High-Yield and High-Efficiency Conversion of HMF to Levulinic Acid in a Green and Facile Catalytic Process by a Dual-Function Brønsted-Lewis Acid HScCl<sub>4</sub> Catalyst
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Lignocellulosic biorefineries have received considerable attention for the purpose of producing high-value chemicals and materials. Levulinic acid (LA) is an important biomass-derived platform chemical that is produced from sugar-based biomass. Unfortunately, the catalysts reported thus far have shortcomings, such as expensive starting materials, complicated synthesis or purification operations, and a low LA yield under harsh reaction conditions. Herein, we develop a novel dual-functional catalyst, HScCl 4, by combining Brønsted acid (HCl) and Lewis acid (ScCl 3 ) sites. The as-prepared HScCl 4 catalyst shows high efficiency and high selectivity for converting 5-hydroxymethylfurfural (HMF) to LA in a biphasic system consisting of methyl isobutyl ketone (MIBK) and water. The density functional theory (DFT) results show that the synergistic catalytic effect, originating from the Brønsted and Lewis acidic sites of HScCl 4, significantly decreases the energy barriers of reactants and intermediates, thus facilitating the conversion of HMF to LA. Moreover, the efficient separation of LA in the water-MIBK biphasic system by extracting LA to the MIBK phase minimizes the side reactions of LA and thus the formation of humins while significantly improving the LA yield. The conversion of HMF and the selectivity for LA are 100 and 95.6% at 120 °C for 35 min, respectively. The free energy (Δ G ) and activation energy ( E a ) of the reaction are −30 kcal mol –1 and 13.7 kJ mol –1, respectively. The developed process provides a green, sustainable, and efficient pathway to produce LA from biomass-derived HMF under mild conditions.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".