How do material characteristics and antimicrobial mechanisms affect microbial control and water disinfection performance of metal nanoparticles?
Bibliographic record
Abstract
Abstract Nanotechnology has been rapidly developing in the past decade, and metal nanomaterials have shown promising improvement in microbial control. Metal nanoparticles have been applied in medical settings for adequate disease spread control and to overcome the challenges of multidrug-resistant microorganisms. Recently, the demand for safe water supply has increased, requiring higher sanitation of the water treatment technology as well as being environmentally sustainable. However, the employed water disinfection technologies cannot meet the elevated demand due to limitations including chemical byproducts, immobility, energy consumption, etc. Metal nanomaterials are considered to be an alternative disinfection technology considering their high efficiency, mobility, and stability. A significant amount of research has been carried out on enhancing the antimicrobial efficiency of metal nanomaterials and determining the underlying antimicrobial mechanisms. This paper provides an overview of emerging metal nanomaterials development, including the synthesis method, material characteristics, disinfection performance, environmental factors, potential mechanism, limitations, and future opportunities in the water disinfection process.
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 | 0.001 |
| 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 | 0.001 |
| 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".