Green and energy-efficient methods for the production of metallic nanoparticles
Bibliographic record
Abstract
In the last decade, researchers paid great attention to the concept of "Green Chemistry", which aims at development of efficient methods for the synthesis of nanoparticles (NPs) in terms of the least possible impact on human life and environment. Generally, several reagents including precursors, reducing agents, stabilizing agents and solvents are used for the production of NPs and in some cases, energy is needed to reach the optimum temperature for reduction. Therefore, to develop a green approach, researchers had the opportunity to investigate eco-friendly reagents and new energy transfer techniques. In order to substitute the harmful reagents with green ones, researchers worked on different types of saccharides, polyols, carboxylic acids, polyoxometalates and extracts of various plants that can play the role of reducers, stabilizers or solvents. Also, there are some reports on using ultraviolet (UV), gamma and microwave irradiation that are capable of reducing and provide uniform heating. According to the literature, it is possible to use green reagents and novel energy transfer techniques for production of NPs. However, these new synthesis routes should be optimized in terms of performance, cost, product quality (shape and size distribution) and scale-up capability. This paper presents a review on most of the employed green reagents and new energy transfer techniques for the production of metallic NPs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".