Quantitative Surface Coverage Calculations via Solid-State NMR for Thin Film Depositions: A Case Study for Silica and a Gallium Amidinate
Bibliographic record
Abstract
For the interrogation of precursor nucleation for vapor deposition processes like atomic layer deposition (ALD) and chemical vapor deposition (CVD), a modified method for quantitative analysis of surface coverage was undertaken via NMR. The initial chemisorption of a new gallium(III) alkyl amidinate compound was investigated on high-surface area silica. N, N ′-Diisopropylacetamidinatediethylgallium(III) ( 2 ) was found to have excellent volatility with no decomposition during a ramped thermogravimetric analysis experiment. Stepped-isotherm experiments showed a 1 Torr vapor pressure at 64 °C. Compound 2 was exposed to a pretreated high-surface area silica substrate at 100, 200°, and 300 °C and was found to exhibit stable, persistent chemisorbed surface species at all three temperatures. Substrates were analyzed by 29 Si and 13 C solid-state nuclear magnetic resonance spectroscopy (SS-NMR) and 1 H high-resolution NMR. At 100 and 200 °C the reactivity of compound 2 to geminal and lone hydroxyl surface sites varied slightly eliminating either one or both ethyl groups to produce an alkylated (or nonalkylated) gallium acetamidinate on the silica surface and producing fractional coverages of 0.087–0.088. At 300 °C there was a larger degree of reactivity producing a minor amount of the same surface species as at 100 and 200 °C but also producing additional chemisorbed products likely arising from the decomposition of the ligand framework but ultimately giving a fractional coverage of 0.232 on hydroxyl-terminated silica.
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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".