Influence of Alkali and Alkaline-Earth Metals on the Cleavage of Glycosidic Bond in Biomass Pyrolysis: A DFT Study Using Cellobiose as a Model Compound
Bibliographic record
Abstract
Fast pyrolysis is a promising technology for the production of renewable fuels and chemicals from lignocellulosic biomass. The product distribution (bio-oil, char) and the composition of bio-oil are significantly influenced by the presence of naturally occurring alkali and alkaline-earth metals (AAEMs). In this paper, we investigate, at the molecular level, the influence of Na(I), K(I), Ca(II), and Mg(II) ions on glycosidic bond breaking reactions using density functional theory. Glycosidic bond breaking reactions are categorized as direct C–O breaking mechanisms, namely, transglycosylation, glycosylation, and ring contraction and the two-step pathways, which include the mannose pathway, dehydration, and ring opening. Our calculations show that in the absence of metal, transglycosylation and dehydration pathways (activation barriers ∼55 kcal.mol –1 ) are kinetically most facile. The linkage type (α- or β-1,4) has an insignificant effect on kinetics of glycosidic bond cleavage. Mg(II) ions have a pronounced effect on lowering the activation barriers of glycosylation, ring contraction, and the mannose pathway, requiring activation enthalpies of 32–52 kcal.mol –1 . Conversely, Mg(II) and Ca(II) ions inhibit the dehydration pathway. Na(I) and K(I) ions do not significantly influence the activation barriers of glycosidic bond cleavage reactions, as the reduction is only about 5–10 kcal.mol –1 . Thus, AAEM ions exhibit different catalytic effects on glycosidic bond breaking reactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".