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Record W2985639246 · doi:10.1002/anie.201913445

Dual Enhancement of Gold Nanocluster Electrochemiluminescence: Electrocatalytic Excitation and Aggregation‐Induced Emission

2019· article· en· W2985639246 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsElectrochemiluminescenceDual (grammatical number)ExcitationAggregation-induced emissionMaterials scienceNanotechnologyOptoelectronicsChemistryPhysicsElectrodeFluorescenceOpticsPhysical chemistryArt

Abstract

fetched live from OpenAlex

Abstract Ligand‐protected gold nanoclusters (AuNCs) have emerged as a new class of electrochemiluminescence (ECL) luminophores for their interesting catalytic and emission properties, although their quantum yield ( Φ ECL ) in aqueous medium is low with a poor mechanistic understanding of the ECL process. Now it is shown that drying AuNCs on electrodes enabled both enhanced electrochemical excitation by an electrocatalytic effect, and enhanced emission by aggregation‐induced ECL (AIECL) for 6‐aza‐2‐thiothymine (ATT) protected AuNCs with triethylamine (TEA) as a coreactant. The dried ATT‐AuNCs/TEA system resulted in highly stable visual ECL with a Φ ECL of 78 %, and a similar enhancement was also achieved with methionine‐capped AuNCs. The drying enabled dual‐enhancement mechanism has solved a challenging mechanistic problem for AuNC ECL probes, and can guide further rational design of ECL emitters.

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.

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 score0.004
Threshold uncertainty score0.585

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)0.0010.000

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 head0.010
GPT teacher head0.248
Teacher spread0.237 · 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