MétaCan
Menu
← Back to cohort
Record W7046048518

BOOSTING CO2 ELECTROREDUCTION VIA MEMBRANE ELECTRODE ASSEMBLIES WITH INCREASED CO2 CONVERSION RATES AND SELECTIVITY TOWARDS CO

2023· dissertation· en· W7046048518 on OpenAlexafffund

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsMcMaster University
FundersSuncor Energy IncorporatedMcMaster UniversityCanadian Light Source
KeywordsCatalysisSelectivityRenewable energyElectrochemistryCarbideBoosting (machine learning)IntermetallicMetalGreenhouse gas
DOInot available

Abstract

fetched live from OpenAlex

To combat the escalating environmental challenges and alleviate the current energy crisis, CO2 conversion to fuels and chemical feedstocks provides a reliable approach to mitigate the devastating impact of greenhouse emissions on climate change. CO2 conversion/reduction could be carried out by several methods; however, the electrochemical CO2 reduction (CO2R) approach has coupled several advantages. For instance, CO2R occurs in near-ambient reaction conditions and could be driven through the employment of renewable energy resources (wind or solar) to generate electricity. However, this reaction has a large energy barrier which requires a catalyst to facilitate its pathway. In this context, various catalyst designs were developed and investigated during the last decades, such as heterogenous (metal and metal oxide) and homogenous (organic molecules) catalysts. A new class of materials – atomically dispersed metal nitrogen–doped carbon support (M–N–C)– has emerged recently and showed remarkable enhancement for CO2R compared to the state-of-the-art. In particular, Ni–N–C catalysts have demonstrated an improved selectivity toward CO production compared to precious metal catalysts. Researchers have postulated this superior performance to the high atomic utilization (theoretically 100%) of the metal sites under reaction conditions and the enhanced electronic properties. In addition, intermetallic carbides have been included as a promising class of catalysts for CO2R due to their unique physical and chemical characteristics. These catalysts could be synthesized using different precursors; among them, MOFs are currently one of the most promising platforms that generate several catalyst designs. It was demonstrated that MOF’s unique characteristics, such as high surface area and porosity, would be transitioned to the derived catalysts. In this thesis, two MOF architectures (ZIF-8 and MOF-74) were initially selected to be employed as precursors for deriving atomically dispersed Ni–N–C catalysts. Both MOF-derived catalysts were evaluated for CO2R using a customized electrochemical cell (E-cell) with a 3–electrode configuration. The derived Ni–N–C catalysts using ZIF-8 and MOF-74 have achieved enhanced CO selectivity with Faradaic efficiencies (FE) > 90% at less negative applied potentials, –0.68 and –0.76 V vs RHE, respectively. Further, various synthetic conditions were explored in these studies, such as the role of the Ni content and the pyrolysis temperature on the resulted catalyst structure, and the electrocatalytic performance during CO2 electrolysis. Subsequently, one of the MOF topologies – ZIF-8 – was further utilized to develop other designs of electrocatalysts by introducing different synthetic conditions. This has resulted in generating various moieties that are able to produce CO during CO2R. For example, one derived catalyst design consists of homogenously distributed atomically dispersed dual Ni–Zn–NX/C sites. Whereas the other design demonstrated a heterogenous structure of Ni3ZnC-based particles anchored on atomically dispersed dual Ni–Zn–NX/C sites. Both electrocatalyst designs were integrated into a gas diffusion electrode (GDE) and evaluated for CO2R using an MEA-based electrolyzer. Our findings revealed that the co-existence of Ni3ZnC particles and dual Ni–Zn–NX/C active sites in a heterogenous structure has boosted the electrocatalytic activity towards CO production, achieving near unity CO FE at 448 mA/cm2 at an overall cell voltage of 3.1 V. Aside from the electrocatalytic performance, the nature of active sites in the developed catalyst designs has been studied using in-situ and ex-situ X-ray absorption spectroscopy. Other analytical techniques such as transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), powder X-ray diffraction (PXRD), and X-ray photoelectron spectroscopy (XPS) have also been used to identify the catalysts’ composition and morphology.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueMacSphere (McMaster University)→Same topicMagnetic confinement fusion research→French-language works237,207→