Development and Validation of an Efficient Method for Processing Microplastics in Biota Samples
Bibliographic record
Abstract
Abstract The impacts of microplastics on aquatic ecosystems and biota are gaining attention globally. Although microplastics have been widely detected in biota, there currently are few standardized detection and identification methods. The present study developed a novel one-step digestion method which was evaluated with mussel and fish samples. This method employed nitric acid and hydrogen peroxide (HNO3:H2O2 = 4:1 by volume) as digestion reagents, which completely digested biota samples <5 g weight within 30 min at 50 °C. A density separation step was subsequently used to remove organic residues as necessary. The efficiency and suitability of this method were tested by spiking microplastics of 7 different types and of various sizes (1000, 900, 675, 300, 250, and 150 μm) into mussel and gastrointestinal tracts of fish. The recoveries of microplastics ranged from 90 to 100%. No significant changes in weight, surface area, and particle size (t test, p > 0.05) were observed for all tested polymers. Fourier transform infrared spectral analyses demonstrated that the method did not degrade any of the polymers except for polyethylene terephthalate. The method was demonstrated with mussel and fish samples collected from the Pearl River delta, south China, and was able to recover microplastics effectively. Overall, the present method is time-saving and easy to operate, with low procedural cross-contamination. The properties of microplastics recovered by the present method remained largely intact, greatly benefiting subsequent qualitative and quantitative analyses. Environ Toxicol Chem 2019;38:1400–1408. © 2019 SETAC Abstract Development and validation of an efficient method for processing microplastics in biota samples
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".