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Record W2771827542 · doi:10.1139/cjc-2017-0472

Carbon dots based dual-emission silica nanoparticles as ratiometric fluorescent probe for chromium speciation analysis in water samples

2017· article· en· W2771827542 on OpenAlexvenueno aff
Guoqiang Xiang, Yue Ren, Heng Zhang, Huanhuan Fan, Xiuming Jiang, Lijun He, Wenjie Zhao

Bibliographic record

VenueCanadian Journal of Chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsChemistryNanosensorDetection limitFluorescenceNanoparticleChromiumNuclear chemistryAnalytical Chemistry (journal)Inorganic chemistryNanotechnologyChromatographyMaterials science

Abstract

fetched live from OpenAlex

A simple and effective strategy for designing a ratiometric fluorescent nanosensor was described. A carbon dots (CDs) based dual-emission nanosensor for chromium speciation analysis was developed by coating CDs on the surface of dye-doped silica nanoparticles. The fluorescence of the resulting dual-emission silica nanoparticles was quenched in acetic acid through potassium bromate (KBrO 3 ) oxidation. Cr(VI) was able to catalyze KBrO 3 oxidation, resulting in ratiometric fluorescence accelerated quenching response of the dual-emission silica nanoparticles. Several important parameters affecting the nanosensor performance, including acid type, concentrations of KBrO 3 , and reaction temperature and time were examined. Under the optimized conditions, the detection limit of the nanosensor towards Cr(VI) reached 1.3 ng mL −1 , and pretty good linearity was obtained between 20 to 500 ng mL −1 . More importantly, the sensor was found suitable for speciation analysis of both Cr(III) and Cr(VI) in different water samples with satisfactory results.

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)0.001
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 score0.011
Threshold uncertainty score0.461

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 studies0.0000.000
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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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