Recent developments in the sonoelectrochemical synthesis of nanomaterials
Bibliographic record
Abstract
In recent years, the synthesis and use of nanoparticles have been of special interest among the scientific communities due to their unique properties and applications in various advanced technologies. The production of these materials at industrial scale can be difficult to achieve due to high cost, intense labour and use of hazardous solvents that are often required by traditional chemical synthetic methods. Sonoelectrochemistry is a hybrid technique that combines ultrasound and electrochemistry in a specially designed electrochemical setup. This technique can be used to produce nanomaterials with controlled sizes and shapes. The production of nanoparticles by sonoelectrochemistry as a technique offers many advantages: (i) a great enhancement in mass transport near the electrode, thereby altering the rate, and sometimes the mechanism of the electrochemical reactions, (ii) a modification of surface morphology through cavitation jets at the electrode-electrolyte interface, usually causing an increase of the surface area and (iii) a thinning of the electrode diffusion layer thickness and therefore ion depletion. The scalability of sonoelectrochemistry for producing nanomaterials at industrial scale is also very plausible due to its "one-pot" synthetic approach. Recent advancements in sonoelectrochemistry for producing various types of nanomaterials are briefly reviewed in this article. It is with hope that the presentation of these studies therein can generate more interest in the field to "catalyze" future investigations in novel nanomaterial development and industrial scale-up studies.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".