Understanding Acidity of Molten Salt Hydrate Media for Cellulose Hydrolysis by Combining Kinetic Studies, Electrolyte Solution Modeling, Molecular Dynamics Simulations, and <sup>13</sup>C NMR Experiments
Bibliographic record
Abstract
Depolymerization of lignocellulosic biomass in concentrated metal salts and more specifically in acidified LiBr molten salt hydrate (AMSH) results in high glucose yields at low acid concentrations, low temperatures, and very short times with potentially considerable economic benefits. However, our understanding of this promising medium is limited. Here, we study the effect of different LiBr concentrations on acidity and hydrolysis of cellobiose, a cellulose surrogate molecule, in dilute H 2 SO 4 solutions. We use thermodynamic modeling to predict the H + (hydron) activity and the speciation and correlate these with the experimentally measured reaction rates. We find that the main contribution of the salt to the reactivity stems from the dramatic increase in H + activity and secondary to an interaction of salt with the acid species that effectively renders the inorganic acid very strong. We perform molecular dynamics simulations and reveal that the increased hydron activity can be attributed to the decrease in the number of water molecules in the hydron solvation shell upon salt addition. Additionally, we extend the analysis to other salts and acids, concluding that the effects of different cations, anions, and acids in cellobiose hydrolysis likewise can be attributed to primarily changes in acidity. A key physicochemical descriptor of various salts is their enthalpy of dissolution. Finally, we explore the use of 13 C NMR spectroscopy to estimate the pH of AMSH solutions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".