Abundance and Reactivity of Dibenzodioxocins in Softwood Lignin
Bibliographic record
Abstract
To define the abundance and comprehend the reactivity of dibenzodioxocins in lignin, model compound studies, specific degradation experiments on milled wood lignin, and molecular modeling calculations have been performed. Quantitative (31)P NMR measurements of the increase of biphenolic hydroxyl groups formed after a series of alkaline degradations in the presence of hydrosulfide anions (kraft conditions) showed the presence of 3.7 dibenzodioxocin rings/100 C9 units in milled wood lignin. The DFRC degradation protocol (Derivatization Followed by Reductive Cleavage) was chosen as an independent means to estimate their abundance. Initial experiments with a dibenzodioxocin model compound, trans-6,7-dihydro-7-(4-hydroxy-3-methoxyphenyl)-4,9-dimethoxy-2,11-dipropyldibenzo[e,g][1,4]dioxocin-6-ylmethanol, showed that it is not cleaved under DFRC conditions, but rather it isomerizes into a cyclic oxepine structure. Steric effects precluded this isomerization from occurring when DFRC was applied to milled wood lignin. Instead, monoacetylated biphenolic moieties were released and quantified by (31)P NMR, at 4.3 dibenzodioxocin rings/100 C9 units. The dibenzodioxocin content in residual lignins isolated from kraft pulps delignified to various degrees showed that during pulp delignification, the initial rate of dibenzodioxocin removal was considerably greater than the cleavage rate of arylglycerol-beta-aryl ether bonds. The activation energy for the degradation of dibenzodioxocins under kraft conditions in milled wood lignin was 96 +/- 9 kJ/mol, similar to that of arylglycerol-beta-aryl ether bond cleavage.
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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 | 0.000 | 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 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".