A Biorefinery Strategy That Introduces Hydrothermal Treatment Prior to Acid Hydrolysis for Co-generation of Furfural and Cellulose Nanocrystals
Bibliographic record
Abstract
Hydrothermal treatment of wood pulp at 150-225°C prior to acid hydrolysis was investigated in the context of isolating cellulose nanocrystals (CNCs). The objective was 2-folds as follows: (a) generating furfural as a value-added co-product; and (b) concentrating and forming new CNC precursors through thermal re-orientation of para-crystalline cellulose chains that will in turn improve CNC recovery and yield. Furfural yields up to 19 and 21% xylan conversion were obtained at 200 and 225°C hydrothermal treatments, respectively. In addition, these hydrothermal treatment conditions increased the crystallinity index of the pulp (77%) to 84 and 80%, respectively. Consequently, the CNC yield from hydrothermally treated wood pulp, when compared to untreated wood pulp, improved by up to 4- and 2-folds, respectively. An efficient acid hydrolysis process with yield improvements can translate to reduced CNC isolation and purification costs and increased production capacity. The qualities of the CNCs in terms of particle size and crystallinity were not affected due to hydrothermal treatment. However, the zeta potential, sulfur, hydrogen, and oxygen content of the CNCs were significantly lower at 225°C while carbon composition increased, and dark brown coloration was observed that indicates caramelization. This study demonstrates for the first time a novel biorefinery strategy that introduces hydrothermal treatment prior to acid hydrolysis to co-generate furfural and CNC with improved efficiency.
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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".