Valorizing Recalcitrant Cellulolytic Enzyme Lignin via Lignin Nanoparticles Fabrication in an Integrated Biorefinery
Bibliographic record
Abstract
Conversion of condensed lignin into value-added products in current lignocellulosic biorefineries has been challenging due to its structure recalcitrance. However, this work showed a technically feasible route to valorize recalcitrant cellulolytic enzyme lignin (CEL: lignin residue after enzymatic hydrolysis) via “high-quality” lignin nanoparticles (LNPs) fabrication. Three representative CELs obtained from hydrolysis of industrial relevant, steam-pretreated, agriculture reside corn stover, hardwood poplar, and softwood lodgepole pine were evaluated for their potential to produce LNPs through the prevalent dialysis method, which gave a LNPs yield of 81.8%, 90.9% and 41.0% with a corresponding average particle size of 218, 131, and 104 nm, respectively. The obtained “high-quality” LNPs were in sphere-like shapes, abundant with functional groups, and highly stable from pH 4 to 10, which showed tremendous promise for the applications in the emerging nanomaterial fields. When the substructures of these three LNPs were further characterized using prevalent 13 C and 2D-HSQC NMR techniques, they showed that their structure recalcitrance followed the order of lodgepole pine LNPs > poplar LNPs > corn stover LNPs. It was also apparent that the biomass lignin condensation occurring during steam pretreatment could be considered as a “hydrophobic modification”, which benefits the self-assembling of LNPs to small particle sizes and regular shapes.
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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) | 0.000 | 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 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".