MétaCan
Menu
Back to cohort
Record W2794328049 · doi:10.1149/ma2018-01/41/2386

A Nano-Carbon Scaffold (NCS) Electrode for the Vanadium Redox Flow Battery

2018· article· en· W2794328049 on OpenAlexaff
Jialang Li, Daouda Fofana, Xiaoan Li, Ehab N. El Sawy, Viola Birss, Fatemeh Shakeri Hosseinabad, Sladjana Maslovara, Edward P.L. Roberts

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOverpotentialFlow batteryCyclic voltammetryMaterials scienceElectrodeVanadiumScanning electron microscopeNanoporousChemical engineeringElectrochemistryNano-NanotechnologyCarbon fibersComposite materialChemistryMetallurgy

Abstract

fetched live from OpenAlex

In this study a template nano-carbon scaffold (NCS) electrode material is evaluated for use in the vanadium redox flow battery (VRB). Scanning electron microscopy (SEM) was used to characterize the morphology of the electrode materials. This material has an organized nanoporous structure and the pore size can be as small as 18nm. The electrochemical properties of the NCS materials were explored using cyclic voltammetry (CV). To investigate the performance of NCS as an electrode material, the NCS was attached to the surface of conventional carbon paper electrodes. The charge discharge performance of the VRB was determined using a flow through mode of operation. The performance of nano carbon scaffold (NCS) with different pore size and thickness was compared with a conventional heat-treated carbon paper. The results show that by using nano carbon scaffold (NCS-85-HT), the voltage efficiency increased from 77% to 94% at 10 mAcm -2 . The energy efficiency also increased from 56% to 69% at 10 mAcm -2 due to the increased voltage efficiency. The results indicate that the large surface area of the NCS, associated with its nano structure, lead to a reduction in overpotential of around 75%, and thus higher battery efficiencies. Cell performance under different current density was also explored and the improved efficiencies for NCS were maintained at all the current densities studied.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.074
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.015
GPT teacher head0.252
Teacher spread0.238 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced battery technologies researchFrench-language works237,207