Analysis of antimicrobial silver nanoparticles synthesized by coastal strains of Escherichia coli and Aspergillus niger
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The present study investigated the extracellular biosynthesis of antimicrobial silver nanoparticles by Escherichia coli AUCAS 112 and Aspergillus niger AUCAS 237 derived from coastal mangrove sediment of southeast India. Both microbial species were able to produce silver nanoparticles, as confirmed by X-ray diffraction spectrum. The nanoparticles synthesized were mostly spherical, ranging in size from 5 to 20 nm for E. coli and from 5 to 35 nm for A. niger, as evident by transmission electron microscopy. Fourier transform spectroscopy revealed prominent peaks corresponding to amides I and II, indicating the presence of a protein for stabilizing the nanoparticles. Electrophoretic analysis revealed the presence of a prominent protein band with a molecular mass of 45 kDa for E. coli and 70 kDa for A. niger. The silver nanoparticles inhibited certain clinical pathogens, with antibacterial activity being more distinct than antifungal activity. The antimicrobial activity of E. coli was more pronounced than that of A. niger and was enhanced with the addition of polyvinyl alcohol as a stabilizing agent. This work highlighted the possibility of using microbes of coastal origin for synthesis of antimicrobial silver nanoparticles.
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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 | 0.000 | 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 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".