Torréfaction de la biomasse lignocellulosique dans les liquides ioniques: Analyse comparative par spectroscopies de surface
Bibliographic record
Abstract
Abstract Torrefaction is a thermal pretreatment used to improve the physicochemical properties of lignocellulosic biomass prior to its thermochemical conversion. In this study, aspen wood imbued with a hydrophilic ionic liquid (1‐ethyl‐3‐methyl imidazolium trifluoromethanesulfonate) was torrefied, and the properties of the solid product were compared to those from conventional dry torrefaction. The solid products were characterized using X‐ray photoelectron spectroscopy X (XPS), photoelectron energy loss spectroscopy (ESCALOSS), and X‐ray excited Auger electron spectroscopy X (XAES) in order to study the biomass surface modifications due to ionic‐liquid torrefaction in comparison to dry torrefaction. The results obtained show that ionic liquids led to lignin degradation while dry torrefaction mostly affected the carbon‐rich extractives. Lignin degradation was monitored using ESCALOSS and XAES techniques, which unveiled reduction in sp2 hybridized carbon forms. Finally, ionic‐liquid assisted torrefaction of the main biomass components, i.e., lignin, cellulose and hemicellulose, treated individually, showed a decrease in their O/C ratio in line with improved hydrophobicity and robustness against moisture uptake by the torrefied products. The diversity of trends revealed by our surface analysis study during the torrefaction of bare or ionic‐liquid‐impregnated wood, especially by means of ESCALOSS and XAES, requires the pursuit of research in order to describe more in‐depth the physicochemical processes involved during torrefaction.
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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) | 0.001 | 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) | 0.001 | 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".