MétaCan
Menu
Back to cohort
Record W2883472594 · doi:10.1139/cjc-2018-0105

Two novel colorimetric probes (5-HMBA-FH and 3-HMBA-FH) based on fluorescein for copper(II) ion detection

2018· article· en· W2883472594 on OpenAlexvenueno aff
Guanhong Liu, Peipei Ren, Fan Yang, Xuekai Dou, Jun Wang, Youtao Song

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsnot available
FundersMinistry of Public Security of the People's Republic of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsChemistryHydrazoneCopperIonHydrazideFluoresceinMetal ions in aqueous solutionProton NMRAnalytical Chemistry (journal)FluorescenceChromatographyStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Two novel isomeric colorimetric probes are established for simultaneous determination of copper ions using 2-hydroxy-5-methoxybenzaldehyde fluorescein hydrazone (5-HMBA-FH) and 2-hydroxy-3-methoxybenzaldehyde fluorescein hydrazone (3-HMBA-FH). They are synthesized by reacting fluorescein hydrazide with 2-hydroxy-5-methoxybenzaldehyde and 2-hydroxy-3-methoxybenzaldehyde, respectively, and then characterized by 1 H-NMR, 13 C-NMR, and infrared spectrum. The addition of copper ions to the solutions of two novel colorimetric probes can generate the obviously peaks at 498 nm in UV–vis absorption spectra along with a rapid colour change from colourless to dark yellow. The detection limits of the method for Cu 2+ ion were 3.442 × 10 −6 mol/L and 3.682 × 10 −6 mol/L separately for 5-HMBA-FH and 3-HMBA-FH, respectively. The additions of other metal ions hardly affect the determination of copper ions. The proposed method was successfully applied to the analysis of Cu 2+ ions in various samples. This method possesses high sensitivity, high simplicity, and minimized interference and will provide a great advantage in detecting copper ions in the environment, food, and medical applications.

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 score0.023
Threshold uncertainty score0.912

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicMolecular Sensors and Ion DetectionFrench-language works237,207