Lignocellulose pretreatment by deep eutectic solvents and related technologies: A review
Bibliographic record
Abstract
Lignocellulose is the main component of plants and has a wide range of sources. The high-value production of lignocellulose lies in the biorefinery of lignin, cellulose and hemicellulose. The advantages and disadvantages of traditional lignocellulose pretreatment methods were summarized, and the effective pretreatment parameters were listed. As a green solvent system with excellent performance, deep eutectic solvents (DES) are considered to be the most potential biomass pretreatment system. Based on this, the new trend and progress of DES in lignocellulose pretreatment were reviewed, focusing on the effects of distinct kinds of lignocellulose raw materials, distinct components of DES, distinct reaction conditions and assisted by microwave ultrasound on the pretreatment of lignocellulose, and the recyclability of DES solution system was discussed. Finally, the application and development direction of DES in lignocellulose pretreatment are proposed and prospected.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".