Near-infrared plasmonic activation of molecular oxygen for selective oxidation of biomass derivatives
Bibliographic record
Abstract
Abstract Upgrading biomass feedstock into higher-value chemicals is central to improve the sustainability of the chemical industry and to reduce its dependence on fossil raw materials. Heterogeneous photocatalysts are promising for the oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA), a biomass-derived molecular platform for biopolymers, but their FDCA selectivity is negligible without the aid of a base. Here we present a plasmonic photocatalyst integrating TiN nanocubes and bimetallic RuPt nanoparticles that in base-free conditions exhibits complete HMF conversion and selective FDCA formation due to an unconventional mechanism of molecular oxygen activation. This unique reactivity is enhanced by both photothermal heating and hot electrons, whose contribution is confirmed by kinetic isotopic effect experiments. Density functional theory calculations support a scenario in which the activated nanoparticle–oxygen complex facilitates the rate-determining step and enables an improved FDCA selectivity. Our results demonstrate the potential of plasmonic photocatalysts in the catalytic transformation of biomass.
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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".