Fourier-Transform Infrared Spectroscopy of Environmentally Weathered Textile Fabrics for Enhanced Microplastic Identification
Bibliographic record
Abstract
Microplastic pollution in oceans is among the global environmental concerns of our time. Emerging research on ocean environments indicates that microfibers, such as those originating from textiles, are some of the most commonly occurring type of microplastic contaminants. While Fourier-transform infrared spectroscopy (FTIR) is commonly used to identify and characterize pollutant samples obtained from the environment, this identification is challenging because infrared spectra of materials can be modified by exposure to the ocean, air, UV light, and other ambient conditions, in a process referred to as “weathering”. We report preliminary efforts in improving FTIR characterization of microplastics by building a library of infrared spectra of common textile fibers weathered under a selection of ambient conditions. Consumer textile materials including polyester, nylon, cotton, and other, were exposed to a selection of ambient conditions: ocean, air, and wastewater treatment stages, in a controlled weathering experiment. Infrared spectra were monitored for up to 52 weeks, with the resulting data illuminating on the environmental fate and longevity of synthetic and natural fibers. Spectral changes caused by weathering were found to depend strongly on both the composition of the material and the specific ambient conditions. This library of weathered material spectra is useful not only in easier identification of environmental microfibers, but also in helping us estimate the duration and manner of weathering that a given environmental microfiber may have experienced.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".