Development of Microplastic Identification using Raman Microspectrophotometry
Bibliographic record
Abstract
Microplastics are microscopic particles that range from 0.1µm - 5mm that originate from a variety of sources such as larger plastics that degrade into smaller pieces, microbeads from beauty products, and the fibers from clothes that come off when washing them. Most microplastics end up in the oceans and freshwaters and has the possibility of persisting and affecting the aquatic ecosystem globally. Due to their minute size and unknown or altered composition, microplastics have been difficult to study and there is still no general method to determine their chemical identity. The most common technique involves the visual identification of the microplastics by microscopy. This technique can sometimes lead to misidentification due to the altered composition of the microplastic or the difficulty in differentiating plastics from natural particles. In this study, further verification of the microplastic composition is done using Raman Microspectrophotometry, which allows a direct measurement on the microplastic particles, to further identify their chemical composition and differentiate them from other organic or natural particles. Different variables on the Raman Microspectrophotometry such as laser power, laser wavelength, spectral integration time, and the number of co-additions are optimized to provide the best spectra for microplastic identification. This approach can help identify and quantify the microplastics present in the environment and will be applied to the samples from the North Saskatchewan River and the Arctic. Discipline: Chemistry Faculty Mentor: Dr. Matthew Ross
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".