Controlling the Thermal Stability and Volatility of Organogold(I) Compounds for Vapor Deposition with Complementary Ligand Design
Bibliographic record
Abstract
Atomic layer deposition (ALD) of gold is being studied by multiple research groups, but to date no process using non‐energetic co‐reactants has been demonstrated. In order to access milder co‐reactants, precursors with higher thermal stability are required. We set out to uncover how structure and bonding affect the stability and volatility of a family of twelve organogold(I) compounds using a combination of techniques: X‐ray diffraction (XRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and density functional theory (DFT). Small, unsubstituted phosphonium ylide ligands bind more strongly to Au(I) than their silyl‐substituted analogues, but the utility of both these ligands suffers due to their poor volatility and substantial thermal decomposition. Pentafluorophenyl (C 6 F 5 ) is introduced as a new, very electronegative ligand for gold vapor deposition precursors, and it was found that the disadvantage to volatility due to π‐stacking and other intermolecular interactions in the solid state was overshadowed by dramatic improvements to kinetic and thermodynamic stability. We introduce a new figure of merit to compare and rank the suitability of these and other complexes as precursors for vapor deposition. Finally, DFT calculations on four compounds that have high figures of merit show a linear correlation between the gold‐coordinative ligand bond dissociation energies and the observed decomposition temperatures, highlighting and justifying this design strategy.
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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 | 0.000 | 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".