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Record W2474978054 · doi:10.1017/s1431927615015470

Microscopy Techniques for Analysis of Nickel Metal Hydride Batteries Constituents

2015· article· en· W2474978054 on OpenAlex
Graham J.C. Carpenter, Zbigniew S. Wronski

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

VenueMicroscopy and Microanalysis · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of WaterlooNatural Resources Canada
FundersUniversity of Manchester
KeywordsHydrideNickelMaterials scienceMetalMetallurgy

Abstract

fetched live from OpenAlex

With the need for improvements in the performance of rechargeable batteries has come the necessity to better characterize cell electrodes and their component materials. Electron microscopy has been shown to reveal many important features of microstructure that are becoming increasingly important for understanding the behavior of the components during the many charge/discharge cycles required in modern applications. The aim of this paper is to present an overview of how the full suite of techniques available using transmission electron microscopy (TEM) and scanning transmission electron microscopy was applied to the case of materials for the positive electrode in nickel metal hydride rechargeable battery electrodes. Embedding and sectioning of battery-grade powders with an ultramicrotome was used to produce specimens that could be readily characterized by TEM. Complete electrodes were embedded after drying, and also after dehydration from the original wet state, for examination by optical microscopy and using focused ion beam techniques. Results of these studies are summarized to illustrate the significance of the microstructural information obtained.

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)0.001
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.314
Teacher spread0.288 · 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