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Record W3106823033 · doi:10.1002/celc.202001334

Functionalization of Contacted Carbon Nanotube Forests by Dip Coating for High‐Performance Biocathodes

2020· article· en· W3106823033 on OpenAlexaff
Hugo Nolan, Maryam Tabrizian, Serge Cosnier, Georg S. Duesberg, Michael Holzinger

Bibliographic record

VenueChemElectroChem · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsMcGill University
FundersAgence Nationale de la RechercheScience Foundation Ireland
KeywordsSurface modificationCarbon nanotubeChemical vapor depositionScanning electron microscopeChemical engineeringMaterials scienceCoatingElectrodeNanotechnologyNanotubeChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract This work focuses on the use of electrically contacted carbon nanotube forests as an electrode material for the bioelectrocatalytic reduction of oxygen to water. The forests are directly grown by chemical vapor deposition on a conductive tantalum layer, which provides enough mechanic stability during several functionalization and enzyme immobilization steps. A pyrene bis‐anthraquinone derivative (pyr‐(AQ) 2 ) was attached via π‐stacking throughout the forest and was used as an anchor molecule for oriented immobilization of laccase. This led to high‐performance biocathodes for oxygen reduction via direct electron transfer with absolute maximum current densities up to 0.84 mA cm −2 at 0.2 V vs Ag/AgCl. The morphological changes during the wet chemical processes were studied by scanning electron microscopy (SEM) revealing cellular patterning of the forest structure. Despite these changes, the forest remained attached and electrically connected to the tantalum layer. The resulting bioelectrodes performed with satisfying stabilities under constant discharge conditions and kept 75 % of its initial performances after one week.

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 score0.042
Threshold uncertainty score0.727

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 head0.008
GPT teacher head0.188
Teacher spread0.179 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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