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Record W1549129423

Electrochemical characteristics of metal hydride electrodes for nickel/metal hydride rechargeable batteries

2002· article· en· W1549129423 on OpenAlexaboutno aff
Feng . Feng

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2002
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsHydrideNickelElectrochemistryMetalMaterials scienceElectrodeMetallurgyInorganic chemistryChemistry
DOInot available

Abstract

fetched live from OpenAlex

The electrochemical characteristics of LaNi4.7Al 0.3, LaNi4.7Al0.3 (with Cu-coating), Mm0.95 Ti0.05Ni3.85Co0.45Mn0.35Al 0.35 and Mm(Ni0.71Co0.14Al0.08Mn 0.06)5.02 alloy electrodes are examined in detail. The specific discharge capacity of the cell made using the LaNi4.7Al0.3 , Mm0.95Ti0.05Ni3.85Co0.45Mn 0.35Al0.35 and Mm(Ni0.71Co0.14Al 0.08Mn0.06)5.02 alloys maintain 250 mAh g -1 at 100--120 mA g-1 discharge current density after 20, 40 and 200 cycles respectively. Thus with regard to cycle lifetime the Mm(Ni0.71Co0.14Al0.08Mn 0.06)5.02 and Mm0.95Ti0.05Ni3.85 Co0.45Mn0.35Al0.35 alloys are considerably superior to LaNi4.7Al0.3 alloy. At the same number of cycles, all the electrochemical properties are related to the hydrogen concentration, i.e., depth of discharge (DOD). As the hydrogen concentration decreases, the exchange current density, the apparent activation energy, the hydrogen diffusion coefficient and the symmetry factor increase. The equilibrium potential increases (i.e. becomes more positive) with decreasing hydrogen concentration. Almost all the electrochemical properties are temperature related. As the temperature increases, both the exchange current density and the symmetry factor increase. The specific discharge capacity reaches a maximum value at room temperature for the Mm(Ni0.71Co0.14Al0.08 Mn0.06)5.02 alloy. The equilibrium potential decreases with increasing temperature. With increasing number of cycles, the exchange current density, the ratio of D/a2 ( D = hydrogen diffusivity; a = sphere radius) and the equilibrium potential increase with cycles, stabilizing after 30--40 cycles. Cu-coating of the electrode alloys increases the exchange current density and the high-rate dischargeability of the metal hydride electrodes, and decreases the discharge potential, especially for higher discharge current densities. The discharge potentials for the Cu-coated electrode showed little change with discharge current density differences, indicating the stabilizing effect of Cu-coating on the battery performance. A theoretical treatment is derived to account for the two-phase (alpha-beta) region of pressure-composition (P-C) isotherms of hydrogen-absorbing alloys by considering H-H interaction kinetics. Based on electrochemical reaction kinetics, a theoretical model on the relationship between equilibrium potential and hydrogen concentration is established for the equilibrium discharge process of a MH electrode. The relationship between equilibrium potential of a metal hydride electrode reaction and hydrogen pressure in a gaseous hydrogen environment is also derived and thus, E-C-T curves can be accurately transferred to P-C-T curves and vice versa. These theoretical equations are of particular use in evaluating suitable electrode alloys. A novel and relatively simple electrochemical method, called "Potential Step Chrono-Amperometry (PSCA)" method, is developed to determine the hydrogen diffusion coefficient and its variation with hydrogen concentration. Using this method, the value of the room temperature diffusion coefficient of hydrogen in a LaNi4.7Al0.3 alloy is found to be in the range of 3.1 x 10-14 to 8.6 x 10 -13 m2 s-1, and is comparable with the range of 10-10 to 10-15 m2 s-1 obtained for various AB5-type alloys by other methods.Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .F46. Source: Dissertation Abstracts International, Volume: 64-01, Section: B, page: 0366. Adviser: Derek O. Northwood. Thesis (Ph.D.)--University of Windsor (Canada), 2002.

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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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.006
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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.

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

Citations0
Published2002
Admission routes1
Has abstractyes

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