Confronting Neutrality: Maximizing Success in the Analysis of Transition-Metal Catalysts by MALDI Mass Spectrometry
Bibliographic record
Abstract
Abstract The elucidation of molecular identity is a central challenge in homogeneous transition-metal catalysis. Most exacting, despite advances in electrospray-ionization mass spectrometry (ESI-MS), are neutral complexes, which encompass the vast majority of metal catalysts. Insight into molecular constitution, readily achieved in other areas of the chemical sciences, is commonly thwarted by fragmentation of inorganic and organometallic compounds, particularly for highly reactive species. MALDI-MS, where coupled with charge-transfer ionization, stands out from all other current MS methods in its unique capacity to report on the molecular identity of intact metal complexes irrespective of their initial charge state. Identified in the present work are methods that enable routine, unambiguous identification of such complexes across a wide range of standard MALDI mass spectrometers. The origin of fragmentation during MALDI-MS analysis is explored on 13 different instruments at 9 facilities and across a range of mass analyzers, from TOF, TOF-TOF, and Q-TOF to Orbitrap. Selected as test analytes were the second-generation Hoveyda and Grubbs metathesis catalysts and the Grubbs resting-state methylidene complex, which span a very broad range in terms of ligand lability, and hence susceptibility to fragmentation. Three critical parameters emerge: (1) using the minimum applied laser energy necessary for sample volatilization; (2) minimizing the absorption cross-section of the analyte at the laser wavelength: (a) by appropriate laser choice, where options exist, (b) by using a matrix that absorbs as strongly as possible at the laser wavelength, in significant excess relative to the analyte (e.g., 500-fold), and (c) by prompt analysis, to limit matrix sublimation in the ion source; (3) using a charge-transfer matrix devoid of reactive protic or donor sites. A final, forward-looking section highlights relevant advances in state-of-the-art instrumentation, and instrumental features that would contribute to the optimization of next-generation MALDI mass spectrometers for this emerging application. These include fast-firing, contoured-profile, and, potentially, wavelength-tunable lasers, high-resolution mass analyzers, automatic plate rastering with software to support retroactive compilation of spectra from optimal sampling locations, and soft-vacuum or atmospheric-pressure sources, in conjunction with a standardized anaerobic interface.
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 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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".