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Record W2587855200 · doi:10.1149/2.0951704jes

pH-Controlled Electrolysis of Electrolytic Manganese Dioxide (EMD) for Improved Cycle Life of Rechargeable MnO<sub>2</sub>/Zn Batteries

2017· article· en· W2587855200 on OpenAlexaff
Farhang Nesvaderani, Arman Bonakdarpour, David P. Wilkinson

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsManganeseElectrolysisElectrochemistryElectrolyteCrystallinityChemistryMaterials scienceInorganic chemistryMetallurgyElectrodeComposite material

Abstract

fetched live from OpenAlex

A systematic study of the effect of acid concentration (0.5–5 M H2SO4) on the electrolysis of electrolytic manganese dioxide (EMD or γ-MnO2) has been performed. All the samples were characterized for their phase and crystallinity, their structural water, the pyrolusitic-to-ramsdellite ratio, BET surface area, pore size range; and were examined for their electrochemical performance. Concentration of the electrolysis acid affects the structural water content and the pyrolusitic-to-ramsdellite ratio of the γ-MnO2 phase as well as the surface area and pore size. The best cycling performance was achieved for the samples prepared with an electrolysis bath containing an acid concentration of about 2 M H2SO4. Electrochemical performance over 100 cycles of the EMD prepared in 2 M H2SO4 has shown higher energy efficiency and improved longer term cycling performance compared to commercial EMD products. Post-mortem analysis of the cycled EMD samples show partial loss of the EMD phase, loss of water content, and growth of irreversible manganese dioxide phases.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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