0
Article Tier 2 Sign in to save

Recent progress and technological advancements for detection of micro/nano-plastics in the environment

AI summary Read the abstract

This review surveys the latest analytical tools for detecting micro- and nanoplastics across environmental samples, covering imaging, spectroscopy, electrochemical sensors, and artificial intelligence. It highlights how the very small size and chemical complexity of nanoplastics makes detection especially challenging, and discusses how AI integration is improving accuracy and throughput. Advancing detection methods is foundational to understanding the true scale of microplastic contamination and its risks to ecosystems and human health.

Micro and nanoplastics (MNPs) are an omnipresent and analytically challenging contaminant class that has been emerged in aquatic, terrestrial, atmospheric, and alimentary matrices. The diminutive dimensions and wide prevalence of MNPs in complex mixtures render their detection especially challenging. This review evaluates the state-of-the-art analytical strategies for detection of MNPs, classified as per imaging-based approaches, spectroscopy techniques, and other emerging practices such as electrochemical sensing platforms and artificial intelligence (AI) integration. The analysis critically deliberates the impact of the colloidal properties of MNPs on the performance of sensors. The colloid and interface science can help to decode the physicochemical behavior of MNPs, which may aid in sensor design. The integration of AI approaches with different detection modalities possess the capability to extract latent features from noisy spectral and imaging data, enhancing efficiency and scalability of the existing detection techniques. This review also evaluates the capabilities of remote monitoring platforms, including hyperspectral imaging. The performance of these conventional and advanced approaches is critically evaluated for MNP analysis in real-field. The choice of a particular approach is dependent on the type of information required from the sample. The integration of different sensing modalities can help in realization of scalable and real-time monitoring of MNPs in several environmental metrics. Finally, future perspectives are discussed to advance the analytical sensitivity of MNPs detection approaches, along with their harmonization to address the global issue of MNP pollution.

More Papers Like This

Article Tier 2

From Microplastics to Nanoplastics: Critical Advances and Persistent Challenges in Detection and Quantification

AI summary Read the abstract

This review surveys the state of micro- and nanoplastic detection methods, concluding that integrating AI-driven spectral analysis, hyperspectral imaging, and thermogravimetric approaches with conventional techniques is essential to close the accuracy and throughput gaps needed for reliable environmental monitoring and risk assessment.

Article Tier 2

Recent advances and future technologies in nano-microplastics detection

AI summary Read the abstract

Researchers reviewed the latest technologies for detecting microplastics and nanoplastics (tiny plastic particles found even in remote environments), including AI-driven classification and advanced microscopy techniques. As particle sizes shrink, detection becomes harder, and the lack of standardized methods remains a major barrier to understanding their full impact on ecosystems and human health.

Article Tier 2

Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods

AI summary Read the abstract

This review examines current methods for detecting and measuring microplastics in water, soil, and biological samples, including microscopy and spectroscopy techniques. The authors highlight how artificial intelligence could make detection faster and more accurate. Standardized testing methods and better health risk assessments are needed to understand and manage the dangers microplastics pose to human health.

Article Tier 2

Artificial intelligence in microplastic detection and pollution control

AI summary Read the abstract

This review examines how artificial intelligence is being combined with spectroscopy and imaging techniques to dramatically improve the speed and accuracy of microplastic detection in the environment. Better detection methods are essential for tracking the sources and spread of microplastic pollution that ultimately affects human health through contaminated food and water.

Article Tier 2

From Microplastics to Nanoplastics: Critical Advances and Persistent Challenges in Detection and Quantification

AI summary Read the abstract

Researchers reviewed the landscape of analytical methods for detecting microplastics and nanoplastics — including spectroscopy, mass spectrometry, thermal analysis, hyperspectral imaging, and AI-assisted tools — finding that AI-driven spectral analysis and thermogravimetric approaches show the most promise for improving throughput and extending detection to nanoscale particles.

Research digests by email

When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.

Email me about

Share this paper