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Record W2100511022 · doi:10.1080/10426914.2015.1058953

Electrochemical Deposition of Composites Using Deoxycholic Acid Dispersant

2015· article· en· W2100511022 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials and Manufacturing Processes · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceDispersantComposite numberDeoxycholic acidElectrochemistryDispersion (optics)Chemical engineeringComposite materialAnodeDeposition (geology)ElectrodeChemistryBile acid

Abstract

fetched live from OpenAlex

A biomimetic method has been used for the electrodeposition of carbon nanotubes (NTs) and composite films using a commercial bile acid. Thin films of deoxycholic acid (DCH) were prepared potentiodynamically and galvanostatically from deoxycholic acid sodium salt (DCNa) solutions. The anodic deposition yield has been investigated at different DCNa concentrations. The electrodeposition mechanism involved electromigration of anionic DC− species, local pH reduction at the anode, and precipitation of DCH. It was found that DCNa allowed excellent dispersion of NTs in water. The use of DCNa as a multifunctional agent for NTs dispersion, charging, and binding allowed electrodeposition of NTs and composite MnO2–NTs films. The MnO2–NTs composites were used for charge storage applications in supercapacitors. The MnO2–NTs electrodes showed good capacitive behavior. DCNa is a promising charging dispersant for NTs dispersion and manufacturing of other composites by electrochemical methods.

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.

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 score0.003
Threshold uncertainty score0.559

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 head0.026
GPT teacher head0.253
Teacher spread0.226 · 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