Biodegradability and Ecotoxicity Profiles of Choline Acetate, Betaine, and L-Proline NADESs: A Hidden Threat for Eutrophication?
Bibliographic record
Abstract
Deep Eutectic Solvents (DESs) and in essence naturally available DESs (NADESs) are considered to be green solvents due to their low vapor pressure, non-flammability, thermal stability, good solvent power and low oxicity. These properties make them attractive as safer and more environmentally acceptable solvent options. Green Chemistry promotes the use of renewable and biocompatible compounds such as amino acids, lipids and acids of natural origin to yield more sustainable DESs, which yields their application in several industrial processes. Driven by the current requisite for sustainable progress, along with overcoming dependence on fossil-based resources, the current work details important findings pertaining to the design of sustainable NADESs from the perspective of green chemistry to exhibit suitable physico-chemical properties and a low toxicological profile. Biodegradation studies using OECD 301D closed bottle test (CBT) were performed to observe the biodegradability of 15 selected NADESs. Toxicity controls were run along with the CBT run to observe the behavior of these NADESs in the environment. In this framework, the present paper investigates the development of safer NADESs. The results obtained suggest that our synthesized NADESs, have high biodegradability and low toxicity towards microalgae. Although a conventional threat to the environment would seem out of reach, it must be hypothesized that such compounds might act as enhancers of eutrophication phenomena.
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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 | 0.000 | 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) | 0.001 | 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".