AI Article Synopsis
- The study investigated how a MoS-functionalized paper sensor detects dopamine using a combination of theoretical analysis, experiments, and machine learning for predictive modeling.
- The effectiveness of the 2D material for dopamine detection was supported by density functional theory, revealing how dopamine interacts with MoS through physisorption and electrostatic forces.
- The sensor demonstrated high performance in detecting dopamine with over 96% accuracy in serum samples, and the addition of a machine-learning web app interface boosted accuracy to nearly 99%.
Article Abstract
The sensing behavior of a MoS-functionalized paper sensor towards dopamine was explored through a combinatorial approach of theoretical analysis, subsequent experimental validation, and machine-learning-driven predictive modeling of the measured electrochemical outputs. The suitability of the chosen 2D material for efficient detection of dopamine was confirmed using density functional theory. The physisorption behavior along with electrostatic interaction due to the incorporation of dopamine on MoS was unraveled under the purview of theoretically estimated noncovalent interaction and charge density difference plot. The theoretical Löwdin population analysis elucidates the alteration in oxidation potential of dopamine, as observed in electrochemical experiments. The electrochemical responses of the developed sensor with the spiked serum samples showed an average accuracy of more than 96% with a limit of detection of 10 nM. Furthermore, implementation of a machine-intelligent interactive web app interface improved the resolution of the sensing platform significantly with an enhanced accuracy of nearly 99%.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acsami.3c03899 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage.
Sensors (Basel)
January 2025
Department of Structures for Engineering and Architecture, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage.
View Article and Find Full Text PDFEnergy-Efficient and Trust-Based Autonomous Underwater Vehicle Scheme for 6G-Enabled Internet of Underwater Things.
Sensors (Basel)
January 2025
Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518000, China.
This paper introduces a novel energy-efficient lightweight, void hole avoidance, localization, and trust-based scheme, termed as Energy-Efficient and Trust-based Autonomous Underwater Vehicle (EETAUV) protocol designed for 6G-enabled underwater acoustic sensor networks (UASNs). The proposed scheme addresses key challenges in UASNs, such as energy consumption, network stability, and data security. It integrates a trust management framework that enhances communication security through node identification and verification mechanisms utilizing normal and phantom nodes.
View Article and Find Full Text PDFAn Innovative Linear Wireless Sensor Network Reliability Evaluation Algorithm.
Sensors (Basel)
January 2025
College of Information Science and Engineering, Shenyang University of Technology, Shenyang 110167, China.
In recent years, wireless sensor networks (WSNs) have become a crucial technology for infrastructure monitoring. To ensure the reliability of monitoring services, evaluating the network's reliability is particularly important. Sensor nodes are distributed linearly when monitoring linear structures, such as railway bridges, forming what is known as a Linear Wireless Sensor Network (LWSN).
View Article and Find Full Text PDFAn Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks.
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
View Article and Find Full Text PDFEmpowering People with Disabilities in Smart Homes Using Predictive Informing.
Sensors (Basel)
January 2025
University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, Croatia.
The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user's preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!