Synergistic Adsorption and In Situ Catalytic Conversion of SO<sub>2</sub>by Transformed Bimetal-Phenolic Functionalized Biomass
Bibliographic record
Abstract
SO 2 removal is critical to flue gas purification. However, based on performance and cost, materials under development are hardly adequate substitutes for active carbon-based materials. Here, we engineered biomass-derived nanostructured carbon nanofibers integrated with highly dispersed bimetallic Ti/CoO x nanoparticles through the thermal transition of metal-phenolic functionalized industrial leather wastes for synergistic SO 2 adsorption and in situ catalytic conversion. The generation of surface-SO 3 2– and peroxide species (O 2 2– ) by Ti/CoO x achieved catalytic conversion of adsorbed SO 2 into value-added liquid H 2 SO 4, which can be discharged from porous nanofibers. This approach can also avoid the accumulation of the adsorbed SO 2, thereby achieving high desulfurization activity and a long operating life over 6000 min, preceding current state-of-the-art active carbon-based desulfurization materials. Combined with the techno-economic and carbon footprint analysis from 36 areas in China, we demonstrated an economically viable and scalable solution for real-world SO 2 removal on the industrial scale.
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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".