ArticleEnvironmental science & technology2024
Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection.
Article in Environmental science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- From Aquatic Pollution to Drinking-Water Exposure: Analytical Challenges in Detecting Nanoplastics in Drinking Water-A PRISMA-Guided Review.Molecules (Basel, Switzerland) · 2026Review
- Molecular Fingerprinting for Source Attribution of Nanoplastics in Drinking-Water Systems.Molecules (Basel, Switzerland) · 2026Review
- Optical, Chemical, and Biological Detection Methods of Microplastics and Nanoplastics.ACS measurement science au · 2026Review
- Simultaneous measurements of microplastic 3D morphology and naphthalene adsorption based on digital holographic microscopy.Scientific reports · 2025Article
- Novel AI technology for 4DRSC advances · 2025Article
- Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning.ACS sensors · 2025Article
- Quantifying Nanoplastic Toxicity Using Gold-Core Polystyrene Nanoparticles: In vivo Evaluation and Human Risk Extrapolation.International journal of nanomedicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively. Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient. It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics. Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data. Complementary modeling demonstrates nano- and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.