Synthesis of hydroxymethylfurfural and furfural from hardwood and softwood pulp using ferric sulphate as catalyst
Bibliographic record
Abstract
Hydroxymethylfurfural (HMF) and furfural are promising chemicals for the creation of a bio-based economy. The development of an inexpensive catalytic system for converting cellulosic biomass into these chemicals is an important step in this regard. Ferric sulphate is a common, cheap and non-toxic Lewis acid that has been used to catalyse reactions such as wood depolymerisation. In this work, ferric sulphate was used to help the production of HMF and furfural from hardwood and softwood pulps. It was found that for hardwood pulp, the use of ferric sulphate alone gave a maximum HMF yield of 31.6 mol-%. The addition of the ionic liquid [BMIM]Cl or HCl as co-catalysts did not lead to an increase in the yields obtained. A prior decationisation step, however, resulted in HMF yields of 50.4 mol-%. Softwood pulp was harder to depolymerise than hardwood, with a yield of 28.7% obtained using ferric sulphate alone. The maximum HMF yield from softwood, 37.9 mol-%, was obtained using a combination of ferric sulphate and dilute HCl. It was thus concluded that ferric sulphate is a promising catalyst for HMF synthesis from cellulosic biomass.
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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 | 0.000 |
| 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) | 0.001 | 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".