<sup>DMP</sup> DAB–Pd–MAH: A Versatile Pd(0) Source for Precatalyst Formation, Reaction Screening, and Preparative-Scale Synthesis
Bibliographic record
Abstract
We report an easily prepared and bench-stable mononuclear Pd(0) source stabilized by a chelating N, N ′-diaryldiazabutadiene ligand and maleic anhydride: DMP DAB–Pd–MAH. Phosphine ligands of all types, including bidentate phosphines and large-cone-angle biarylphosphines, rapidly and completely displace the diazabutadiene ligand at room temperature to give air-stable Pd(0) phosphine complexes. DMP DAB–Pd–MAH itself is readily soluble and stable in several organic solvents, making it an ideal Pd source for in situ catalyst preparation during reaction screening as well as solution-dispensing to plate-based reaction arrays for high-throughput experimentation. Evaluation of DMP DAB–Pd–MAH alongside other common Pd(0) and Pd(II) sources in microscale reaction screens reveals that DMP DAB–Pd–MAH is superior at identifying hits across six different C–N, C–C, and C–O coupling reactions. DMP DAB–Pd–MAH, and the phosphine precatalysts derived therefrom, are also effective in preparative-scale cross-couplings at low Pd loadings.
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 | 0.000 | 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) | 0.002 | 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".