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Record W2626806539

In operando spectroscopic studies of high temperature electrocatalysts used for energy conversion

2016· dissertation· en· W2626806539 on OpenAlexfundno aff
Melissa D. McIntyre

Bibliographic record

VenueMontana State University ScholarWorks (Montana State University) · 2016
Typedissertation
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
FundersDivision of Materials ResearchOffice of Naval ResearchU.S. Naval Research LaboratoryNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and TechnologyMontana State UniversityMontana Space Grant ConsortiumNational Science Foundation
KeywordsMaterials scienceEnergy transformationEnergy (signal processing)NanotechnologyChemical engineeringEngineeringThermodynamicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Solid-state electrochemical cells are efficient energy conversion devices that can be used for clean energy production or for removing air pollutants from exhaust gas emitted by combustion processes. For example, solid oxide fuel cells generate electricity with low emissions from a variety of fuel sources; solid oxide electrolysis cells produce zero-emission H2 fuel; and solid-state DeNO x cells remove NO x gases from diesel exhaust. In order to maintain high conversion efficiencies, these systems typically operate at temperatures > or = 500°C. The high operating temperatures, however, accelerate chemical and mechanical cell degradation. To improve device durability, a mechanistic understanding of the surface chemistry occurring at the cell electrodes (anode and cathode) is critical in terms of refining cell design, material selection and operation protocols. The studies presented herein utilized in operando Raman spectroscopy coupled with electrochemical measurements to directly correlate molecular/material changes with device performance in solid oxide cells under various operating conditions. Because excessive carbon accumulation with carbon-based fuels destroys anodes, the first three studies investigated strategies for mitigating carbon accumulation on Ni cermet anodes. Results from the first two studies showed that low amounts of solid carbon stabilized the electrical output and improved performance of solid oxide fuel cells operating with syn-gas (H 2/CO fuel mixture). The third study revealed that infiltrating anodes with Sn or BaO suppressed carbon accumulation with CH 4 fuel and that H 2O was the most effective reforming agent facilitating carbon removal. The last two studies explored how secondary phases formed in traditional solid oxide cell materials doped with metal oxides improve electrochemical performance. Results from the fourth study suggest that the mixed ion-electron conducting Zr 5Ti 7O 24 secondary phase can expand the electrochemically active region and increase electrochemical activity in cermet electrodes. The final study of lanthanum strontium manganite cathodes infiltrated with BaO revealed the reversible decomposition/formation of a Ba 3Mn 2O 8 secondary phase under applied potentials and proposed mechanisms for the enhanced electrocatalytic oxygen reduction associated with this compound under polarizing conditions. Collectively, these studies demonstrate that mechanistic information obtained from molecular/material specific techniques coupled with electrochemical measurements can be used to help optimize materials and operating conditions in solid-state electrochemical cells.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

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