The polyol process: a unique method for easy access to metal nanoparticles with tailored sizes, shapes and compositions
Bibliographic record
Abstract
After about three decades of development, the polyol process is now widely recognized and practised as a unique soft chemical method for the preparation of a large variety of nanoparticles which can be used in important technological fields. It offers many advantages: low cost, ease of use and, very importantly, already proven scalability for industrial applications. Among the different classes of inorganic nanoparticles which can be prepared in liquid polyols, metals were the first reported. This review aims to give a comprehensive account of the strategies used to prepare monometallic nanoparticles and multimetallic materials with tailored size and shape. As regards monometallic materials, while the preparation of noble as well as ferromagnetic metals is now clearly established, the scope of the polyol process has been extended to the preparation of more electropositive metals, such as post-transition metals and semi-metals. The potential of this method is also clearly displayed for the preparation of alloys, intermetallics and core-shell nanostructures with a very large diversity of compositions and architectures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".