Synergistic antimicrobial mechanisms of silver-doped zinc oxide for water treatment: a systematic review
Bibliographic record
Abstract
This systematic review provides an overview of the existing research on antimicrobial mechanisms of silver-doped zinc oxide nanocomposites (Ag/ZnO NCs). It reports the existing knowledge on the synergistic effect (relationship) between silver (Ag) and zinc oxide (ZnO) for its optimum application. The doping of Ag into the ZnO has been used to enhance its photocatalysis and antimicrobial performance by improving the generation of reactive oxygen species (ROS). The Ag/ZnO NCs' microbial elimination can be done through generated ROS, metallic (Ag+ and Zn2+) ions, and direct attack by the nanoparticles (NPs). Unlike the summation of individual use outcomes, the antimicrobial results of Ag/ZnO create a synergetic effect. This brings the sustainable use of the materials by increasing their efficiency while lowering the amounts used. This article systematically reviews the antimicrobial mechanisms of Ag/ZnO against gram-negative and gram-positive bacteria. It further analyses the quantitative and qualitative synergism between Ag and ZnO when applied together as antimicrobial materials. This systematic review found Ag/ZnO as a potential microbial elimination agent. Many studies reported the chemical synthesis of Ag/ZnO, which might cause a yield of toxic nanomaterials. Further studies on biosynthesis are pivotal for the sustainable supply of safe, non-toxic materials aimed at drinking water treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.004 | 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".