Dual-Site-Mediated Hydrogenation Catalysis on Pd/NiO: Selective Biomass Transformation and Maintenance of Catalytic Activity at Low Pd Loading
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Bibliographic record
Abstract
Creating a new chemical ecosystem based on platform chemicals derived from waste biomass has significant \nchallenges; catalysts need to be able to convert these highly functionalised molecules to specific target chemicals, \neconomical – not relying on large quantities of precious metals - and maintain activity over many cycles. Herein, we \ndemonstrate how Pd/NiO is able to direct the selectivity of furfural hydrogenation and maintain performance at low Pd \nloading by a unique dual-site mechanism. Sol-immobilization was used to prepare 1 wt% Pd nanoparticles supported on \nNiO and TiO2, with the Pd/NiO catalyst showing enhanced activity with a significantly different selectivity profile; Pd/NiO \nfavours tetrahydrofurfuryl alcohol (72%), whereas Pd/TiO2 produces furfuryl alcohol as the major product (68%). Density \nfunctional theory studies evidenced significant differences on the adsorption of furfural on both NiO and Pd surfaces. Based on this observation we hypothesised 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/TiO2. In this study, the 0.1 and 1 wt% Pd/NiO catalysts had a comparable activity, whereas there was a 10-fold reduction in performance for 0.1 wt% Pd/TiO2. When using TiO2 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 bio-derived feedstocks, suggesting alternative ways for promoting selective transformations and reducing the reliance on precious metals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it