Dissociation Constants (pKa) of Eight Amines: Measurements, Computational Chemistry Calculations, and ANN
Bibliographic record
Abstract
This work focused on determining the dissociation constants (pKa) for eight amines, namely, 3-(Diethylamino) propylamine, 1,3-Diaminopentane, 3-Butoxypropylamine, 2-(Methylamino) ethanol, Bis(2-methoxyethyl) Amine, α-Methylbenzylamine, 2-Aminoheptane, and 3-Amino-1-phenylbutane at temperatures ranging from 293.15 K to 323.15 K. The protonated order of two polyamines, 3-(Diethylamino) propylamine and 1, 3-Diaminopentane, were determined using computational chemistry methods. The dissociation constants at the standard temperature of 298.15 K were estimated using group functional models (paper-pencil) and computational methods using software such as COSMO-RS and Gaussian. In addition, the pKas at various temperatures were calculated using computational methods for two different thermodynamic cycle. A simple artificial neural network (ANN) method was also employed to reduce the calculation time as well as improve the accuracy. Instead of using the experimental property data, these could be generated using Aspen Plus or CosmothermX. The simulated model provided a very good fit to the pKa values.
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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) | 0.001 | 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 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".