Acid−Base Equilibrium Constants for Ferric <i>trans</i>-1,2-Diaminocyclohexanetetraacetic Acid (Fe<sup>3+</sup>CDTA<sup>4-</sup>/Fe<sup>3+</sup>OH<sup>-</sup>CDTA<sup>4-</sup>) in NaCl, Na<sub>2</sub>SO<sub>4</sub>, and LiCl Aqueous Solutions at 298 K
Bibliographic record
Abstract
The dependence of the ferric chelate ( trans -1,2-diaminocyclohexanetetraacetic acid) acid−base stoichiometric equilibrium constant on pH and on low-to-moderate ionic strength [(0 < I ≤ 2) mol kg -1 ] was systematically studied in sodium chloride, sodium sulfate, and lithium chloride aqueous solutions at 298 K. Activity coefficient models characterizing the ionic behavior of electrolytic solutions were applied in conjunction with the measured equilibrium constant ( K C ) in order to obtain the thermodynamic equilibrium constant ( K ) for the Fe 3+ CDTA 4- /Fe 3+ OH - CDTA 4- couple in slightly-to-moderately alkaline conditions (8 ≤ pH ≤ 10.5). On the basis of the Hückel, Bromley, Scatchard, and Pitzer models, the best fit over 150 K C measurements procured an optimal log( K ) value of 4.288 ± 0.022 at (298 ± 1) K. A good fit of the K C measurements was achieved in the following ionic strength regions: Hückel ( I < 0.25 mol kg -1 ), Bromley ( I < 1 mol kg -1 ), Scatchard ( I < 1.5 mol kg -1 ), Pitzer ( I < 2 mol kg -1 ). In those instances, the predictions presented acceptable fitting capability well within the accepted limits (average absolute error on p K C < 0.05).
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".