Thermal ranges and figures of merit for gold-containing precursors for atomic layer deposition
Bibliographic record
Abstract
This is a survey of known gold-containing chemical vapor deposition (CVD) and atomic layer deposition (ALD) precursors with a focus on collecting their volatilization and decomposition data. These data were applied to a figure of merit (σ) developed to easily assess the thermal characteristics (“volatilization temperature” and the onset of decomposition]) important in the initial assessment of molecules as potential ALD precursors. Of the 46 compounds that were identified, 23 had sufficient thermal data reported in the literature to determine a σ value. The two known gold ALD precursors (15, σ = 104 and 22, σ = 44) were both shown to be in a cluster of excellent precursors. They were each the best in their families of precursors, measured by their σ and their onset of volatility. It was also found that many potentially excellent gold ALD precursors had gold in the +3 oxidation state. Some gold(I) compounds were identified as having good figures of merit (28, σ = 78, 29, σ = 32, 36, σ = 31), but 28 and 29 were found to undergo a reductive elimination reaction at growing gold surfaces, which is a common CVD deposition route for gold(I) compounds. β-diketonates and β-ketoiminates of gold were the most encouraging family of compounds, with σ values of greater than 50. This family contained the precursor with the highest figure of merit (9, σ = 212). These compounds are difficult to make in high yield, but an alternate synthetic route that was identified during this survey might serve to make this class of gold compound competitive as a precursor.
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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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".