Novel Dynamic Technique, Nano-DIHM, for Rapid Detection of Oil, Heavy Metals, and Biological Spills in Aquatic Systems
Bibliographic record
Abstract
real-time analysis of particle size, shape, and phase. Nano-DIHM enables 4D tracking of particles in water and their transformations in three-dimensional space. We demonstrate that nano-DIHM can be automated to detect and track oil spills/oil droplets in dynamic systems. We provide evidence that nano-DIHM can detect the MS2 bacteriophage as a representative biological-viral material and mercury-containing particles alongside other heavy metals as common toxic contaminants. Nano-DIHM shows the capability of observation of combined materials in water, characterizing the interactions of various particles in mixtures, and particles with different coatings in a suspension. The observed sizes of the particles and droplets ranged from ∼1 to 200 μm. We herein demonstrate the ability of nano-DIHM to characterize and distinguish particle-based contaminants in water and their interactions in both stationary and dynamic modes with a 62.5 millisecond time resolution. The fully automated software for dynamic and real-time detection of contaminants will be of global significance. A comparison is also made between nano-DIHM and established techniques such as S/TEM for their different capabilities. Nano-DIHM can provide a range of physicochemical information in stationary and dynamic modes, allowing life cycle analysis of diverse particle contaminants in different aquatic systems, and serve as an effective tool for rapid response for spills and remediation of natural waters.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".