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Record W7133018033

Energy- and Carbon-efficient CO2 Electrolysis

2022· dissertation· W7133018033 on OpenAlexafffund
Adnan Ozden

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersQueen's UniversityInstitut de Ciències Fotòniques
KeywordsRenewable energyOverpotentialElectricityElectrolysisCarbon fibersEnergy storageProton exchange membrane fuel cellGalvanic cell
DOInot available

Abstract

fetched live from OpenAlex

Carbon dioxide/monoxide (CO2/CO) reduction (CO2R/COR) – when powered by renewable electricity – provides a sustainable means to convert emissions into valuable products for the manufacturing, transport, and chemical industries. CO2R can contribute to sustainability through closing the carbon loop, increasing the penetration of renewables in the petrochemical industry, and achieving long-term storage of renewable electricity. Despite considerable promise, there have yet been few demonstrations of CO2R with a rate, energy efficiency, and carbon efficiency that would bring techno-economics in line with incumbents. The main objective of the thesis is to contribute to the realization of energy- and carbon-efficient CO2R. The first three technical chapters of this thesis focus on improving the performance metrics in flow cells and membrane electrode assembly electrolyzers (Chapters 3, 4, and 5). The last two technical chapters focus on implementing the performance-boosting strategies into carbonate-formation-free systems to achieve simultaneously high carbon and energy efficiencies (Chapters 6 and 7). The first work reports a new catalyst design that decouples gas, ion, and electron transport and enables, for the first time, CO2R at activities greater than 1 A cm−2 in alkaline flow cell electrolyzers (Chapter 3). Then, low overpotential and high selectivity in CO2R is achieved via an adparticle functionalization catalyst: gold adparticles formed on the silver-gold alloying interface via galvanic replacement (Chapter 4). A molecule:ionomer hierarchy is developed to lower the activation barrier for C–C coupling and control CO2, water, and proton transport – which in turn enabled record energy efficiency towards ethylene at industrially relevant reaction rates (Chapter 5). A cascade system – CO2R to CO in a solid oxide electrolysis cell (SOEC) with zero carbonate formation and COR to C2+ products in a MEA electrolyzer – is developed to achieve record low energy intensities in the electrosynthesis of C2+ products (Chapter 6). Finally, a catalyst microenvironment exhibiting cation repulsion and anion attraction is developed to combine practical energy- and carbon-efficiency in electrosynthesis of C2+ from CO2/CO feedstocks (Chapter 7).

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 score0.003
Threshold uncertainty score0.010

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.278
Teacher spread0.271 · 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
Published2022
Admission routes2
Has abstractyes

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