Why Is THCA Decarboxylation Faster than CBDA? an in Silico Perspective
Bibliographic record
Abstract
Tetrahydrocannabinol acid (THCA) and cannabidiol acid (CBDA), the two crucial organic components in cannabis and hemp, decarboxylate at different rates to their more active neutral forms. Theoretical calculations are used herein to analyze how the remote annulated ring or pendant substituent influences the rate determining steps of the decarboxylation processes. The uncatalyzed keto-enol tautomerization that precedes decarboxylation is found to be extremely slow in both cases albeit with a ten-fold preference for CBDA. A single molecule of methanol dramatically enhances the reaction rates by allowing for tautomerization through a more favorable six-membered ring transition state. Methanol-catalyzed tautomerization is found to be faster in THCA than in CBDA. This difference results from both the larger dipole moment of the THCA scaffold as well as its greater rigidity relative to CBDA. The greater dipole moment leads to a somewhat better binding of methanol. The lower entropic penalty in THCA towards tautomerization leads to faster decarboxylation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".