BOOSTING CO2 ELECTROREDUCTION VIA MEMBRANE ELECTRODE ASSEMBLIES WITH INCREASED CO2 CONVERSION RATES AND SELECTIVITY TOWARDS CO
Notice bibliographique
Résumé
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.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».