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Record W2505676970 · doi:10.1021/acs.nanolett.6b02418

A Method for Promoting Assembly of Metallic and Nonmetallic Nanoparticles into Interfacial Monolayer Films

2016· article· en· W2505676970 on OpenAlexfundno aff
Yikai Xu, Magdalena P. Konrad, Wendy W. Y. Lee, Ziwei Ye, Steven E. J. Bell

Bibliographic record

VenueNano Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
FundersEuropean Social FundQueen's UniversityQueen's University Belfast
KeywordsMonolayerNanoparticleMaterials scienceNanotechnologyMetalSelf-assemblyPhase (matter)Layer (electronics)Chemical engineeringSurface chargeChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Two-dimensional metal nanoparticle arrays are normally constructed at liquid-oil interfaces by modifying the surfaces of the constituent nanoparticles so that they self-assemble. Here we present a general and facile new approach for promoting such interfacial assembly without any surface modification. The method use salts that have hydrophobic ions of opposite charge to the nanoparticles, which sit in the oil layer and thus reduce the Coulombic repulsion between the particles in the organic phase, allowing the particles to sit in close proximity to each other at the interface. The advantage of this method is that because it does not require the surface of the particles to be modified it allows nonmetallic particles including TiO2 and SiO2 to be assembled into dense interfacial layers using the same procedure as is used for metallic particles. This opens up a route to a new family of nanostructured functional materials.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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

Citations108
Published2016
Admission routes1
Has abstractyes

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