Conversion of Wood-Based Hemicellulose Prehydrolysate into Succinic Acid Using a Heterogeneous Acid Catalyst in a Biphasic System
Bibliographic record
Abstract
A novel approach for the conversion of biomass-based hemicellulose prehydrolysate to high-value succinic acid has been investigated using a heterogeneous acid catalyst, Amberlyst 15 and hydrogen peroxide, in a biphasic system. A vital intermediate in this process, furfural, was oxidized in the biphasic system to produce succinic acid. Production of furfural in good yields is a limiting step in such processes for a number of reasons. Among the organic solvents evaluated, toluene was found to be an ideal solvent for furfural extraction and to facilitate the conversion of furfural to succinic acid. Simultaneous extraction of furfural into the organic solvent as it was produced increased the overall yield. It was observed that the developed method resulted in a succinic acid yield of 52.34% from the furfural obtained from hemicellulose prehydrolysate. It was found that 50 mg of Amberlyst 15 per millimole of furfural resulted in 100% furfural conversion in less time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".