Recent Advances in Nanoparticle Preparation by Spray and Microemulsion Methods
Bibliographic record
Abstract
Micro- and nano-sized metal, semiconductor, pharmaceutical, and simple or complex ceramic particles have numerous applications in the development of sensors, thermal barrier coatings, catalysts, pigments, drugs, etc. The challenges include controlling the particle size, size distribution, particle crystallinity, morphology and shape, being able to use the nanoparticles for a given purpose, and to produce them from a variety of precursors. There are several methods to produce nanoparticles, each suitable for a range of applications. In this article, two methods that are receiving increasing attention are considered: spray and microemulsion methods. Spray techniques are single-step methods of producing a broad spectrum of simple to multicomponent functional micro and nanoparticles and quantum dots. Microemulsion is a wet chemistry method. A micro-emulsion system consists of aqueous domains, called reverse micelles, dispersed in a continuous oil phase. In this article, the above mentioned methods of nanoparticle production are introduced and recent advances, research directions and challenges, and the pertinent patents are reviewed and discussed.
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.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".