Properties and behavior of nanoparticles
Bibliographic record
Abstract
The methods of obtaining nanomaterials have been described, and the conditions for the formation of nanoparticles of a given size have been assessed. It has been shown that the surface energy of the nano-sized particles, due to a large portion of surface atoms, is significantly greater than the surface energy of the microparticles resulting in the non-equilibrium state of the former with the possibility to self-organize forming, in particular, strong and wear-resistant cladding films from the metallic nanoparticles on the friction surfaces. To explain the interaction of the nanoparticles, which are at the boundary of the quantum and classical states, with the environment, the main provisions of quantum mechanics have been used. The mutual interaction between the bare nanoparticles and between the nanoparticle and a surface, separated by the environment, has been considered. It has been shown that without the stabilization with surfactants, metallic nanoparticles will aggregate; in turn, the nature of the interaction between the nanoparticles with the solid substrates is determined by their dielectric constants. The paper presents an overview of the development of the nanotechnology industry, indicates the commercial demand for nanomaterials, marks the countries that are leaders in the production of nanomaterials, and lists the mainelements used for the production of metallicnanopowders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".