Sustainable catalytic pathways for biofuel precursors: quantitative conversion of glucose to gluconic acid using Pt-Zn biochar catalyst
Bibliographic record
Abstract
Achieving quantitative conversion of biomass-derived feedstocks under ambient environmental conditions (20°C, atmospheric air, 0.1 MPa) is a critical milestone for sustainability in chemical processes. Herein, the quantitative conversion of glucose to gluconic acid was accomplished under ambient environmental conditions without any additives using a Pt-Zn intermetallic nanoparticle-supported biochar catalyst prepared from raw rice straw (Pt-Zn/strawC) via a straightforward one-pot solvothermal reaction with ethylene glycol solvent. Spectroscopic analyses verified the formation of the Pt-Zn intermetallic alloy and confirmed strong electronic metal-support interactions. The Pt-Zn/strawC catalyst (Pt:Zn molar ratio of 1:6) was highly selective for the conversion of glucose to gluconic acid, whereas yields as high as 99.9% (98.9% gluconic acid, 1.0% glucaric acid) were reached at 20 oC under base-free and additive-free conditions. Isotope measurements and density functional theory revealed synergistic interactions in the Pt-Zn alloy, wherein the alloy tended to absorb glucose and active O2 into superoxide radical (O2·). This work demonstrates a chemocatalytic method that is practical for environmental conditions and provides a new avenue for sustainable conversion of lignocellulosic biomass to chemical products.
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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) | 0.000 | 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 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".