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Record W4285398870 · doi:10.1149/ma2022-01391770mtgabs

Tailoring a Three-Phase Microenvironment for High-Performance CO<sub>2</sub> Electroreduction

2022· article· en· W4285398870 on OpenAlexaff
Shaoqing Liu, Ehsan Shahini, Min‐Rui Gao, Gong Lu, Peng‐Fei Sui, Tian Tang, Hongbo Zeng, Jing‐Li Luo

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCatalysisOverpotentialChemical engineeringAqueous solutionElectrolyteChemistryElectrolysisDiffusionSolubilityPhase (matter)Materials scienceInorganic chemistryElectrochemistryOrganic chemistryPhysical chemistryElectrodeThermodynamics

Abstract

fetched live from OpenAlex

Modern industrialization is accompanied with the extensive usage of fossil fuels for energy demands and consequently, an excessive emission of CO2 into the atmosphere. To combat the injurious greenhouse effects, the CO2 electroreduction (CER) to feedstocks and fuels becomes an appealing approach to reducing CO2 emission and simultaneously producing useful products. It is generally recognized that the solid-catalyst/liquid-electrolyte/gaseous-CO2 triple-phase boundary is the key microstructure feature of CER, where CO2 molecules react with protons (H+) and e− and are reduced. Besides the intrinsic activity of catalyst itself and the accessibility of active sites, CER performance also strongly depends on the transport of H+ and CO2 molecules through electrolyte to catalyst surface. Apparently, the availability of H+ in aqueous solution can be readily achieved via H2O ionization, while the low solubility of CO2 limits the supply of CO2 to the catalyst surface. Moreover, previous study has demonstrated that complete depletion of CO2 on the catalyst surface can even occur when high overpotential is applied.1 In this regard, rationally designing the structure of catalyst to increase the concentration of CO2 at the triple-phase boundary is immensely significant for CER since this could overcome the limited diffusion of CO2 in aqueous medium. Recently, surface hydrophobicity engineering has been proved to be a wise tactic to increase the local CO2 concentration by trapping CO2 near the catalyst surface, thus improving CO2 electrolysis. For example, modification of the hydrophobic organics on catalyst surface could create triple-phase boundary and increase CO2 concentration on catalyst surface, these improved CER performance and simultaneously inhibited HER.2 Unfortunately, the insulative organics coated on the catalyst surface will sacrifice its activity, while some small pieces of organics may desorb from the surface or in the case of flow cell, these pieces can be flushed away by the fluid. Since most catalysts are in situ grown on carbon support with the advantages of rapid electron transfer and seamless contact,3, 4 it is possible to tailor the microenvironment around the catalyst through chemical modification of carbon support. For example, platinum-based catalysts supported on hydrophobic carbon with a desirable microenvironment display a state-of-the-art catalytic activity for oxygen reduction reaction.5 However, few studies have been conducted to investigate how the carbon support can be modified to create a favourable triple-phase boundary for CER. In this study, we have in situ grown Bi2O3 nanosheets (NSs) on two types of carbon materials, hydrophobic carbon nanofiber (Bi2O3@C/HB) and hydrophilic carbon nanofiber (Bi2O3@C/HL), respectively, and used them as the cathode catalysts for CER. Compared to Bi2O3@C/HL, the as-obtained Bi2O3@C/HB exhibits significantly boosted CER performances for formate formation with the high FEformate of ˃ 93% over an extremely wide potential window of 1000 mV, high formate partial current density (jformate ) of 102.1 mA cm−2 and high formate formation rate of 1905 μmol h−1 cm−2. Molecular dynamics (MD) simulations together with electrochemical measurements reveal that the hydrophobic carbon support can create a hydrophobic microenvironment by avoiding the formation of hydrogen bond. This increases the local CO2 concentration and pH, both contributing to the enhancement of the overall CER. We believe that the findings from this work can provide significant guidelines for designing highly active CER catalysts and showcase a promising approach to improving other types of electrolysis involving gas phase. Raciti, D.; Mao, M.; Park, J. H.; Wang, C., Mass transfer effects in CO2 reduction on Cu nanowire electrocatalysts. Catal. Sci. Technol. 2018, 8, 2364-2369. Wang, J.; Cheng, T.; Fenwick, A. Q.; Baroud, T. N.; Rosas-Hernández, A.; Ko, J. H.; Gan, Q.; Goddard Iii, W. A.; Grubbs, R. H., Selective CO2 Electrochemical Reduction Enabled by a Tricomponent Copolymer Modifier on a Copper Surface. J. Am. Chem. Soc. 2021, 143, 2857-2865. Liu, S.; Lu, X. F.; Xiao, J.; Wang, X.; Lou, X. W., Bi2O3 nanosheets grown on multi‐channel carbon matrix to catalyze efficient CO2 electroreduction to HCOOH. Angew. Chem., Int. Ed. 2019, 58, 13828-13833. Li, F.; Chen, L.; Knowles, G. P.; MacFarlane, D. R.; Zhang, J., Hierarchical mesoporous SnO2 nanosheets on carbon cloth: a robust and flexible electrocatalyst for CO2 reduction with high efficiency and selectivity. Angew. Chem., Int. Ed. 2017, 56, 505-509. Zhao, Z.; Hossain, M. D.; Xu, C.; Lu, Z.; Liu, Y.-S.; Hsieh, S.-H.; Lee, I.; Gao, W.; Yang, J.; Merinov, B. V., Tailoring a Three-Phase Microenvironment for High-Performance Oxygen Reduction Reaction in Proton Exchange Membrane Fuel Cells. Matter 2020, 3, 1774-1790.

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.237
Teacher spread0.225 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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