The polyol process: a unique method for easy access to metal nanoparticles with tailored sizes, shapes and compositions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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 it