Boron Lewis Acid Extraction of Wood Generates High Quality Lignin
Bibliographic record
Abstract
The separation of lignocellulose into lignin, cellulose, and hemicellulose without significantly altering the chemical structures of these component biopolymers remains a modern chemical challenge. Lignin, in particular, has potential as a highly valuable feedstock material but remains underutilized due to the difficulty of generating lignin with low modification and condensation. This work investigates the lignin-rich solids ("boron lignin") generated from a previously reported boron Lewis acid-mediated lignocellulose separation and concludes that (1) boron Lewis acid extraction removes 80-85% of carbohydrates from the original lignocellulose sample, and (2) the resulting lignin possesses a low condensation level and high similarity to native lignin structure. Residual carbohydrate assessment, depolymerization efficiency analyses, heteronuclear single quantum coherence (HSQC) and solid-state nuclear magnetic resonance (NMR) analyses are discussed, including benchmarking results with alternate lignin sources known to possess low and high condensation levels. Further, two different wood sources (white pine, a softwood, and beechwood, a hardwood) were employed to generate lignin samples. Depolymerization of a white pine-derived boron-lignin produced 47% (±9.5) of extractable monomers, which compares well to a state-of-the-art method to generate low condensed lignin (56 ± 7.8%). An unexpected instability of the oil sample was observed following hydrogenolysis of boron lignin generated from beechwood. Dramatic color changes coupled with precipitation and lowered monomer yields were observed when samples were aged (11% decrease) or concentrated (30% decrease). Based on NMR spectroscopic analyses, this instability is postulated to arise due to boron-mediated demethylation of methoxy sites on the lignin scaffold.
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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".