Antimicrobial nanomaterials against biofilms: an alternative strategy
Bibliographic record
Abstract
Microbial adhesion to surfaces and the consequent biofilm formation under various environmental conditions is a common ecological phenomenon. Although biofilms play crucial beneficial roles in many processes, they can also cause serious problems for food, biomedical, environmental, and industrial sectors, leading to higher costs of production and equipment maintenance, and negative public health and environmental impacts. Biofilms are difficult to eradicate due to their resistance to conventional antimicrobial applications. Consequently, attention has been devoted to new emerging nanomaterials for their remarkable antimicrobial function. Understanding the inactivation mechanisms is the key to increase the efficiency of nanoparticles (NPs) and enhance the feasibility of their application against various microorganisms under different environments. In this paper, we review the activities of NPs as antimicrobial agents. We also discuss the mechanisms and factors contributing to antimicrobial properties of NPs. In addition, we describe some of the approaches employing NPs as effective antimicrobial agent, and associated challenges and problems in developing NPs as effective antibiofilm agents.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".