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Record W3043906235 · doi:10.1088/1361-6528/aba86e

Mechanisms of solid-state nanopore enlargement under electrical stress

2020· article· en· W3043906235 on OpenAlexafffund
Chelsea Leung, Kyle Briggs, Marie-Pier Laberge, Smile Peng, Matthew Waugh, Vincent Tabard‐Cossa

Bibliographic record

VenueNanotechnology · 2020
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsNanoporeMaterials scienceElectric fieldNanotechnologyJoule heatingChemical physicsElectrolyteSolid-stateDissolutionComposite materialChemical engineeringElectrodeChemistry

Abstract

fetched live from OpenAlex

We present a thorough exploration of nanopore growth under electrical stress in electrolyte solution, and demonstrate that despite their superficial similarities, nanopore formation by controlled breakdown (CBD) and nanopore growth under moderate voltage stress are fundamentally different processes. In particular, we demonstrate that unlike the CBD process, nanopore growth is primarily driven by the level of ionic current passing through the nanopore, rather than the strength of the electric field generating the current, and that enlargement has a much weaker pH dependence than does CBD pore formation. In combination with other works in the field, our results suggest that despite clear current-dependence, Joule heating is unlikely to be the main driver of pore growth during electrical stress, pointing instead toward electrochemical dissolution of membrane material along the pore walls. While the chemistry underlying the growth process remains unclear, the dependence of growth rate on current allows decoupling of the pore enlargement mechanism from the possibility of forming additional nanopores during the growth process, providing a practical method by which to rapidly enlarge a nanopore without risking opening a second nanopore.

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 score0.510
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.214
Teacher spread0.202 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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