Rapid Dissolution of Lignocellulosic Plant Materials in an Ionic Liquid
Bibliographic record
Abstract
Abstract Concerns regarding the non-renewable nature of, and pollution from, petroleum derived energy and commercial products has led to the concept of a biomass economy. As part of this vision for a society based on sustainable biomaterials, proposed biorefineries need to tackle the challenges of taking a wide diversity of raw biomass and rapidly and effectively transforming it into functionalizable platform molecules that can be derivatized into industrial and consumer products, or converted into biofuels. A substantial research effort is underway focussed on degrading biomass into smaller constituents using a variety of physical, chemical, and biological processes. One promising technology for the solubilization of biomass is ionic liquids (ILs), which has received considerable attention as a medium for efficient solubilization of a variety of materials. ILs also allow fractional separation when combined with solvent extraction (conventional, and green technologies such as supercritical CO2), precipitation, and adsorption/absorption methods, and to conduct a wide range of chemical reactions using thermal, electrochemical, photochemical, and biocatalytic processes. As a potential pretreatment technology for the biorefineries of the future, we report herein the first rapid dissolution of a range of coniferous and deciduous woods and grassy lignocellulosic plant materials in an IL using microwave radiation.
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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) | 0.000 | 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".