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A Fluorescent Probe Using the Boron and Nitrogen Co-Doped Carbon Dots for the Detection of Hg2+ Ion in Environmental Water Samples

2017· article· en· W2610942345 on OpenAlex
Wei Bian, Ya‐Kun Wang, Haifen Yang, Ping Li, Qing Yu, Shaomin Shuang, Chuan Dong, Martin M. F. Choi

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

VenueCurrent Analytical Chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsAcadia University
Fundersnot available
KeywordsMercury (programming language)FluorescenceBoronEnvironmental chemistryBioaccumulationCarbon fibersIonNitrogenChemistryInorganic chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Background: Mercury (Hg2+) ion which is one of the most toxic heavy metals, can pollute the environment and threaten human health due to its features of non-biodegradation and bioaccumulation. Therefore, the detecting of Hg2+ ion is very necessary and has received increasing interest among researchers. The aims of this study is to developed a simple, sensitive and selective fluorescence probe for Hg2+ ion detecting. Keywords: A fluorescent probe, boron and nitrogen co-doped carbon dots, mercuric ion, environmental water samples, toxic heavy metals, TEM.

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 score0.024
Threshold uncertainty score0.197

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 head0.047
GPT teacher head0.311
Teacher spread0.264 · 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