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Record W2093976803 · doi:10.1139/cjc-2014-0600

DNA-templated fluorescent gold nanoclusters reduced by Good’s buffer: from blue-emitting seeds to red and near infrared emitters

2015· article· en· W2093976803 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanoclustersChemistryFluorescenceDNAReducing agentPhotochemistryNuclear chemistryNanotechnologyCombinatorial chemistryOrganic chemistryBiochemistryMaterials science

Abstract

fetched live from OpenAlex

DNA-templated fluorescent gold nanoclusters (AuNCs) have been recently prepared showing higher photostability than the silver counterpart. In this work, we examined the effect of pH, DNA length, DNA sequence, and reducing agent. Citrate, HEPES, and MES produce blue emitters, glucose and NaBH 4 cannot produce fluorescent AuNCs, while ascorbate shows blue emission even in the absence of DNA. This is the first report of using Good’s buffer for making fluorescent AuNCs. Dimethylamine borane (DMAB) produces red emitters. Poly-C DNA produces AuNCs only at low pH and each DNA chain can only bind to a few gold atoms, regardless of the DNA length. Otherwise, large nonfluorescent gold nanoparticles (AuNPs) are formed. Each poly-A DNA might template a few independent AuNCs. The blue emitters can be further reduced to form red emitters by adding DMAB. The emission color is mainly determined by the type of reducing agent instead of DNA sequence.

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 score0.034
Threshold uncertainty score0.789

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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