In-situ and real-time detection of micro/nanoplastics in water: Combining laboratory experiments and modelling studies for plastic life cycle analysis
Bibliographic record
Abstract
Maritime micro/nanoplastic research provides valuable insights into oceanic plastic waste remediation. Yet, there is a notable disparity, with micro/nanoplastic research in freshwater being ~ 85% less extensive than that in seawater. Observational studies suggest that over 1000 rivers contribute to ~ 80% of the global riverine plastic input into the oceans. Understanding the presence of micro/nanoplastics in freshwater systems is essential for unraveling the global micro/nanoplastic cycle.In our laboratory, a cutting-edge nano-digital inline holographic microscope (nano-DIHM) was developed for real-time and in-situ micro- and nanoplastic research, including physicochemical characteristics, coatings, and dynamic behaviours in freshwater systems. The nano-DIHM data revealed distinct intensity and optical phase patterns of various types of single particles and clusters of micro/nanoplastics (PE, PP, PS, PET, PVC, and PUR), along with other organics (oleic acid), inorganics (magnetite), and biological materials (phytoplankton). We further incorporated a deep neural network functionality to nano-DIHM for rapid micro/nanoplastic detection in real-environmental waters. With its 4D (3D + time) tracking capability, we utilized nano-DIHM to measure the sedimentation (settling and floating) velocity of plastics in two size categories in water. The experimental results were subsequently integrated into a numerical model (CaMPSim-3D) developed at the National Research Council Canada to simulate the transport of plastic particles in Canadian rivers. Complementary modelling results demonstrated distinct distribution and accumulation patterns of macro-, micro-, and nanoplastic particles in aquatic systems, establishing nano-DIHM a powerful approach for plastic life-cycle analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".