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Record W4376881419 · doi:10.31399/asm.cp.itsc2023p0597

Tab-to-Busbar Interconnects Formed by Dual Flow Cold Spraying

2023· article· en· W4376881419 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

VenueThermal spray · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrical Contact Performance and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBusbarMaterials scienceInterconnectionElectrical engineeringElectrodeElectrical resistivity and conductivityBattery (electricity)CopperComposite materialPower (physics)MetallurgyEngineeringTelecommunicationsChemistry

Abstract

fetched live from OpenAlex

Abstract Battery manufacturing involves a large number of individual cells arranged in modules configured within a battery pack and connected either in series and/or parallel to deliver the required power and driving range. Cells within a module are linked using a tab-to-busbar connection as the electrical interconnect. Therefore, a battery pack contains a plurality of tab-to-busbar joints, and each must provide low electrical resistivity connection to minimize losses that may reduce the effective performance of the battery. In this work, the Dual Flow Cold Spray (DFCS) process, a modification of low-pressure cold spraying, was used to form low resistivity Cu+10%Zn and Al+10% Zn tab-to-busbar interconnects. As test coupons, 0.8 mm thick copper (Cu) was used to represent the busbar while 0.3 mm thick aluminum and nickel coated copper foils represented the respective electrode tabs. Low resistivity joint interconnects (≈100 μΩ) with high adhesion strength (≈120 MPa) have been formed. The influence of busbar surface preprocessing on the resistivity of the tab-to-busbar joints has been 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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.267
Threshold uncertainty score0.999

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)0.0000.002

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 head0.008
GPT teacher head0.208
Teacher spread0.201 · 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