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Record W3009365353 · doi:10.1139/cjc-2019-0425

UV-emitting polyelectrolyte-modified MoS<sub>2</sub> quantum dots for selective determination of nitrophenol in water samples based on inner filter effect

2020· article· en· W3009365353 on OpenAlexvenueno aff
Keke Ning, Guoqiang Xiang, Cuicui Wang, Jingxing Wang, Xiaohong Qiao, Ruofei Zhang, Xiuming Jiang, Lijun He, Wenjie Zhao

Bibliographic record

VenueCanadian Journal of Chemistry · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryFluorescenceQuantum dotQuantum yieldMolybdenum disulfideSodium molybdateDetection limitAnalytical Chemistry (journal)ReagentAmmonium molybdateAbsorption (acoustics)PhotochemistryNuclear chemistryMolybdateInorganic chemistryNanotechnologyChromatographyZincMaterials sciencePhysical chemistry

Abstract

fetched live from OpenAlex

In this work, poly(sodium 4-styrenesulfonate) (PSS) modified molybdenum disulfide quantum dots (MoS 2 -PSS QDs) were synthesized via a simple hydrothermal method using l-cysteine and anhydrous sodium molybdate as precursors and PSS as a modification reagent, and a selective and sensitive fluorescent sensing method for the determination of p-nitrophenol (p-NP) based on their UV emission was developed. The obtained MoS 2 -PSS QDs have an obvious UV emission peak (390 nm) with quantum yield of 5.13%. The strong absorption peak of p-NP at 400 nm has large spectral overlap with the UV emission peak (390 nm) of MoS 2 -PSS QDs. Because of this p-NP absorption, the fluorescence of MoS 2 -PSS QDs at 390 nm is quenched with the introduction of p-NP via the inner filter effect (IFE) and the decreased fluorescence intensity was linearly proportional to the p-NP concentration in the range of 1–20 μmol/L, leading to a detection limit of 0.13 μmol/L for p-NP. The MoS 2 QDs-based fluorescent probe for p-NP is sensitive and selective and was successfully applied in the determination of p-NP in the pond 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)0.000
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 score0.006
Threshold uncertainty score0.524

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 head0.227
Teacher spread0.213 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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