Fixed-charge phosphine ligands to explore gas-phase coinage metal-mediated decarboxylation reactions
Bibliographic record
Abstract
A combination of multistage mass spectrometry experiments and density functional theory (DFT) calculations were used to examine the decarboxylation reactions of a series of metal carboxylate complexes bearing a fixed-charge phosphine ligand, [(O3SC6H4)(C6H5)2PM(I)O2CR](-) (M = Cu, Ag, Au; R = Me, Et, benzyl, Ph). Collision-induced dissociation (CID) of these complexes using an LTQ linear ion mass spectrometer results in three main classes of reactions being observed: (1) decarboxylation; (2) loss of the phosphine ligand; (3) loss of carboxylic acid. The gas-phase unimolecular chemistry of the resultant decarboxylated organometallic ions, [(O3SC6H4)(C6H5)2PM(I)R](-), were also explored using CID experiments, and fragment primarily via loss of the phosphine ligand. Energy-resolved CID experiments on [(O3SC6H4)(C6H5)2PM(I)O2CR](-) (M = Cu, Ag, Au; R = Me, Et, benzyl, Ph) using a Q-TOF mass spectrometer were performed to gain a more detailed understanding of the factors influencing coinage metal-catalyzed decarboxylation and DFT calculations on the major fragmentation pathways aided in interpretation of the experimental results. Key findings are that: (1) the energy required for loss of the phosphine ligand follows the order Ag < Cu < Au; (2) the ease of decarboxylation of the coordinated RCO2 groups follows the order of R: Ph < PhCH2 < Me < Et; (3) in general, copper is best at facilitating decarboxylation, followed by gold then silver. The one exception to this trend is when R = Ph and M = Au which has the highest overall propensity for decarboxylation. The influence of the phosphine ligand on decarboxylation is also considered in comparison with previous studies on metal carboxylates that do not contain a phosphine ligand.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.141 | 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; both teacher heads agree on what is shown here.
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".