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Record W2046547007 · doi:10.1039/c4cp00954a

Chemical stability and degradation mechanisms of triangular Ag, Ag@Au, and Au nanoprisms

2014· article· en· W2046547007 on OpenAlexaff
Kee Eun Lee, Amelia V. Hesketh, Timothy L. Kelly

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDegradation (telecommunications)PlasmonHalideNanotechnologyNanoparticleChemistryMetalColloidal goldMetal ions in aqueous solutionEtching (microfabrication)Chemical engineeringMaterials scienceChemical stabilityIonInorganic chemistryOrganic chemistryLayer (electronics)Optoelectronics

Abstract

fetched live from OpenAlex

Anisotropic metal nanoparticles have found use in a variety of plasmonic applications because of the large near-field enhancements associated with them; however, the very features that give rise to these enhancements (e.g., sharply curved edges and tips) often have high surface energies and are easily degraded. This paper describes the stability and degradation mechanisms of triangular silver, gold-coated silver, and gold nanoprisms upon exposure to a wide variety of adverse conditions, including halide ions, thiols, amines and elevated temperatures. The silver nanoprisms were immediately and irreversibly degraded under all of the conditions studied. In contrast, the core-shell Ag@Au nanoprisms were less susceptible to etching by chlorides and bromides, but were rapidly degraded by iodides, amines and thiols by a different degradation pathway. Only the pure gold nanoprisms were stable to all of the conditions tested. These results have important implications for the suitability of triangular nanoprisms in many applications; this is particularly true in biological or environmental fields, where the nanoparticles would inevitably be exposed to a wide variety of chemical stimuli.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.224
Teacher spread0.210 · 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.

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

Citations63
Published2014
Admission routes1
Has abstractyes

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