Current breakthroughs in the hardwood biorefineries: Hydrothermal processing for the co-production of xylooligosaccharides and bioethanol
Bibliographic record
Abstract
The development of lignocellulosic biorefineries requires a first stage of pretreatment which enables the efficient valorization of all fractions present in this renewable material. In this sense, this review aims to show the main advantages of hydrothermal treatment as a first step of a biorefinery infrastructure using hardwood as raw material, as well as, main drawback to overcome. Hydrothermal treatment of hardwood highlights for its high selectivity for hemicelluloses solubilization as xylooligosaccharides (XOS). Nevertheless, the suitable conditions for XOS production are inadequate to achieve an elevate cellulose to glucose conversion. Hence, several strategies namely the combination of hydrothermal treatment with delignification process, in situ modification of lignin and the mixture with another renewable resources (concretely, seaweeds, and by-products generated in the food industry with high sugar content) were pinpointed as promising alternative to increase the final ethanol concentration coupled with XOS recovery in the hydrolysate.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".