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Record W1976323350 · doi:10.1139/cjc-2015-0017

Rifampicin determination in human serum and urine based on a disposable carbon paste microelectrode modified with a hollow manganese oxide@mesoporous silica oxide core-shell nanohybrid

2015· article· en· W1976323350 on OpenAlexvenueno aff
Tian Gan, Zhaoxia Shi, Kaili Wang, Junyong Sun, Zhen Lv, Yan‐Ming Liu

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryMicroelectrodeElectrochemistryMesoporous materialMesoporous silicaCalibration curveOxideCyclic voltammetryGrapheneChemical engineeringInorganic chemistryElectrodeNuclear chemistryDetection limitNanotechnologyChromatographyMaterials scienceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

This work designed a simple, sensitive, low-cost, and disposable electrochemical platform for the detection of antibiotic rifampicin (RIF) by using a hollow manganese oxide@mesoporous silica oxide (Mn 3 O 4 @SiO 2 ) core-shell nanohybrid as sensitive material. The Mn 3 O 4 @SiO 2 core-shell nanohybrid, prepared by a facile polyacrylic acid soft templating method, was applied to modify a homemade carbon paste microelectrode, which provided a feasible pathway for electron transfer of RIF due to the low density, large surface area, excellent loading capacity, high permeability, and abundant amount of active sites. The electrocatalytic behavior was further used for sensitive detection of RIF by square wave voltammetry. Under optimal conditions, the calibration curve was linear in the range from 30 nmol/L to 3.0 μmol/L. The electrochemical method showed good stability, reproducibility, and selectivity. It could effectively be applied to the determination of RIF in human serum and 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 score0.011
Threshold uncertainty score0.917

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 head0.210
Teacher spread0.200 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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