Bibliographic record
Abstract
The conversion of biomass into bio-oil through fast pyrolysis followed by its upgrading via hydrodeoxygenation (HDO) is considered a potential route for the production of renewable fuels. The present work aimed to develop new catalysts for the hydrodeoxygenation (HDO) of phenol, which is a typical bio-oil model compound. The work section was divided in 5 independent chapters: (i) a thermodynamic study in order to determine the most favorable operational conditions for the HDO of phenol. According to this study when methane was added to the system, the equilibrium composition calculated indicated only the formation of methane for all the conditions evaluated. However without CH4, the best operational conditions to perform the phenol HDO reaction are at intermediate temperatures and with high H2/phenol ratio; (ii) In sequence, the effect of to the addition of a second metal (Cu, Ag, Zn, Sn) on the performance of Pd/ZrO2 catalyst for HDO of phenol in the gas phase was studied. The incorporation of dopants to Pd/ZrO2 resulted in the formation of Pd–X (Cu, Ag, Zn) alloys, which reduced the reaction rate for HDO and increased the selectivity to hydrogenation products (cyclohexanone and cyclohexanol). However, the oxophilic sites generated by Sn cations promoted the hydrogenation of the carbonyl group of the keto-tautomer intermediate formed, producing benzene as the main product; (iii) The impact of particle size of ZrO2-supported Pd and of alloying with Ag was explored for hydrogenation of phenol in aqueous phase. This study was performed during an internship at PNNL (Pacific Northwest National Laboratory). Kinetic assessments were performed in a batch reactor, on monometallic Pd/ZrO2 samples with different Pd loadings (0.5%, 1% and 2%), as well as on a 1% PdAg/ZrO2 sample. In general, the lower activity of the small Pd particles was attributed to low activation entropies for the strongly bound species and the presence of Ag increases catalyst activity by decreasing the apparent energy of activation and increasing the coverages of phenol and H2, without negatively affecting the transition entropy; (iv) After that, based on the recent insides reported about the HDO reactions, the hydrodeoxygenation of phenol was studied using Rh, Pd and Ni catalysts supported on Nb2O5. This part allowed understanding how the SMSI (strong metal-support interaction) affects the selectivity of the HDO reaction. In general, an increase in the reduction temperature favored benzene selectivity, all the samples showed selectivity of approximately 95% for benzene for high reduction temperatures; (v) In the final chapter of this work the effect of doping cerium oxide support with niobium was investigated for HDO of phenol at 573K in the gas phase. The incorporation of niobium altered the lattice parameters of cerium based oxides, favored the reduction of the cerium and increased the selectivity to deoxygenated products (benzene). Small amounts of niobium affected the surface area of the support and promoted the formation of more dispersed nickel particles, which disfavored the hydrogenolysis of benzene. In general, the data presented in this thesis contributed to a better understanding of the HDO reaction. Keywords: hydrodeoxygenation, phenol, bimetallic, thermodynamics, niobium, oxides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".