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Record W2157954525 · doi:10.1002/asia.201200033

Fluorescent Silver Nanoclusters as Effective Probes for Highly Selective Detection of Mercury(II) at Parts‐per‐Billion Levels

2012· article· en· W2157954525 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.

Bibliographic record

VenueChemistry - An Asian Journal · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsSteacie Institute for Molecular Sciences
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsNanoclustersDetection limitFluorescenceMercury (programming language)Parts-per notationChemistrySolubilityNanoparticleSilver nanoparticleNanotechnologyMaterials scienceAnalytical Chemistry (journal)Chemical engineeringCombinatorial chemistryPhotochemistryChromatographyOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Facile preparation of water-soluble and fluorescent Ag nanoclusters (NCs) stabilized by glutathione at room temperature is described. Although the glutathione layer was introduced to prevent the silver nanoparticles from decomposition and increase their water solubility, this simple surface optimization resulted in surprisingly high efficiency of selective Hg(2+) sensing, where the limit of detection (LOD) was as low as 10(-10) M (0.02 ppb, 0.1 nM). This result revealed a simple and practical strategy for Hg(2+) detection using fluorescent Ag NCs as sensor probe, with the lowest detecting limits reported to date.

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)0.001
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 score0.005
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.256
Teacher spread0.243 · 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