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Record W7033241933

Properties and behavior of nanoparticles

2022· article· uk· W7033241933 on OpenAlexaff

Bibliographic record

VenueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2022
Typearticle
Languageuk
FieldEngineering
TopicAdvanced Theoretical and Applied Studies in Material Sciences and Geometry
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsNanoparticleNanomaterialsCladding (metalworking)MetalSurface energyQuantum dotInteraction energy
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.286
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogySame topicAdvanced Theoretical and Applied Studies in Material Sciences and GeometryFrench-language works237,207