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Record W2885688387 · doi:10.1002/ente.201800338

X‐ray Nano Computed Tomography of Electrospun Fibrous Mats as Flow Battery Electrodes

2018· article· en· W2885688387 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

VenueEnergy Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of WaterlooMcGill University
FundersScience and Technology Facilities CouncilEngineering and Physical Sciences Research CouncilRoyal Academy of Engineering
KeywordsElectrospinningMaterials scienceElectrodeBattery (electricity)Porous mediumPorosityNano-NanotechnologyMicrostructurePolymerComposite materialBiomedical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Many electrochemical energy storage and conversion devices employ porous media as electrodes, gas diffusion layers or separators. Recently, electrospinning has received significant attention as a way to generate nano‐fibers of polymers with controlled morphology and properties that, once carbonised, can act as conductive and porous media for electrochemical energy devices. The recent advances in X‐ray computed tomography have led the technique to be widely used in the characterisation of energy technologies and porous media as it offers a uniquely non‐destructive insight into the 3D microstructure of materials. Here we present electrospun fibrous mats with uncontrolled, controlled and aligned morphology for use as redox flow battery electrodes and, for the first time, obtain ultra‐high resolution nano‐tomographic X‐ray imaging of the materials using a lab source. The virtual 3D volumes enable extraction of parameters that would not be possible via other characterisation routes.

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 categoriesMeta-epidemiology (narrow)
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 score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.004
GPT teacher head0.219
Teacher spread0.215 · 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