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Record W1841550642 · doi:10.1039/c5nr02291f

Ultra-fast electron capture by electrosterically-stabilized gold nanoparticles

2015· article· en· W1841550642 on OpenAlexafffund
Khashayar Ghandi, Alexander D. Findlater, Zahid Mahimwalla, Connor S. MacNeil, Ernest Awoonor‐Williams, Federico Zahariev, Mark S. Gordon

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsMount Allison University
FundersNatural Sciences and Engineering Research Council of CanadaAdvanced Research Projects Agency - EnergyNew Brunswick Innovation Foundation
KeywordsSolvationIntermolecular forceColloidal goldCovalent bondElectron transferNanoparticleMaterials scienceNanotechnologyElectronChemical physicsChemistryMoleculePhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Ultra-fast pre-solvated electron capture has been observed for aqueous solutions of room-temperature ionic liquid (RTIL) surface-stabilized gold nanoparticles (AuNPs; ∼9 nm). The extraordinarily large inverse temperature dependent rate constants (k(e)∼ 5 × 10(14) M(-1) s(-1)) measured for the capture of electrons in solution suggest electron capture by the AuNP surface that is on the timescale of, and therefore in competition with, electron solvation and electron-cation recombination reactions. The observed electron transfer rates challenge the conventional notion that radiation induced biological damage would be enhanced in the presence of AuNPs. On the contrary, AuNPs stabilized by non-covalently bonded ligands demonstrate the potential to quench radiation-induced electrons, indicating potential applications in fields ranging from radiation therapy to heterogeneous catalysis.

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 score0.010
Threshold uncertainty score0.793

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)0.0000.001

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.

Opus teacher head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations18
Published2015
Admission routes2
Has abstractyes

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