Novel catalytically active Pd/Ru bimetallic nanoparticles synthesized by Bacillus benzeovorans
Bibliographic record
Abstract
Abstract Bacillus benzeovorans assisted and supported growth of ruthenium (bio-Ru) and palladium/ruthenium (bio-Pd@Ru) core@shell nanoparticles (NPs) as bio-derived catalysts. Characterization of the bio-NPs using various electron microscopy techniques and high-angle annular dark field (HAADF) analysis confirmed two NP populations (1–2 nm and 5–8 nm), with core@shells in the latter. The Pd/Ru NP lattice fringes, 0.231 nm, corresponded to the (110) plane of RuO 2. While surface characterization using X-ray photoelectron spectroscopy (XPS) showed the presence of Pd(0), Pd(II), Ru(III) and Ru(VI), X-ray absorption (XAS) studies of the bulk material confirmed the Pd speciation (Pd(0) and Pd(II)- corresponding to PdO), and identified Ru as Ru(III) and Ru(IV). The absence of Ru–Ru or Ru–Pd peaks indicated Ru only exists in oxide forms (RuO 2 and RuOH), which are surface-localized. X ray diffraction (XRD) patterns did not identify Pd-Ru alloying. Preliminary catalytic studies explored the conversion of 5-hydroxymethyl furfural (5-HMF) to the fuel precursor 2,5-dimethyl furan (2,5-DMF). Both high-loading (9.7 wt.% Pd, 6 wt.% Ru) and low-loading (2.4 wt.% Pd, 2 wt.% Ru) bio-derived catalysts demonstrated high conversion efficiencies (~95%) and selectivity of ~63% (~20% better than bio-Ru NPs) and 58%, respectively. These materials show promising future scope as efficient low-cost biofuel catalysts.
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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".