Computational Design of Two-dimensional Materials for Electrocatalysis
Notice bibliographique
Résumé
Computational Design of Two-dimensional Materials for ElectrocatalysisSamira Siahrostami aa Associate Professor, Department of Chemistry, Simon Fraser UniversityMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#MatInter - Materials and Interfaces for emerging electrocatalytic reactionsBarcelona, Spain, 2024 March 4th - 8thOrganizers: Marta Costa Figueiredo and María Escudero-EscribanoInvited Speaker, Samira Siahrostami, presentation 316DOI: https://doi.org/10.29363/nanoge.matsus.2024.316Publication date: 18th December 2023Electrocatalysis is at the heart of emerging renewable energy technologies such as fuel cells, electrolyzes, and rechargeable metal-air batteries, all of which are expected to play a significant role in transitioning to a more sustainable future. Two-dimensional materials have emerged as promising electrocatalysts with a wide range of application in electrocatalysis involving oxygen, carbon and nitrogen reactions. In this talk, I will present our recent progress on atomic scale design of two-dimensional materials for various electrocatalysis reactions. More specifically, I focus on computational catalyst design for 1) 2e- oxygen reduction reaction (ORR) for hydrogen peroxide (H2O2) synthesis1-4, and 2) CO2 reduction reaction (CO2RR)5-7. In the first part, I show various strategies that we have applied to tune the activity and selectivity of carbon-based materials for 2e-ORR to enhance the production of H2O2. The main drawback of carbon-based materials is related to their limited performance under acidic conditions which is desired for storage and transportation of H2O2. Extensive experimental work demonstrates that the majority of carbon-based materials are highly active in alkaline conditions and moderately active in neutral conditions. I discuss the computational efforts in understanding the pH effect on the activity of carbon-based structures as well as the insight we can gain from them. In the second part, I describe how we can tune carbon-based materials and two-dimensional metal organic frameworks for CO2RR as well as the insights we acquired from computational analysis. References:[1] Lu, Z.; Chen, G.; Siahrostami, S.; Chen, Z.; Liu, K.; Xie, J.; Liao, L.; Lin D.; Liu, Y.; Jaramillo, T.F.; Nørskov, J. K.; Cui, Y.[2] Jiang, K.; Back, S.; Akey, A.J.; Chuan, X.; Hu, Y.; Liang, W.; Schaak, D; Stavitski, E.; Nørskov, J.K.; Siahrostami, S.; Wang, H.[3] Han, G.; Li, F.; Zou, W.; Karamad, M.; Jeon, J.; Kim, W.; Kim, S.; Bu, Y.; Fu, Z.; Lu, Y.; Siahrostami, S.; Baek, J.[4] Chang, Q.; Zhang, P.; Mostaghimi, A.H.B.; Zhao, X.; Denny, S.R.; Lee, J.H.; Gao, H.; Zhang, Y.; Xin, H.; Siahrostami, S.; Chen, J.G.; Chen, Z.[5] Siahrostami, S.; Jiang, K.; Karamad, M.; Chan, K.; Wang, H.; Nørskov, J.[6] Kirk, C.; Chen, L. D.; Siahrostami, S.; Karamad, M.; Bajdich, M.; Voss, J.; Nørskov. J. K.; Chan, K.[7] Jiang, K.*; Siahrostami, S.*; Zheng, T.; Hu, Y.; Hwang, S.; Stavitski, E.; Peng, Y.; Dynes, J.; Gangisetty, M.; Su, D.; Attenkofer, K.; Wang, H.© FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».