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Papers
61,005 resultsShowing papers similar to Advances in the Development of Innovative Sensor Platforms for Field Analysis
ClearChemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies
This review examines the use of mobile platforms such as smartphones and drones equipped with chemical sensors for environmental monitoring, including air pollution and industrial waste detection. Researchers surveyed various sensor technologies and how machine learning is being applied to improve the accuracy of chemical detection from portable devices. While broadly focused on chemical sensing, the work is relevant to developing field-deployable monitoring tools for environmental contaminants including microplastic-associated pollutants.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
Recent developments on aerial lab-on-a-drone platforms for remote environmental monitoring: A review.
This review surveys recent advances in drone-mounted 'lab-on-a-drone' platforms designed for remote detection of environmental contaminants including microplastics, volatile organic compounds, heavy metals, and pesticides. Researchers examine sensing materials, miniaturised analytical systems, and deployment strategies that enable spatial and temporal mapping of pollutant distributions in water and air.
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring
This review examines advances in sensor technology, autonomous deployment methods, and artificial intelligence for monitoring water quality in real time across rivers, lakes, and oceans. Researchers describe how networks of sensors on robotic boats, buoys, and drones can now measure physical, chemical, and biological water parameters more broadly than ever before. The study proposes that connecting water monitoring systems globally could help address challenges related to drinking water safety, aquaculture, and emerging contaminants like microplastics.
Integrated Approaches to Water Quality Assessment and Treatment: A Comprehensive Review
This comprehensive review integrates physical, chemical, and biological water quality parameters, examines major pollution sources including emerging contaminants like microplastics, and surveys advances in real-time IoT-enabled monitoring and integrated treatment approaches.
Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges
This review covers advances in tiny sensors built with micro- and nanotechnology that can detect pollutants in air, water, soil, and food, as well as diagnose diseases. These sensor technologies are relevant to microplastic research because they could enable faster and more sensitive detection of plastic particles in environmental and biological samples.
Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
Researchers explored the integration of IoT sensor technologies for detecting and monitoring microplastics in soil and water environments, proposing a connected sensing framework for real-time environmental surveillance. The system enables automated data collection and remote monitoring of microplastic contamination.
Monitoring Water Quality: Suggestions and Prospects
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Monitoring Water Quality: Suggestions and Prospects
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
Field-Portable dissolved gas sensing and perspectives in aqueous microplastic detection
This paper reviews portable sensor technologies for detecting dissolved gases in water and discusses their potential application for monitoring microplastic pollution in aquatic environments. Developing compact, field-deployable detection tools is critical for improving real-time environmental monitoring.
Residential Environment Pollution Monitoring System Based on Cloud Computing and Internet of Things
A cloud computing and Internet of Things-based system was developed to monitor multiple environmental parameters in residential areas simultaneously, including air and water quality. The system improved real-time data collection and analysis compared to traditional single-factor monitors. Integrated monitoring platforms can help communities detect and respond to pollution events more effectively.
Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques
This review examines emerging field-portable technologies for detecting and quantifying microplastics in aqueous environments, discussing optical, spectroscopic, and electrochemical sensing approaches. Researchers identify the lack of a standardized, rapid on-site method as the primary bottleneck limiting accurate real-world microplastic monitoring.
Eco-Sensing System for Water Pollution and Microplastic Detection
This study evaluates new sensor-based and spectroscopic technologies for detecting microplastics in water in real time, comparing them with traditional lab-based methods. The portable systems showed improved accuracy and efficiency for field use, making it possible to monitor microplastic contamination as it happens. Better detection tools are essential for protecting drinking water sources and understanding the true scale of human microplastic exposure.
Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments
This paper proposes using Internet of Things (IoT) sensors to build a real-time monitoring network for microplastics in aquatic environments. Automated, continuous monitoring systems could provide much better spatial and temporal coverage than current sampling-based approaches.
Advances and innovations in machine learning-based spectral detection methods for trace organic pollutants
This review examines recent advances in machine learning applications for spectral detection of trace organic pollutants in water, covering techniques such as generative adversarial networks for data augmentation, intelligent feature extraction, and model construction across multiple spectral methods. The authors highlight how ML integration overcomes the limitations of traditional detection approaches for large-scale, real-time environmental monitoring.
How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
This review examines how artificial intelligence methods including machine learning and deep learning are being integrated with nanophotonic sensor platforms to enhance environmental monitoring capabilities, with applications including microplastic and contaminant detection in portable, real-time systems.
Enhancing Agricultural Sustainability Through Robotic-IoT Systems for Real-Time Monitoring Soil Contamination
Researchers developed an IoT-based robotic system integrating portable NIR spectroscopy sensors and machine learning, including a Random Forest algorithm, to monitor soil quality and detect microplastic contamination in real time, achieving 96% accuracy in microplastic detection and 91% accuracy in broader pollutant analysis.
Biosensors and technological advances in monitoring marine pollution in the USA
This review examines how biosensor technology is advancing marine pollution monitoring in the United States, including detection of heavy metals, hydrocarbons, and microplastics. Researchers highlight innovations integrating nanotechnology, wireless communication, and artificial intelligence into biosensor design for real-time, in-field pollution detection. The study assesses current applications in government programs led by NOAA and the EPA while identifying technical and regulatory challenges that need to be addressed.
Untethered Micro/Nanorobots for Remote Sensing: Toward Intelligent Platform
Researchers reviewed recent advances in tiny wirelessly-controlled robots (micro/nanorobots) designed to detect substances in complex environments, such as inside the body or in contaminated water, using motion, light, and chemical signals for sensing. These miniature devices could eventually enable real-time detection of pollutants like microplastics or disease markers in places that conventional sensors cannot reach.
Recent Trends in Chemical Sensors for Detecting Toxic Materials
This review covers recent advances in chemical sensors for detecting toxic substances, including improvements in nanomaterial-based, optical, and electrochemical sensing technologies. While focused broadly on toxic chemical detection, the sensor technologies described are increasingly being adapted to detect microplastics in environmental samples. Better detection tools are essential for understanding how much microplastic contamination exists in water, food, and air.
Developing Low-Cost In-Situ Water Pollution Sensors
Researchers reviewed low-cost in-situ sensor technologies for detecting water pollutants including heavy metals, pharmaceuticals, and emerging contaminants, evaluating their sensitivity, selectivity, and feasibility for deployment in resource-limited settings.
Detecting Chemical Contaminants in Water Using AI
This review examines how artificial intelligence and machine learning tools are being applied to detect chemical contaminants in water, including microplastics, covering sensor technologies, data processing approaches, and the potential for real-time monitoring systems.
Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review
This review examined the integration of Internet of Things sensors and edge computing for precision agriculture applications, including real-time soil quality monitoring and heavy metal detection, evaluating current technologies and identifying challenges for practical farm-scale deployment.