In-situ and real-time nano/microplastic coatings and dynamics in water using nano-DIHM: A novel capability for the plastic life cycle research
Bibliographic record
Abstract
A novel nano-digital inline holographic microscope (nano-DIHM) was used to advance in-situ and real-time nano/microplastic physicochemical research, such as particle coatings and dynamic processes in water. Nano-DIHM data provided evidence of distinct coating patterns on nano/microplastic particles by oleic acid, magnetite, and phytoplankton, representing organic, inorganic, and biological coatings widely present in the natural surroundings. A high-resolution scanning transmission electron microscopy confirmed nano-DIHM data, demonstrating its nano/microplastic research capabilities. The sedimentation of two plastic size categories was examined: (a) ∼10 to 700 µm, and (b) ∼ 1 to 5 mm. Particle size was the primary factor affecting the sedimentation for studied (a) microplastics and (b) pellets. Two types of silicone rubbers exhibited different sedimentation processes. We also demonstrated that inorganic ions in seawater and oleic acid organic coatings altered the sedimentation velocity of studied plastics by 9 - 13% and 5 - 9%, respectively. Semi-empirical probability functions were developed and incorporated into a numerical model (CaMPSim-3D) to simulate the transport of studied microplastics and pellets in the Saint John River estuary. Water dynamics was the driving force of plastic transport, yet the accumulation of plastics was selectively dependant on particle physicochemical properties such as size and density by ∼ 7%. The usage of nano-DIHM for targeted identification of nano/microplastic hotspots and aquatic plastic wastes remediation were discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".