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Record W4408309914 · doi:10.1002/adfm.202419748

Print‐and‐Plate Architected Electrodes for Electrochemical Transformations Under Flow

2025· article· en· W4408309914 on OpenAlexaff
Dylan M. Barber, Sofía Edgar, Michael S. Emanuel, Michael Nelwood, Bok Yeop Ahn, Benito Román‐Manso, Thomas Cochard, Justin Platero, Kiana Amini, Chris H. Rycroft, Shmuel M. Rubinstein, Michael J. Aziz, Jennifer A. Lewis

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceElectrochemistryElectrodeFlow (mathematics)NanotechnologyComposite materialMechanics

Abstract

fetched live from OpenAlex

Abstract Flow cell electrodes are typically composed of porous carbon materials, such as papers, felts, and cloths. However, their random architecture hinders the fundamental characterization of electrode structure‐performance relationships during in situ operation of porous electrochemical flow systems. This work describes a “print‐and‐plate” method that combines direct ink writing of micro‐periodic lattices with a two‐step metal plating process that converts them into highly conductive (sheet resistance 40 mΩ sq −1 ) electrodes. Their operando performance is assessed in an anthraquinone disulfonic acid half‐cell using widefield electrochemical fluorescence microscopy, where output current and fluorescence intensity are in excellent agreement. The pressure drop associated with flow through three electrode designs is determined via simulations from which the most efficient design is identified and manufactured via print‐and‐plate. Confocal fluorescence microscopy is then used to create a 3D map of the state of charge (SOC) inside this print‐and‐plate electrode. The experimental state of the charge map is in good agreement with computational predictions. The rapid design, simulation, and fabrication of print‐and‐plate electrodes enable fundamental investigations of how architected porosity affects electrochemical performance under flow.

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: none
Teacher disagreement score0.664
Threshold uncertainty score0.682

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 head0.009
GPT teacher head0.247
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
Published2025
Admission routes1
Has abstractyes

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