Understanding the Aqueous-Phase Adsorption and Hydrogenation of Model Bio-Oil Molecules on Metals and Alloys
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".