A novel method for organic matter removal from samples containing microplastics
Bibliographic record
Abstract
Sludge and biosolids from wastewater treatment plants (WWTPs), identified as important pathways through which microplastics (MPs) can enter the wider environment, contain high organic content, which can obstruct MP quantification/identification. Time- and cost-effective removal of organics is a significant barrier to MP analysis. This study aims to alleviate these obstacles using a widely available store-bought septic tank cleaner, comprised of enzymes and bacteria. The cleaner was added to sludge samples, obtained from a local WWTP. Digestion was tested across a range of cleaner concentrations and heat treatments, and compared to a control digestion without cleaner. Organic content of samples digested with cleaner was reduced by 93%, representing a 22% greater reduction compared to control samples. Virgin plastic pellets, of a variety of polymers, were subjected to the digestion process and underwent no physical or chemical changes, demonstrating this method does not degrade MPs. As all enzymes were added in a single step, the time required for enzymatic digestion using the cleaner was only two days. Compared to existing methods, which take up to several weeks, this novel enzymatic digestion method offers a viable means of extracting MPs from organic materials without either the long processing times required of chemical (solely Fenton's) methods or high cost of laboratory grade enzyme approaches.
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 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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".