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Record W4235663121 · doi:10.5539/ijc.v8n1p86

Noble Metal Nanoparticles and Their (Bio) Conjugates. II. Preparation

2016· article· en· W4235663121 on OpenAlexvenueno aff
Ignác Capek

Bibliographic record

VenueInternational Journal of Chemistry · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVAgentúra na Podporu Výskumu a Vývoja
KeywordsNanoparticleChemistryNanomaterialsSurface modificationNanotechnologyBimetallic stripNucleationOstwald ripeningNanostructureParticle (ecology)Colloidal goldChemical engineeringMetalMaterials scienceOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Hybrid nanoparticles of gold and silver can not only retain the beneficial features of both nanomaterials, but also possess unique advantages (synergism) over the other two types. Novel pseudospherical and anisotropic nanoparticles, bimetallic triangular nanoparticles, and core@shell nanoparticles were prepared by the different procedures for various applications and understanding both the particle evolution (nucleation) and nanoparticle anisotropy. Hybrid nanoparticles of gold and silver are considered to be low in toxicity, and exhibit facile surface functionalization chemistry. Furthermore, their absorption peaks are located in visible and near-infrared region. These nanoparticles provide significant plasmon tunability, chemical and surface modification properties, and significant advances in the growth into anisotropic nanostructures. The photoinduced synthesis can be used to prepare various (sub) nanoparticles and OD and 1D nanoparticles. Ostwald and digestive ripening provided narrower particle size distribution.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Journal of ChemistrySame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207