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Record W2796489411 · doi:10.1149/ma2018-01/1/54

Atomic Layer Deposition of Catalytic Manganese Oxide for High Surface Area Zinc-Air Battery Electrodes

2018· article· en· W2796489411 on OpenAlexaff
M.P. Clark, Ken Cadien, Douglas G. Ivey

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRenewable energyBattery (electricity)ElectrolyteEnergy storageAtomic layer depositionFossil fuelMaterials scienceCathodeCatalysisNanotechnologyChemical engineeringEnvironmental scienceWaste managementLayer (electronics)ChemistryElectrodeElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Driven by the need to move away from the use of fossil fuels, clean energy technologies such as wind and solar have seen tremendous technological and economic development in the last decade. The rapidly falling cost of wind and solar energy has made these technologies economically competitive with fossil fuels, promoting large growth. Renewable energy grew by 14.1% in 2016 alone.[1,2] With the continuing growth of renewable technologies, comes the need for cheap, safe and reliable energy storage solutions. Currently Li-ion batteries are seeing widespread use for renewable energy storage. However, the use of metallic Li in these batteries makes them expensive and unsafe. Zinc-air batteries are an attractive alternative to Li-ion batteries because they are inexpensive, safe, environmentally benign, and have excellent energy density.[3] The cathode for Zn-air batteries has a number of design requirements in order to facilitate the reduction of oxygen from the air. The cathode must be hydrophobic and porous to allow for diffusion of oxygen into the cell while maintaining intimate contact with the electrolyte within the cell. It also must be conductive and have sufficient loading of an effective catalyst to improve the poor kinetics of the oxygen reduction reaction. Catalyst distribution on/in the cathode is very important as well, as effective surface area of the catalyst is critical to battery performance. Since the oxygen reduction reaction utilizes oxygen from the air, three phase boundaries between air, electrolyte, and catalyst are of key importance.[3] Atomic layer deposition (ALD) is a gas phase deposition technique capable of producing high purity thin films of a wide variety of materials. The development of ALD techniques has largely been driven by the strict material and design challenges of the semiconductor industry. ALD utilizes alternating pulses of reactants that each undergo self-limiting reactions on the sample surface. These self-limiting reactions give rise to a number of useful properties of ALD films such as uniformity, conformality, composition control and thickness control on the order of Ångstroms.[4] ALD can be used to deposit catalytic material directly onto high surface area electrodes, such as porous carbon paper, so that the internal surfaces of the electrode can all be coated. By coating the entire porous structure of the electrode, the effective catalyst surface area and three phase boundary area can be greatly increased. In this work, an ALD process is developed to deposit Mn oxide (MnO x ) catalytic films directly onto porous carbon for application as the air electrode in Zn-air batteries. Initial depositions are conducted on Si wafers so that in-situ spectroscopic ellipsometry can be used to monitor deposition behavior. Once optimal ALD parameters are finalized, MnO x films of varying thicknesses are prepared on carbon electrodes and tested for their reactivity towards the oxygen reduction reaction. MnO x has a variety of oxidation states and crystal structures, each with varying degrees of catalytic activity. In order to maximize performance, various deposition and annealing conditions will be used to generate different MnO x phases. Deposits are characterized using a variety of electrochemical and materials techniques including linear sweep voltammetry, electrochemical impedance spectroscopy, galvanostatic cycling, scanning and transmission electron microscopy, x-ray diffraction, x-ray photoelectron spectroscopy and Raman spectroscopy. [1] BP Statistical Review of World Energy 66 th Edition, June 2017 [2] G. Jifan, “The next energy revolution is already here”, World Economic Forum , September 20 2017, [online] https://www.weforum.org/agenda/2017/09/next-energy-revolution-already-here/ [3] J. Lee, S. T. Kim, R. Cao, N. Choi, M. Liu, K. T. Lee, “Metal–Air Batteries with High Energy Density: Li–Air versus Zn–Air”, Adv. Energy Mater. , 1 (2011) 34-50 [4] S. M. George, “Atomic Layer Deposition: An Overview”, Chem. Rev. , 110 (2010) 111-131

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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)0.000
metaresearch head score (Gemma)0.000
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 score0.014
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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