Analysis of the Catalytic Effects Induced by Alkali and Alkaline Earth Metals (AAEMs) on the Pyrolysis of Beech Wood and Corncob
Bibliographic record
Abstract
The catalytic pyrolysis of beech wood and corncob was experimentally investigated considering six additives containing alkali and alkaline earth metals (Na2CO3, NaOH, NaCl, KCl, CaCl2 and MgCl2). Thermogravimetric analyses (TGA) were carried out with raw feedstocks and samples impregnated with different concentrations of catalysts. In a bid to better interpret observed trends, measured data were analyzed using an integral kinetic modeling approach considering 14 different reaction models. As highlights, this work showed that cations (Na+, K+, Ca2+, and Mg2+) as well as anions (i.e., CO32−, OH−, and Cl−) influence pyrolysis in selective ways. Alkaline earth metals were proven to be more effective than alkali metals in fostering biomass decomposition, as evidenced by decreases in the characteristic pyrolysis temperatures and activation energies. Furthermore, the results obtained showed that the higher the basicity of the catalyst, the higher its efficiency as well. Increasing the quantities of calcium- and magnesium-based additives finally led to an enhancement of the decomposition process at low temperatures, although a saturation phenomenon was seen for high catalyst concentrations.
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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".