Dual-Site-Mediated Hydrogenation Catalysis on Pd/NiO: Selective Biomass Transformation and Maintenance of Catalytic Activity at Low Pd Loading
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Creating a new chemical ecosystem based on platform chemicals derived from waste biomass has significant challenges: catalysts need to be able to convert these highly functionalized molecules to specific target chemicals and they need to be economical—not relying on large quantities of precious metals—and maintain activity over many cycles. Herein, we demonstrate how Pd/NiO is able to direct the selectivity of furfural hydrogenation and maintain performance at low Pd loading by a unique dual-site mechanism. Sol-immobilization was used to prepare 1 wt % Pd nanoparticles supported on NiO and TiO 2, with the Pd/NiO catalyst showing enhanced activity with a significantly different selectivity profile; Pd/NiO favors tetrahydrofurfuryl alcohol (72%), whereas Pd/TiO 2 produces furfuryl alcohol as the major product (68%). Density functional theory studies evidenced significant differences on the adsorption of furfural on both NiO and Pd surfaces. On the basis of this observation we hypothesized that the role of Pd was to dissociate hydrogen, with the NiO surface adsorbing furfural. This dual-site hydrogenation mechanism was supported by comparing the performance of 0.1 wt % Pd/NiO and 0.1 wt % Pd/TiO 2 . In this study, the 0.1 and 1 wt % Pd/NiO catalysts had comparable activities, whereas there was a 10-fold reduction in performance for 0.1 wt % Pd/TiO 2 . When TiO 2 is used as the support, the Pd nanoparticles are responsible for both hydrogen dissociation and furfural adsorption and the activity is strongly correlated with the effective metal surface area. This work has significant implications for the upgrading of bioderived feedstocks, suggesting alternative ways for promoting selective transformations and reducing the reliance on precious metals.
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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".