How do material characteristics and antimicrobial mechanisms affect microbial control and water disinfection performance of metal nanoparticles?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it