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Record W4309813924 · doi:10.1149/ma2022-02542021mtgabs

Synthesis of Single-Atom and Dual-Atom Catalyst Using N-Defective C<sub>3</sub>N<sub>4</sub>

2022· article· en· W4309813924 on OpenAlexaff
Sang yong Shin, Hyunjoo Lee

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsCatalysisElectrochemistryMetalCrystallographyAtom (system on chip)Materials scienceX-ray absorption fine structureChemistryPhysical chemistryElectrodeSpectroscopyPhysicsMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Single-atom catalysts, which have been studied tremendously in the past decade, are emerging as a new class of heterogeneous catalysts. It is now generally known that defect sites play an important role in forming single-atomic structures. In the case of SACs, defective sites provide not only space for fixation of metal precursor, but also electrons for stabilizing positive metal ions. Graphitic carbon nitride (g-C3N4) is one of the ideal 2D materials for the synthesis of SACs. C3N4 has enough anchoring sites which originate from the ordered structure of tri-s-triazines connected together. Furthermore, abundant nitrogen atoms in C3N4 can provide electrons to single metal atoms. however, C3N4 is unsuitable as support for electrochemical catalysts due to its low electrical conductivity. Therefore, we made a C3N4 shell on the outer surface of carbon black (Ketjen Black EC-600JD) to obtain conductive support (C@C3N4).[1] Pt was supported on C@C3N4 at 1, 2, 4, and 8 wt% by wetness impregnation method. Through XRD, HAADF-STEM, and XAFS analysis, it was confirmed that a single-atomic structure was formed only when the Pt content was 1 or 2 wt%. In addition, I confirmed that the H2O2 selectivity (%) in the electrochemical oxygen reduction reaction was different according to the Pt content of the catalysts. In terms of controlling reaction sites at the atomic level, the challenge above creating single-atomic structures is creating structures in which two or three atoms are formed as dimer or trimer. Unlike single-atom catalysts, in a dimer or trimer structure, two or more atoms are adjacent to each other, so they can exhibit completely different catalytic properties. Finely controlling the defect site of the support can be a good strategy. Therefore, I adopted the strategy of making a defect in C3N4 to synthesize a dimer structure. It has been reported that N-vacancy can be selectively formed on C3N4 by additional heat treatment with NaBH4.[2, 3] In addition, DFT calculation results have been reported that Pd-Cu dimer structure can be stably formed on nitrogen-defective C3N4.[4] In this work, I create N-vacancy on a Pt1/C@C3N4 by additional heat treatment with NaBH4, and Co atoms were deposited as a secondary transition metal. The research strategy was to anchor the Co atom to the N-vacancy formed around the Pt single-atom, resulting in the formation of a dimer structure. First of all, the formation of N-vacancy on C@C3N4 support was clearly confirmed by FT-IR and XPS analysis. To confirm the dimer structure of Pt and Co, XAFS analysis was conducted. As a result, although not all Pt and Co atoms formed a dimer structure due to the limitation of the synthesis method, the intended structure of Co atom connected to a Pt atom was confirmed. In the Pt L3 edge EXAFS, a peak arising from Pt-Co scattering was observed at 2.4 Å. Furthermore, to confirm that PtCo alloy particles were not formed, high-result HAADF-STEM images were taken. All metal atoms were atomically dispersed, and nanoparticles were not found. This research demonstrated that defect engineering on support material can successfully modify active site on the atomic scale. References [1] Lee, I. H.; Cho, J.; Chae, K. H.; Cho, M. K.; Jung, J.; Cho, J.; Lee, H. J.; Ham, H. C.; Kim, J. Y., Appl. Catal. B, 2018, 237, 318-326. [2] Wen, Y.; Qu, D.; An, L.; Gao, X.; Jiang, W.; Wu, D.; Yang, D.; Sun, Z., ACS Sustain. Chem. Eng. 2019, 7 (2), 2343-2349. [3] Yu, H.; Shi, R.; Zhao, Y.; Bian, T.; Zhao, Y.; Zhou, C.; Waterhouse, G. I. N.; Wu, L.-Z.; Tung, C.-H.; Zhang, T., Adv. Mater. 2017, 29 (16), 1605148. [4] Cao, Y.; Zhao, C.; Fang, Q.; Zhong, X.; Zhuang, G.; Deng, S.; Wei, Z.; Yao, Z.; Wang, J., J. Mater. Chem. A, 2020, 8 (5), 2672-2683.

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 score0.001
Threshold uncertainty score0.002

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.243
Teacher spread0.227 · 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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Citations1
Published2022
Admission routes1
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