Oxidative Ring-Opening of Aromatics: Decomposition of Biphenyl Carboxylic Acids and Zinc Biphenyl Carboxylates
Bibliographic record
Abstract
Ring-opening conversion of multinuclear aromatics can be used to upgrade heavy aromatic oils to lighter products, and it is usually performed reductively with H 2 . Oxidative ring-opening is an alternative strategy that involves three steps: (i) oxidation of multinuclear aromatics to quinonoids, (ii) further oxidation and ring-opening to produce aromatic carboxylic acids, and (iii) decarboxylation of aromatic carboxylic acids. In the last step, decomposition by ketonization is an undesirable side reaction that leads to a ring-closed product. Selectivity control during aromatic carboxylic acid decomposition was investigated using biphenyl-2-carboxylic acid, biphenyl-2,2′-dicarboxylic acid, zinc(II) biphenyl-2-carboxylate, and zinc(II) biphenyl-2,2′-dicarboxylate. The reaction networks of thermal decomposition of the aromatic carboxylic acids were determined. Decomposition of biphenyl-2-carboxylic acid took place mainly by decarboxylation to produce biphenyl, dehydration and ring-closure to produce fluorenone, and the formation of diphenic anhydride as intermediate product leading to fluorenone. Decomposition of biphenyl-2,2′-dicarboxylic acid proceeded through decarboxylation to biphenyl-2-carboxylic acid as intermediate, as well as two seemingly related pathways, leading to the formation of a hydroxy-fluorenone and a cyclic trione. Over the temperature range from 340 °C to 400 °C, thermal decomposition invariably resulted in a higher ketonization than decarboxylation selectivity. Decomposition of the analogous zinc carboxylates demonstrated that ketonization could be suppressed and the most abundant products were biphenyl > fluorenone > fluorene. It was possible to achieve a biphenyl (decarboxylation) to fluorenone (ketonization) selectivity ratio of 17:1 during batch reactor decomposition of zinc(II) biphenyl-2,2′-dicarboxylate at 380 °C. Reaction stoichiometry indicated that water should affect selectivity, which is consistent with observations in the literature, but this aspect was not investigated further.
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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".