Analysis by response surface methodology of gold nanoparticles obtained by green chemical reduction using aqueous coffee pulp extract (<i>Coffea arabica</i>)
Bibliographic record
Abstract
Green synthesis of gold nanoparticles using an aqueous coffee (Coffea arabica L.) pulp extract as a reducing agent was achieved and analyzed by response surface methodology, given the diverse applicability of these nanoparticles containing a wide range of phenolic compounds from the coffee pulp extract. Three factors (precursor concentration, reducing agent concentration, and reaction time) were considered and their combined effects on the maximum intensity MA, particle size d, and particle density N were analyzed. An opposing effect between the precursor and reducing agent was observed during synthesis, because although the precursor increased d and diminished N, the reducing agent diminished d and increased N. These effects were observed simultaneously through the response surfaces of d and N. This methodology allowed the synthesis of nanoparticles with an average particle size of 5–22 nm and N around 2.9 × 1011 − 3.7 × 1013 part/mL, depending on the response surface methodology, Box–Behnken design. The R2 value determined for all cases was 0.98 (MA), 0.99 (d), and 0.97 (N), clearly indicating that the model can be used to predict or design the response variables in the design space. Finally, Fourier transform infrared measurements showed that the organic compounds present in the aqueous coffee pulp extract were coating the surface of the gold nanoparticles. The proposed methodology could contribute to the design of new alternatives for the synthesis of specific nanostructures with potential applications in the therapeutic area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".