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Record W2345323515 · doi:10.1139/cjc-2016-0039

Simple naked-eye colorimetric chemosensors based on Schiff-base for selective sensing of cyanide and fluoride ions

2016· article· en· W2345323515 on OpenAlexvenueno aff
Asadollah Mohammadi, Jeyran Jabbari

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsnot available
FundersUniversity of Guilan
KeywordsChemistryNaked eyeFluorideCyanideTitrationIonSchiff baseStoichiometryProton NMRBase (topology)PhotochemistryDetection limitCombinatorial chemistryInorganic chemistryStereochemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

The present work describes the design and synthesis of simple colorimetric chemosensors based on Schiff-base for highly selective sensing of cyanide and fluoride ions. The chemosensor S2, containing an electron withdrawing group (EWG), displayed selective sensing properties for both cyanide and fluoride ions. The interaction of S2 with F– and CN– ions provides remarkable colorimetric responses from yellow to purple, enabling naked-eye sensing without any spectroscopic instrumentation. The mechanism of anion binding with chemosensor S2 showed one-to-one stoichiometry by Job’s plot. The mechanism of interaction between the S2 and CN– ions has been confirmed by the 1H NMR titration experiments. Furthermore, the detection limits of chemosensor S2 towards F– and CN– ions were found to be 3.7 and 1.2 μmol/L, respectively.

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)0.001
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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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