Understanding the Effect of Solvent Environment on the Interaction of Hydronium Ion with Biomass Derived Species: A Molecular Dynamics and Metadynamics Investigation
Bibliographic record
Abstract
The addition of aprotic solvents results in higher reactivities and selectivities in many key aqueous phase biomass reactions, including the acid-catalyzed conversion of fructose to 5-hydroxyl methyl furfural (HMF). The addition of certain co-solvents inhibits the formation of humins via preferential solvation of key functional groups and can alter reaction kinetics. An important factor in this context is the relative stability of the hydronium ion (the catalyst) in the vicinity of the biomass moiety as compared to that in bulk, as it could determine its efficacy in the protonation step. Hence, in the present work, molecular dynamics (MD) simulations of HMF (the model product) and fructose (the model reactant) in acidic water and water-DMSO mixtures are performed to analyze their interaction with the hydronium ions. We show that the presence of DMSO favors the interaction of the hydronium ion with fructose, whereas it has a detrimental effect on the interaction of hydronium ion with HMF. Well-tempered metadynamics (WT-MTD) simulations are performed to determine the relative stability of the hydronium ion in the immediate vicinity of fructose and HMF, as compared to that in the bulk solvent phase, as a function of solvent composition. We find that DMSO improves the stabilization of the hydronium ions in the first solvation shell of fructose compared to that in the bulk solvent. On the other hand, hydronium ions become less stable in the immediate vicinity of HMF, as the concentration of DMSO increases.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".