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Record W3024766237 · doi:10.1149/ma2020-01462645mtgabs

Ammonia Thermal Treatment Toward Topological Defects in Porous Carbon for Enhanced Carbon Dioxide Electroreduction

2020· article· en· W3024766237 on OpenAlexaff
Dong Yan, Előd Gyenge

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCatalysisDensity functional theoryCarbon dioxideElectrochemical reduction of carbon dioxideSelectivityGrapheneCarbon fibersMaterials scienceTopology (electrical circuits)ChemistryNanotechnologyCarbon monoxideComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The design of specific active sites for the carbon dioxide electro-reduction reaction (CO 2 RR), is crucial for determining product selectivity and catalysis performance. Here, we present an efficient NH 3 thermal treatment for thoroughly removing the pyrrolic-N and pyridinic-N dopants from 3D N-enriched graphene analogue particles, to create high density of topological defects as active sites for CO 2 RR. [1] Firstly we found that NH 3 showed a pronounced effect of eliminating specific N-containing moieties at elevated temperatures, superior to Ar. The identification of topological defects (pentagonal carbon polygon and 585 defect) was investigated by near-edge X-ray absorption fine structure measurements and local density of states analysis, and its formation mechanism was revealed by reactive molecular dynamics simulations.The as-prepared catalysts exhibited excellent performance in flooded half-cell experiments in 0.1 M KHCO 3 reaching current densities of 2.8 mA cm -2 and 4.4 mA cm -2 with FE CO of 92.7% and 89.2% at -0.6 V and -0.7 V v.s. RHE, respectively at room temperature and atmosphere pressure. These results are among the best performances reported for metal-free catalysts. [2,3,4,5] Density functional theory calculations revealed that the edged pentagonal sites (penta-1 and 585-1 defects) are the dominating active centers with the lowest free energy barriers of CO 2 RR for CO production. [6,7] References [1] Zhu J.; Huang J.; Mei W. et al, Angew. Chem. Int. Ed. 2019 , 58 , 3859-3864 [2] Wu, J.; Liu, M.; Sharma, P. P. et al, Nano Lett. 2016, 16 (1), 466-70 [3] Wu, J.; Yadav, R. M.; Liu, M. et al, ACS Nano 2015, 9 (5), 5364-5371. [4] Daiyan, R.; Tan, X.; Chen, R. et al, Acs Energy Lett. 2018, 3 (9), 2292-2298. [5] Kumar, B.; Asadi, M.; Pisasale, D. et al, Nat. Commun. 2013, 4 , 2819. [6] Banhart F., Kotakoski J., Krasheninnikov A.V. ACS Nano, 2011 , 5 , 29-41. [7] Dong, Y.,zhang Q.J. et al, Ammonia Thermal Treatment toward Topological Defects in Porous Carbon for Enhanced Carbon Dioxide Electroreduction. (submitted) Figure 1

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)0.000
metaresearch head score (Gemma)0.000
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 score0.036
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.022
GPT teacher head0.258
Teacher spread0.235 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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