Metal Hydride Vibrations: The Trans Effect of the Hydride
Bibliographic record
Abstract
trans-Dihydride complexes are important in many homogeneous catalytic processes. Here vibrational spectroscopy and density functional theory (DFT) methods are used for the first time to reveal that 4d and 5d metals transmit more effectively than the 3d metals influence of the ligand trans to the hydride and also couple the motions of the trans-hydrides more effectively. This property of the metal is linked to higher hydride reactivity. The IR and Raman spectra of trans-FeH2(dppm)2, trans-RuH2(PPh(OEt)2)4, and mer-IrH3(PiPr2CH2pyCH2PiPr2) provide M–H force constants and H–M–H interaction force constants that increase as FeII < RuII < IrIII. DFT methods are used to determine, for the first time, the effect of the metal ion (MnI, ReI, FeII, RuII, OsII, CoIII, RhIII, IrIII, PtIV) and ligands on the gap in wavenumbers between the symmetric νsymH–M–H and antisymmetric νasymH–M–H vibrational modes of hydrides that are mutually trans in d6 octahedral complexes. The magnitude of this gap reflects the degree of coupling of, or interaction between, these modes, and this is shown to be a distinctive property of the metal ion. The more polarizable 4d and 5d metal ions are found to have an average gap of 246 cm–1, while the 3d metals have only 90 cm–1. This has been verified experimentally for 3d, 4d, and 5d transition-metal trans-dihydrides, where both the IR and Raman spectra have been measured: trans-RuH2(PPh(OEt)2)4 (from the literature) and trans-FeH2(PPh2CH2PPh2)2 and mer-IrH3(PiPr2CH2pyCH2PiPr2) (this work). Because the 4d and 5d metal ions tend to be better catalysts for the hydrogenation of substrates with polar bonds, this gap may be a fundamental determinant of the kinetic hydricity of the catalyst. Finding the magnitude of this gap and a new estimate of the large hydride trans-effect (Δνt −235 cm–1) allows us to improve the simple equation reported previously, which allows a better estimate of νM–H.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".