MétaCan
Menu
← Back to cohort
Record W2523463846 · doi:10.1149/ma2016-02/2/216

Determining the Mechanism of Self-Discharge in Rechargeable Zn Electrodes Using a Novel but Simple Method

2016· article· en· W2523463846 on OpenAlexaff
Patrick Bonnick, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of WaterlooDalhousie University
Fundersnot available
KeywordsOverpotentialBattery (electricity)ElectrodeOpen-circuit voltageZincMaterials scienceSelf-dischargeChemistryNanotechnologyElectrical engineeringVoltageElectrochemistryMetallurgyEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Rechargeable zinc (Zn) electrodes for aqueous batteries are poised to play an important role in grid storage battery systems in the near future. Zn is favoured as a negative electrode material because it is cheap, non-toxic, has a low potential, fast kinetics and it has a high overpotential for hydrogen evolution. This last advantage is what allows typical primary alkaline AA batteries to last up to 10 years on the shelf, but this impressive shelf life is not shared by rechargeable batteries with Zn electrodes. For example, a PowerGenix Sub C NiZn battery has a shelf life of less than 1 year.1 Frustratingly, the rate determining step of self-discharge in rechargeable Zn electrodes has not been clearly identified in the literature since many authors have reported ambiguous and at times contradictory results in that regard.2 We have developed a simple, but novel, technique of measuring the self-discharge rate of rechargeable Zn electrodes, and have used it to elucidate the rate determining step of the Zn self-discharge mechanism. The method, deemed the charge-wait-discharge (CWD) technique, is depicted in Figure 1. It involves charging a Zn electrode, leaving it at open circuit for a set amount of time, and then discharging it to measure how much capacity remains. By performing this experiment repeatedly with increasing amounts of time left at open circuit, a plot of capacity vs open circuit time can be formed. The slope of this plot yields the self-discharge rate, as a coarse function of time. One of the principal advantages to this method is that it can easily be performed on full, unmodified cells, as opposed to the more traditional method of measuring self-discharge which involves measuring the volume of H2 gas released by the Zn electrode over a long period of time.3 Using the CWD technique, we explored the effect on self-discharge rate of KOH concentration in the electrolyte, surface area of the deposition, and current collector material. Our results indicate that only the current collector material has a strong effect on self-discharge rate, suggesting that the rate determining step of Zn self-discharge is H2 evolution on the current collector (as opposed to on the Zn deposit) as shown in Figure 2. In which case, to minimize Zn self-discharge the exposed surface area of the current collector should be minimized and the overpotential for H2evolution on the current collector material should be maximized. In the literature, several electrolyte additives have been claimed to reduce self-discharge rates, reduce dendrite growth and/or extend cycle life. The effect of these additives on self-discharge was screened using the CWD technique. These results will be presented, along with the best current collector material identified in our studies. References PowerGenix, http://powergenix.com/wp-content/uploads/2014/04/pgx_nizn_subc_datasheet2.pdf, Accessed: March 13, 2016, Last Updated: April 2009. X. G. Zhang, Corrosion and Electrochemistry of Zinc, 1st ed., Plenum Press, New York (1996). R. N. Snyder and J. J. Lander, Electrochem. Technol., 3, 5-6 (1965). Figure 1

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.265
Teacher spread0.237 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicAdvanced Sensor and Energy Harvesting Materials→French-language works237,207→