Waste Biomass Selective and Sustainable Photooxidation to High-Added-Value Products: A Review
Bibliographic record
Abstract
Researchers worldwide seek to develop convenient, green, and ecological production processes to synthesize chemical products with high added value. In this sense, lignocellulosic biomass photocatalysis is an excellent process for obtaining various outcomes for the industry. One issue of biomass transformation via heterogeneous catalysis into valuable chemicals is the selection of an adequate catalyst that ensures high conversion and selectivity at low costs. Titanium oxide (TiO2), is widely used for several applications, including photocatalytic biomass degradation, depolymerization, and transformation. Graphite carbon nitride (g-C3N4) is a metal-free polymeric semiconductor with high oxidation and temperature resistance and there is a recent interest in developing this catalyst. Both catalysts are amenable to industrial production, relatively easy to dope, and suited for solar light absorption. Recent investigations also show the advantages of using heterojunctions, for biomass derivates production, due to their better solar spectrum absorption properties and, thus, higher efficiency, conversion, and selectivity over a broader spectrum. This work summarizes recent studies that maximize selectivity and conversion of biomass using photocatalysts based on TiO2 and g-C3N4 as supports, as well as the advantages of using metals, heterojunctions, and macromolecules in converting cellulose and lignin. The results presented show that heterogeneous photocatalysis is an interesting technology for obtaining several chemicals of industrial use, especially when using TiO2 and g-C3N4 doped with metals, heterojunctions, and macromolecules because these modified catalysts permit higher conversion and selectivity, milder reaction conditions, and reduced cost due to solar light utilization. In order to apply these technologies, it is essential to adopt government policies that promote the use of photocatalysts in the industry, in addition to encouraging active collaboration between photooxidation research groups and companies that process lignocellulosic 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".