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Record W2109776854 · doi:10.1149/1.1613670

Design and Testing of a 64-Channel Combinatorial Electrochemical Cell

2003· article· en· W2109776854 on OpenAlexaff
Michael D. Fleischauer, T. D. Hatchard, Gregory P. Rockwell, Jessica M. Topple, S. Trussler, S. K. Jericho, M. H. Jericho, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2003
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrochemistryElectrodeElectrochemical cellBattery (electricity)Materials scienceRepeatabilityThroughputNanotechnologyComputer scienceChemical engineeringChemistryChromatographyEngineeringTelecommunicationsPhysicsWireless

Abstract

fetched live from OpenAlex

Design, testing, and performance of a 64-channel combinatorial electrochemical cell are described. This cell is used for high-throughput screening of materials for use as Li-ion rechargeable battery electrodes. Our sealed cell has 64 separate positive electrodes and Li foil for the reference and counter electrodes. This combinatorial electrochemical cell was designed to complement our existing combinatorial materials science infrastructure. Using the combinatorial electrochemical cell decreases the time and labor associated with test cell assembly, improves the repeatability of our assembly procedure, and increases the number of compositions we can test in a given amount of time. In this paper, we demonstrate that the results from the combinatorial electrochemical cell and from conventional 2325 coin-type test cells, containing electrodes of sputtered films prepared in the same sputtering run, are identical for electrodes of the same composition. © 2003 The Electrochemical Society. All rights reserved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations95
Published2003
Admission routes1
Has abstractyes

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