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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".