MétaCan
Menu
Back to cohort
Record W2083619442 · doi:10.1002/elan.200390027

An Amperometric Biosensor Based on the Coimmobilization of Horseradish Peroxidase and Methylene Blue on a Carbon Nanotubes Modified Electrode

2003· article· en· W2083619442 on OpenAlexaff
Jin‐Zhong Xu, J ZHU, Qiang Wu, Zheng Shui Hu, Hong‐Yuan Chen

Bibliographic record

VenueElectroanalysis · 2003
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsBiosensorHorseradish peroxidaseAmperometryCarbon nanotubeMethylene blueAscorbic acidCyclic voltammetryDetection limitElectrodeHydrogen peroxideChemistryGlassy carbonMaterials scienceAnalytical Chemistry (journal)ChromatographyOrganic chemistryNanotechnologyElectrochemistryCatalysisPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract A novel hydrogen peroxide biosensor has been constructed based on the characteristics of the carbon nanotube. The multiwall carbon nanotube (MWNT) was used as a coimmobilization matrix to incorporate horseradish peroxidase (HRP) and electron transfer mediator methylene blue (MB) onto a glassy carbon electrode surface. Cyclic voltammetry and amperometric measurements were employed to demonstrate the feasibility of methylene blue as an electron carrier between the immobilized peroxidase and the surface of glassy carbon electrode. The amperometric response of this resulting biosensor to H 2 O 2 shows a linear relation in the range from 4 μM to 2 mM. The detection limit was 1 μM when the signal to noise ratio is 3. The presence of dopamine and ascorbic acid hardly affects the sensitive determination of H 2 O 2 . This biosensor also possesses very good stability and reproducibility.

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)0.000
metaresearch head score (Gemma)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations208
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueElectroanalysisSame topicElectrochemical sensors and biosensorsFrench-language works237,207