Understanding the Aqueous-Phase Adsorption and Hydrogenation of Model Bio-Oil Molecules on Metals and Alloys
Notice bibliographique
Résumé
Valorization of biomass-derived molecules via aqueous-phase catalytic hydrogenation is a promising strategy for producing CO2-neutral fuels and chemicals to reduce our reliance on fossil fuels and lower our greenhouse gas emissions. However, the high cost and low activity of current catalysts prevent the economical implementation of the technology. The work herein focuses on gaining a fundamental understanding of the processes that govern aqueous-phase hydrogenation of biomass-derived compounds to inform the design of efficient materials for the production of sustainable chemicals. In Chapter 2, we measure the aqueous-phase adsorption enthalpies and free energies of phenol, benzaldehyde, furfural, benzyl alcohol, and cyclohexanol on polycrystalline Pt and Rh via experimental isotherm fitting and density functional theory modeling. We find that the experimental aqueous-phase adsorption enthalpies are between 50 to 250 kJ mol−1 less exothermic than calculated gas-phase enthalpies. We also find that there is a larger difference between the gas-phase and aqueous-phase enthalpies for Rh than there is for Pt. Aromatics adsorb with similar strength on Pt and Rh in the aqueous-phase, despite Rh binding compounds more strongly in the gas phase. A widely used implicit solvent model overpredicts the heats of adsorption for all organics compared with experimental measurements. However, accounting for the enthalpic penalty of displacing surface-adsorbed water molecules upon organic adsorption using a bond-additivity model explains the greatly reduced heats of adsorption and rationalizes the similar binding strength on Pt and Rh in the aqueous phase. In Chapter 3, we identify the active facet of Pt and Rh catalysts for aqueous-phase hydrogenation of phenol and explain the origin of size-dependent activity trends observed on Pt and Rh nanoparticles. We extract phenol adsorption energies on the active sites of Pt and Rh by fitting kinetic data, and we show that the active sites adsorb phenol weakly. We predict turnover frequencies (TOF) on the (111) terraces and (221) steps of Pt and Rh with density functional theory modeling and mean-field microkinetic simulations and find that the (111) terraces are more active than the step sites. The higher activities of the (111) terraces are due to lower activation energies and weaker phenol adsorption, which prevents high coverages of adsorbed phenol from inhibiting hydrogen adsorption. Finally, we measure the TOF for phenol hydrogenation on Rh nanoparticles as a function of particle diameter and find that the TOF increases as a function of particle size, which is caused by larger particles having higher fraction of (111) terrace sites. Lastly, in Chapter 4, we investigate platinum-cobalt alloys for the hydrogen evolution reaction (HER) and the electrocatalytic hydrogenation (ECH) of phenol, and we evaluate the adequacy of the hydrogen adsorption energy as a descriptor the catalytic activity for both reactions. Through a combination of electrochemical measurements, DFT calculations, and kinetic modeling, we show that while that the hydrogen adsorption energy is a useful descriptor for HER, it is an insufficient descriptor for ECH of phenol. Structural characterization reveals that the PtxCoy catalysts have a surface containing both Co and Pt. DFT calculations paired with kinetic modeling of the PtxCoy surface corroborates our experimental finding that the ECH is not enhanced by weakening hydrogen adsorption. However, kinetic modeling predicts that platinum-cobalt catalysts with a core-shell may have enhanced ECH performance, warranting future consideration.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».