Carbon Materials as Catalyst Supports and Catalysts in the Transformation of Biomass to Fuels and Chemicals
Bibliographic record
Abstract
Carbon plays a dual role as a catalyst or a catalyst support for chemical and enzymatic biomass transformation reactions due to its large specific surface area, high porosity, excellent electron conductivity, and relative chemical inertness. Advantageously, carbon materials can be prepared from residual biomass, an attractive property for decreasing the so-called “carbon-footprint” of a biomass transformation process. Carbon can be chemically functionalized and/or decorated with metallic nanoparticles and enzymes to impart or improve novel catalytic activity. Sulfonated porous carbon materials exhibit high reactivity in diversified catalytic reactions compared to their nonporous counterparts. However, the SO 3 H groups prevent the incorporation of hydrophobic molecules into the bulk, thereby causing hydrophobic acid-catalyzed reactions to proceed only on the surface. Metal and enzymatic catalysts on carbon supports have significant advantages over other oxide materials for different types of reactions. The future success of biorefinery will require the design of a new generation of multifunctional catalysts, possibly derived from emerging carbon materials such as graphene, carbon nanotubes, and carbon monoliths, for the selective processing of carbohydrates and lignin. The most achievable and economical way is to convert lignocellulosic biomass directly, rather than pure cellulose, hemicellulose, or lignin using multifunctional 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".