Surfactants: hygiene’s first line of defence against pollution
Bibliographic record
Abstract
Surfactants are a diverse group of compounds that are widely used in a range of industrial, commercial and household applications. They are amphiphilic molecules that contain both hydrophilic and hydrophobic groups, which allow them to interact with both water and oil. Surfactants have a number of important properties, including the ability to reduce surface tension, emulsify liquids and solubilise hydrophobic compounds. These properties make them valuable in a range of applications, including detergents (hygiene products), cosmetics, pharmaceuticals and agricultural products. However, the widespread use of surfactants has also raised concerns about their environmental impacts. This review provides an overview of the properties and applications of surfactants, as well as their environmental impacts, the different types of surfactants and their properties and uses in different applications and the current understanding of the environmental fate and impacts of surfactants, including their interactions with aquatic organisms, microbial communities and natural ecosystems. Finally, ths study discusses strategies for minimising the environmental impacts of surfactants, including the development of biodegradable and environmentally friendly surfactants.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".