Experimental determination of dissociation constants (p<i>K<sub>a</sub></i>) for <scp>N</scp>‐(2‐aminoethyl)‐1,3‐propanediamine, 2‐methylpentamethylene diamine, <scp>N,N</scp>‐dimethyldipropylenetriamine, 3,3′‐<scp>diamino‐N</scp>‐methyldipropyl‐amine, <scp>Bis</scp>[2‐(<scp>N,N</scp>‐dimethylamino)ethyl]ether, 2‐[2‐(dimethyl‐amino) ethoxy] ethanol, 2‐(dibutylamino) ethanol, and <scp>N</scp>‐propylethanol‐amine and modeling with artificial neural network
Bibliographic record
Abstract
Abstract The dissociation constants (p K a ) of the eight amines, namely, N‐(2‐aminoethyl)‐1,3‐propanediamine, 2‐methylpentamethylene diamine, N,N‐dimethyldipropylene‐triamine, 3,3′‐diamino‐N‐methyl‐dipropylamine, Bis[2‐(N,N‐dimethylamino) ethyl]ether, 2‐[2‐(dimethyl‐amino)ethoxy] ethanol, 2‐(dibutylamino) ethanol, and N‐propylethanolamine were measured between 298.15 and 313.15 K with 5 K increment. Based on the experimental values and using the van't Hoff equation, thermodynamic properties such as the standard state changes of enthalpy, entropy, and Gibbs free energy were calculated. Using computational chemistry calculations, the amine that is protonated first was predicted. Furthermore, computer‐free group contribution methods such as the original Perrin–Dempsey–Serjeant (PDS), the modified PDS, and the Qian–Sun–Sun–Gao (QSSG) model were used to estimate the dissociation constants of the studied amines at 298.15 K. The QSSG provided the most accurate results. Finally, this work utilized an artificial neural network for estimating the p K a values, which were in excellent agreement with the experimental data.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| 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; both teacher heads agree on what is shown here.
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".