Alkaline oxidative degradation of diphenylmethane structures Activation energy and computational analysis of the reaction mechanism
Bibliographic record
Abstract
A diphenylmethane model compound (2,2¢-methylenebis[6-methoxy-4-methylphenol]) and residual kraft lignin were treated with alkaline hydrogen peroxide.Kinetic data for the disappearance of the model and the diphenylmethane structures in the residual lignin was collected.The activation energies for the degradation were found to be similar (54 ± 11 kJ mol -1 for the model and 58 ± 5 kJ mol -1 for the residual lignin).A comparison of the activation energies with the data of a previous study on a biphenyl model compound (3,3¢-dimethoxy-5,5¢-dimethyl-[1,1¢biphenyl]-2,2¢-diol) showed a substantially higher activation energy for the degradation of the latter.Pathways for the degradation of 2,2¢-methylenebis[6-methoxy-4-methylphenol] were proposed and the intermediates subjected to computational analysis using a semiempirical method (PM3).The results suggest that initially a common pathway exists, resulting in 2-[2-hydroxy-3-methoxy-5-methyl-phenylmethyl]-4-methyl-2,4-hexadienedioic acid.Then the pathway branches into three, resulting in three major degradation products.The main driving force of the reactions is the formation of radical sites after reaction with hydroxyl radicals and subsequent radical coupling with perhydroxyl radicals to form peroxides.All the reactions on the pathways are exothermic except for the transformations of peroxides into dioxetanes.The dioxetanes cleave exothermically resulting in ring cleavage and fragmentation.The computed data permitted for the rationalization as to why the diphenylmethane structures appear to be more labile than biphenyl structures under alkaline oxidative conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".