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Electrodeposition of biopolymer–glucose oxidase composites

2011· article· en· W2018179769 on OpenAlexafffund
Y Li, Igor Zhitomirsky

Bibliographic record

VenueSurface Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlucose oxidaseBiopolymerMaterials scienceChemical engineeringElectrophoretic depositionDeposition (geology)Fourier transform infrared spectroscopyBiosensorPolymerComposite materialNanotechnologyCoating

Abstract

fetched live from OpenAlex

Composite films containing glucose oxidase in a biopolymer matrix were prepared by electrodeposition. Chitosan–glucose oxidase films were prepared by cathodic electrodeposition. Anodic electrodeposition method was developed for the deposition of alginic acid and hyaluronic acid films containing glucose oxidase. The proposed deposition mechanisms involved electrophoresis, pH changes at the electrode surfaces attributed to electrochemical reactions, coagulation of polymer macromolecules and glucose oxidase and film formation. The deposition yield was studied as a function of biopolymer and glucose oxidase concentration in the solutions. The results of the deposition yield measurements, Fourier transform infrared spectroscopy and thermogravimetric and differential thermal analyses showed the formation of composite films. Electron microscopy studies showed that the film thickness can be varied in the range of 0–3 μm by the variation in deposition time at a constant deposition voltage. The methods enabled the formation of uniform films of controlled thickness. The electrodeposition methods can be used for the fabrication of biosensors.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.160
Teacher spread0.154 · 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

Citations10
Published2011
Admission routes2
Has abstractyes

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