A Study of Transition‐Metal Organometallic Complexes Combining <sup>35</sup>Cl Solid‐State NMR Spectroscopy and <sup>35</sup>Cl NQR Spectroscopy and First‐Principles DFT Calculations
Bibliographic record
Abstract
A series of transition-metal organometallic complexes with commonly occurring metal-chlorine bonding motifs were characterized using (35)Cl solid-state NMR (SSNMR) spectroscopy, (35)Cl nuclear quadrupole resonance (NQR) spectroscopy, and first-principles density functional theory (DFT) calculations of NMR interaction tensors. Static (35)Cl ultra-wideline NMR spectra were acquired in a piecewise manner at standard (9.4 T) and high (21.1 T) magnetic field strengths using the WURST-QCPMG pulse sequence. The (35)Cl electric field gradient (EFG) and chemical shielding (CS) tensor parameters were readily extracted from analytical simulations of the spectra; in particular, the quadrupolar parameters are shown to be very sensitive to structural differences, and can easily differentiate between chlorine atoms in bridging and terminal bonding environments. (35)Cl NQR spectra were acquired for many of the complexes, which aided in resolving structurally similar, yet crystallographically distinct and magnetically inequivalent chlorine sites, and with the interpretation and assignment of (35)Cl SSNMR spectra. (35)Cl EFG tensors obtained from first-principles DFT calculations are consistently in good agreement with experiment, highlighting the importance of using a combined approach of theoretical and experimental methods for structural characterization. Finally, a preliminary example of a (35)Cl SSNMR spectrum of a transition-metal species (TiCl4) diluted and supported on non-porous silica is presented. The combination of (35)Cl SSNMR and (35)Cl NQR spectroscopy and DFT calculations is shown to be a promising and simple methodology for the characterization of all manner of chlorine-containing transition-metal complexes, in pure, impure bulk and supported forms.
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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 | 0.000 |
| 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) | 0.001 | 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".