Thermal Decomposition of Acetic and Formic Acid Catalyzed by Red Mud—Implications for the Potential Use of Red Mud as a Pyrolysis Bio-Oil Upgrading Catalyst§Dedicated to Prof. Ulf Schuchardt on the occasion of his retirement.
Bibliographic record
Abstract
Acetic and formic acid impart a high acidity on pyrolysis bio-oil (obtained by fast pyrolysis of ligno-cellulosic biomass), which is one of the factors preventing its direct use as a fuel. At temperatures ≥ 330 °C, Red Mud, a waste byproduct of the aluminum industry produced at >70 million tons p.a., is a good catalyst for thermal decomposition of these acids. Formic acid can serve as an internal source of hydrogen through the formation of synthesis gas and the water gas shift reaction. The formation of C 6 −C 10 hydrocarbons in the nonpolar phase of the resulting product mixture and the identification of C 3 and C 4 hydrocarbons and CO 2 in the gas phase and acetone in the polar liquid phases can be rationalized through mechanisms involving ketene as the intermediate formed by acetic acid dehydration, with subsequent formation of acetone. Higher hydrocarbons, mostly alkanes and alkenes, are then formed through iterative aldol condensation, hydrogenation, hydrogenolysis, and deoxygenation reactions of the primary products. During the reaction, the Red Mud used in these reactions undergoes a distinct color change to gray, yielding a nonalkaline magnetic material containing Fe 3 O 4 and metallic iron rather than Fe 2 O 3 .
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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".