Biodiesel as a non-aqueous medium for the synthesis of nanomaterials: relevance to metallic particulate suspensions in biofuels and their removal
Bibliographic record
Abstract
In relevance to materials chemistry, this study presents biodiesel as an important non-aqueous medium for the synthesis of nanomaterials by demonstrating the synthesis of most versatile gold (Au) and silver (Ag) nanoparticles (NPs) in biodiesel medium. We show single-step in situ synthesis of Au and Ag NPs using biodiesels prepared from soybean and olive oil. Both biodiesels proved to be excellent solvents, reducing agents, and stabilizing agents for Au and Ag NPs. Au and Ag NPs were characterized by transmission electron microscope and XRD analyses, and were within the range of ∼10–50 nm. Colloidal stabilization of NPs by the surface adsorption of biodiesel was evaluated by detailed FT-IR analysis and determined to be driven by the ester head group of biodiesel molecules. Biodiesel-stabilized NPs in aqueous phase were efficiently extracted in the organic phase without using any phase transfer agent, suggesting the applicability of biodiesel in entrapping metal particulates and removing them from the aqueous phase with relevance to environmental sustainability.
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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) | 0.001 | 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".