Structural and antimicrobial properties of synthesized gold nanoparticles using biological and chemical approaches
Bibliographic record
Abstract
This study explores the synthesis and characterization of gold nanoparticles (AuNPs) using green and chemical methods, employing ginger extract and curcumin as reducing agents, in comparison to sodium citrate reduction. The biosynthesized AuNPs synthesized with ginger extract exhibited an average hydrodynamic diameter of 15 and 10 nm for curcumin-conjugated AuNPs, while chemically synthesized AuNPs with sodium citrate displayed an average size of 10 nm. Assessments via Zeta potential measurements revealed negative surface charges across all samples, with the curcumin-conjugated AuNPs showing −36.3 mV, ginger extract-synthesized AuNPs showing −31.7 mV, and chemically produced gold nanoparticles having a surface charge of −40.4 mV. Transmission Electron Microscopy (TEM) confirmed spherical morphologies for the synthesized nanoparticles,and it revealed the presence of biomolecules embedded within the nanoparticles synthesized using biological materials, whereas chemically synthesized AuNPs lacked such features. The FTIR spectra of the biosynthesized AuNPs highlighted the presence of phenolic and aromatic compounds from the ginger extract and curcumin, indicating their role in coating the nanoparticles. Gas chromatography-mass spectrometry (GC-MS) analysis identified gingerol as a key component in the ginger extract, contributing to nanoparticle capping. The antimicrobial efficacy of the AuNPs was evaluated against P. aeruginosa , E. coli , and S. aureus , revealing superior activity for curcumin-AuNPs, with ginger-AuNPs also outperforming chemically synthesized counterparts. These findings confirm the advantages of biological approaches, using a plant extract like ginger and pure curcumin suspension, for better size distribution when used as reducing agents, along with improved antimicrobial efficacy compared to chemically produced gold nanoparticles synthesized with sodium citrate. This study also highlight the potential of green-synthesized AuNPs in biomedical applications, due to their enhanced stability from higher surface charge and the repeatability of biological methods.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".