechanism of phosphine on the surface of Lyc-Bi₂MoO₆.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"8 1","pages":""},"PeriodicalIF":8.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841614","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}