Mechanism and Implications of Nanoparticle Release from Commercial Nano-Textiles: A systematic review
Bibliographic record
Abstract
The application of nanomaterials in textiles is a result of the growing consumer desire for robust, environmentally responsible apparel. Concerns are raised, meanwhile, regarding the effects of engineered nanomaterials (ENMs). Research has looked into the release of nanoparticles from fabrics during washing and use. However, the available studies vary widely, making a thorough understanding difficult. To compile the available data and provide a comprehensive analysis of nanoparticle release from commercially used and washed nano-enhanced textiles, a systematic review is necessary. We used PRISMA guidelines to search for the available literature using pre-specified inclusion and exclusion criteria. These databases provided 1158 relevant research articles, which Endnote software screened for duplicates. 36 studies were considered relevant for reading in full after 479 distinct abstracts were assessed for relevant studies. After complete text evaluation, only 13 of these articles were found to be relevant. New Castle Ottawa (NOS) was used for the risk bias assessment of all included studies. The findings show that a significant quantity of nanoparticles can be released by textiles using nanotechnology. Numerous factors, including the structure of the nanoparticles, the adhesive qualities, the type of fabric, and the interactions with the environment, affect the characteristics of those released particles, particularly their quantity and composition. These findings highlight potential risks associated with nanoparticle release, highlighting the need for toxicological evaluations and further research into particle behavior, with a focus on the functional aspects of fibers and how they affect the environment after nanoparticle release after washing, even though there are differences between laboratory simulations and real-world conditions.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".