Enhancing lignocellulosic biorefinery sustainability: mechanisms and optimization of microwave-responsive deep eutectic solvents for rapid delignification
Bibliographic record
Abstract
Attaining sustainability and carbon neutrality necessitates a transition towards cleaner biorefinery, while the exploitation of sustainable and eco-friendly pretreatment techniques, as a pivotal stage in lignocellulose biorefinery, represents a challenge. Here, an ultrafast biomass pretreatment strategy enabled by microwave (MW) responsive deep eutectic solvent (DES) is proposed. The solvent properties (Kamlet-Taft parameters) of DES under MW participation are closely correlated with wheat straw fractionation efficiency. The lignin removal exhibits a positive correlation with polarity/polarizability (π*) and hydrogen-bond-donating ability (α), establishing a strong relationship between the tunable DES properties and MW responsiveness. MW reinforces the delignification efficiency of DES with relatively high π* and α, as corroborated by comparative analysis with conventional heating (CH) pretreatment. The reinforcement by MW moderates the pretreatment process and enables ultrafast lignocellulose deconstruction (130 ℃, 150 s, and 96.1% lignin removal), subsequently with 92.4% enzymatic hydrolysis and 8.8 g microbial lipid/100 g wheat straw at a remarkably low severity factor (R0). Life cycle assessment manifests the environmental benefits of MW-assisted DES in mitigating impacts by 63.1%, including global warming potential, resource depletion-fossil fuels, and ecotoxicity, in comparison to CH pretreatment. MW-DES exhibits an economic superiority based on life cycle cost analysis, with pretreatment cost 44.1% lower than CH-DES. The mechanistic insights into MW intensification of DES with specific properties provide a viable protocol for tailoring green solvents with enhanced MW responsiveness for efficient and sustainable biorefineries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".