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Enregistrement 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 sur OpenAlexaboutno aff
Katarina Guzman, Uziel Landau

Notice bibliographique

RevueECS Meeting Abstracts · 2018
Typearticle
Langueen
DomaineChemistry
ThématiqueElectrochemical Analysis and Applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCopperElectroplatingElectrodeTexture (cosmology)Materials scienceAdsorptionSurface finishDeposition (geology)Surface roughnessChemical engineeringNanotechnologyChemistryComposite materialMetallurgyLayer (electronics)Organic chemistryComputer science

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,267
Écart entre enseignants0,247 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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