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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 OpenAlexaff
Chuanxi Wang, Lin Xu, Yu Wang, Dan Zhang, Xiaodong Shi, Fengxia Dong, Kui Yu, Quan Lin, Bai Yang

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations82
Published2012
Admission routes1
Has abstractyes

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