Novel synthesis of Lewis and Bronsted acid sites incorporated CS-Fe3O4@SO3H catalyst and its application in one-pot synthesis of tri(furyl)methane under aqueous media
Bibliographic record
Abstract
A sustainable chitosan (CS)-derived magnetic solid acid catalyst (CS-Fe3O4@SO3H) incorporated by Lewis and Bronsted acid sites was synthesized in an eco-friendly manner through the preloading of iron on CS and one-pot low-temperature carbonization/sulfonation. The carbonization/sulfonation of CS-Fe3O4 using p-Toluenesulfonic acid (p-TSA) at 140 oC resulted in the loss of ammonia in some extent and provided bifunctional sites on the catalyst. This heterogeneous catalyst was found to be highly selective for the conversion of xylose and arabinose to furfural (FF) and subsequent tri(furyl)methane (TFM) formation by the condensation with furan in the same reaction vessel without any purification. The outcome of optimization under different reaction parameters showed that only 20 wt.% of CS-Fe3O4@SO3H catalyst resulted in 81% TFM yield from xylose while arabinose gave a 70% TFM yield in dimethyl sulfoxide (DMSO):water with high selectivity. This green protocol provides an easy isolation of products and minimizes the formation of polymerized by-products. The catalyst can be readily recovered and efficiently reused for three consecutive catalytic cycles without any significant loss on product yields.
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) | 0.001 | 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".