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Record W2802997228 · doi:10.1149/ma2018-01/44/2580

Inhibiting Dendritic Growth Using Additives: Efficacy of Deposit-Incorporating Additives vs. Additives Accumulating on the Electrode

2018· article· en· W2802997228 on OpenAlexaboutno aff
Katarina Guzman, Uziel Landau

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCopperElectroplatingElectrodeTexture (cosmology)Materials scienceAdsorptionSurface finishDeposition (geology)Surface roughnessChemical engineeringNanotechnologyChemistryComposite materialMetallurgyLayer (electronics)Organic chemistryComputer science

Abstract

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Electrodeposition, particularly when carried under transport limitations, may result in rough surface texture. The roughness elements often evolve into dendrites which may cause internal shorts in batteries, leading to catastrophic failure. Since just about all commercial electroplating processes use additives to control the texture and distribution of electrodeposits, it has been suggested to use similar additives to eliminate or minimize dendritic growth. Strickler and Landau presented an analysis1 showing that if inhibiting additives were to preferentially adsorb on the tips of the roughness elements, dendritic growth could indeed be suppressed. However, these authors also argued that any additive that adsorbs and accumulates on the surface will eventually cover the entire electrode, eliminating the preferential inhibition at the tips and thus negating the beneficial effects. In order to maintain the preferential tip coverage, additive removal mechanism must be provided, e.g., by additive incorporation into the deposit. A correct balance of additive transport, adsorption, and incorporation could maintain the tips preferentially covered while the remainder of the electrode can stay essentially free of inhibiting additives, leading to level deposition1. We report here the results of an experimental study that was conducted in order to test this hypothesis. Although copper is not used in advanced batteries, it was selected as a model system because the interaction of different additives in the copper system is well-characterized2,3. Copper was plated from acidified copper sulfate (0.1 M) solution onto a rotating disk electrode, in the presence of different additives. In order to accelerate roughness evolution and increase the number of roughness elements to yield reliable statistics, deposition was conducted close to the limiting current (i/iL=0.95). Following deposition (209 coulomb/cm2), the height of the roughness elements (0-200 mm) was measured by focusing on the roughness elements tips an optical microscope objective with calibrated stage elevation. Two additives were compared: polyethylene glycol (PEG) and polyethyleneimine (PEI). PEG adsorbs weakly on copper (through its polyether oxygen) and is known to remain on the electrode. By contrast, the stronger adsorbing PEI (due to its nitrogen moiety) is incorporated within the deposit. Results, summarized in Fig. 1, indicate, as expected, only small reduction in number and height of the roughness elements in the presence of PEG as compared to the pure copper system. Two PEG systems were compared: PEG 4000 (100 ppm) by itself, and PEG 4000 (100 ppm) in the presence of chloride (70 ppm), which is known to enhance the PEG adsorption2. Both PEG systems reduced the number of large (150 – 200 mm) roughness elements by only about 14% as compared to the pure copper system. By contrast, the PEI (10 ppm), which is known to incorporate within the deposit, reduced the number of large roughness elements by a significant amount (42%). These results give credence to the hypothesis that incorporating additives should be effective in inhibiting roughness evolution and dendritic growth, while additives that remain on the electrode are expected to have little effect. However, in order to irrevocably validate the hypothesis, more studies, encompassing additional additives, with precise control of the additives transport and incorporation balance, should be conducted. Acknowledgements This research was conducted as part of the NSF/DOD funded Research Experience for Undergraduates (REU) program on Electrochemical Engineering at Case Western Reserve University. NSF/DOD stipend to K.G. is gratefully acknowledged. References Alaina Strickler and Uziel Landau, “Arresting Dendritic growth during electrodeposition using special additives”, Abstract # 993, 223rd, Electrochemical Society Meeting, Toronto, Canada, May 15, 2013. Rohan Akolkar and Uziel Landau, Electrochem. Soc., 151, C702 (2004). Julie Mendez, Rohan Akolkar and Uziel Landau, Electrochem. Soc. 156, (11), D474-D479 (2009). Figure 1

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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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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