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Record W4285398494 · doi:10.1149/ma2022-01391739mtgabs

Elucidating the Reaction Pathways Responsible for Carbon Monoxide Production in Solid Oxide Co-Electrolysis Cells: A Theoretical Framework

2022· article· en· W4285398494 on OpenAlexaff
Anders S. Nielsen, Brant A. Peppley, Odne Stokke Burheim

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectrolysisCarbon monoxidePower to gasElectrochemical reduction of carbon dioxideWater-gas shift reactionSyngasHydrogen productionElectrolysis of waterSteam reformingCarbon dioxideHigh-temperature electrolysisChemistryHydrogenChemical engineeringOxideWaste managementCatalysisOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

The continuous emission of anthropogenic greenhouse gases, in tandem with the demand for renewable and intermittent energy storage systems, has given rise to the development of technologies which can convert carbon dioxide into useful chemicals and feedstocks. Solid oxide co-electrolysis cells have the potential to help address these demands, as they can efficiently convert carbon dioxide and steam into synthesis gas (carbon monoxide and hydrogen) utilizing clean electricity, which can then be supplied downstream to Fischer-Tropsch reactors in order to produce synthetic liquid fuels. Advantages of solid oxide co-electrolysis cells are mainly attributed to their capacity to operate at high temperatures, which in turn enhances the catalytic performance of electrode materials and reduces the amount of energy required to facilitate the electrochemical reduction of carbon dioxide and steam. The development of these devices has consequently drawn significant attention from designers and researchers, and have emerged as a promising alternative for renewable energy storage and synthesis gas production. One important challenge that has arisen in the analysis of solid oxide co-electrolysis cells is determining the reaction pathways responsible for carbon monoxide production. Studies have suggested that the reverse water gas shift reaction is the primary mechanism [1,2], since the co-electrolysis polarization curves have been shown to be identical to those obtained in pure steam electrolysis, while others have found that such curves lie between the results produced by pure steam and pure carbon dioxide electrolysis, thus indicating the electrochemical reduction of carbon dioxide and reverse water gas shift reaction are both contributors [3,4]. Given the varying operating conditions and cell designs that have been implemented in these studies, it is difficult to draw meaningful comparisons between these results and to deduce the reaction pathways by which carbon monoxide is generated. The objective of the current work is to, therefore, develop a theoretical framework to elucidate the reaction pathways which govern carbon monoxide production for a broad range of operating conditions and design specifications. An in-house 1-Plus-1D transport model is developed to quantify how each transport mechanism affects the rate of production and consumption of carbon monoxide, as well as the operating potential of a solid oxide co-electrolysis cell. This analysis is intended to cultivate an improved understanding of the reaction pathways responsible for carbon monoxide production and to reveal how they can be exploited in order to enhance the performance of solid oxide co-electrolysis cells. [1] P. Kim-Lohsoontorn and J. Bae, J. Power Sources, 196 , 7161 (2011). [2] C. Stoots, J. O’Brien and J. Hartvigsen, Int. J. Hydrogen Energy , 34 , 4208 (2009). [3] C. Graves, S. D. Ebbesen and M. Mogensen, Solid State Ion. , 192 , 398 (2011). [4] W. Li, H. Wang, Y. Shi and N. Cai, Int. J. Hydrogen Energy , 38 , 11104 (2013).

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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.127
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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
Published2022
Admission routes1
Has abstractyes

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