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Record W2964184411 · doi:10.1139/cjc-2019-0079

Design and synthesis of a novel fluorescent probe, Eu<sup>III</sup>-dtpa-bis(adenine), and application for the detection of orotic acid in urine

2019· article· en· W2964184411 on OpenAlexvenueno aff
Zhiyue Yu, Haishuang Jia, Na Li, Wenfang Liu, Jun Wang, Youtao Song

Bibliographic record

VenueCanadian Journal of Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsnot available
FundersLiaoning UniversityDepartment of Education of Liaoning ProvinceNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsChemistryDetection limitFluorescenceProton NMRFourier transform infrared spectroscopyAnalytical Chemistry (journal)Ligand (biochemistry)Orotic acidSchiff baseNuclear chemistryChromatographyStereochemistryBiochemistry

Abstract

fetched live from OpenAlex

For detection of orotic acid (OA), a novel fluorescent probe, Eu III -dtpa-bis(adenine), was designed and synthesized based on the complementation base pairing principle. The dtpa-bis(adenine) ligand was characterized by nuclear magnetic resonance spectroscopy ( 1 H NMR and 13 C NMR), Fourier transform infrared spectra (FTIR), and elemental analysis. Then, the effects of solution pH values and some coexisting interfering substances on OA detection using Eu III -dtpa-bis(adenine) as a fluorescent probe were investigated. The linear relationship of F 0 /F and OA concentration in the range of 10–150 μmol/L was obtained, where F 0 and F are the fluorescence intensities of Eu III -dtpa-bis(adenine) at 320 nm in the absence and presence of OA, respectively. The calculated limit of detection is about 0.807 μmol/L. The detection mechanism of OA was proposed. In addition, the detection of OA in urine samples has also been studied. It was found that the designed and synthesized fluorescent probe is effective for the detection of OA in urine samples.

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 score0.022
Threshold uncertainty score0.204

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 head0.202
Teacher spread0.190 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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