Estimating the Acidity of Transition Metal Hydride and Dihydrogen Complexes by Adding Ligand Acidity Constants
Bibliographic record
Abstract
A simple equation (pKa(THF) = ∑AL + Ccharge + Cnd + Cd6) can be used to obtain an estimate of the pKa of diamagnetic transition metal hydride and dihydrogen complexes in tetrahydrofuran, and, by use of conversion equations, in other solvents. It involves adding acidity constants AL for each of the ligands in the 5-, 6-, 7-, or 8-coordinate conjugate base complex of the hydride or dihydrogen complex along with a correction for the charge (Ccharge = -15, 0 or 30 for x = +1, 0 or -1 charge, respectively) and the periodic row of the transition metal (Cnd = 0 for 3d or 4d metal, 2 for 5d metal) as well as a correction for d(6) octahedral acids (Cd6 = 6 for d(6) metal ion in the acid, 0 for others) that are not dihydrogen complexes. Constants AL are provided for 13 commonly occurring ligand types; of these, nine neutral ligands are correlated with Lever's electrochemical ligand parameters EL. This method gives good estimates of the over 170 literature pKa values that range from less than zero to 50 with a standard deviation of 3 pKa units for complexes of the metals chromium to nickel, molybdenum, ruthenium to palladium, and tungsten to platinum in the periodic table. This approach allows a quick assessment of the acidity of hydride complexes found in nature (e.g., hydrogenases) and in industry (e.g., catalysis and hydrogen energy applications). The pKa values calculated for acids that have bulky or large bite angle chelating ligands deviate the most from this correlation. The method also provides an estimate of the base strength of the deprotonated form of the complex.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".