Thermal and Kinetic Studies on Biomass Degradation <i>via</i> Thermogravimetric Analysis: A Combination of Model-Fitting and Model-Free Approach
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Thermal degradation behavior and kinetics of two agricultural (soy and oat hulls) and two forestry biomass (willow and spruce) residues were investigated using a unique combination of model-fitting and model-free methods. Experiments were carried out in an inert atmosphere at different heating rates. Both single step and multistep models were explored in deriving activation energies, frequency factors, and mechanisms of all four biomass residues. For the single step models, activation energy values ranged from 107.2 kJ/mol for willow and 139.7 kJ/mol for soy hull, and the frequency factors for both materials were 1.1 × 10 9 and 2.66 × 10 12 s –1, respectively. The multistep models gave further insight into the different mechanisms across the full degradation spectrum. There was an observed difference between the number of distinct steps/mechanisms for the agriculture-based versus wood-based biomass materials, with pyrolysis occurring in three distinct steps for the agricultural biomass residues while the woody residues degraded in two steps. The difference in the number of distinct steps can be attributed to the composition and distribution of components of the biomass, which would differ based on the nature and source of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".