Chemical Vapor Deposition of Cerium Oxide Using the Precursors [Ce(hfac)<sub>3</sub>(glyme)]
Bibliographic record
Abstract
Precursors of formula [Ce(hfac) 3 {MeO(CH 2 CH 2 O) n Me}], 1 ( n = 1), 2 ( n = 2), and 3 ( n = 3), and [{Ce(hfac) 3 } 2 {μ-MeO(CH 2 CH 2 O) 4 Me}], 4 (hfac = CF 3 COCHCOCF 3 ), have been prepared and used as precursors for chemical vapor deposition (CVD) of films of cerium oxides on the substrates Si, Pt, and TiN. Thermal CVD at 450 °C with oxygen as carrier gas gave mixed Ce(III)/Ce(IV) oxides, and the main crystalline component was Ce 4 O 7, but with fluoride impurity. The fluoride impurity was not observed if CVD was carried out using moist oxygen as carrier gas or if the as-deposited films were annealed in oxygen. Codeposition with [Y(hfac) 3 {MeO(CH 2 CH 2 O) 2 Me}] gave films of the mixed Ce(IV)Y(III) oxide Ce 2 Y 2 O 7 . The depositions of cerium oxides could be enhanced by use of a palladium precursor catalyst [Pd(2-methylallyl)(acetylacetonate)] and could then be carried out at 250 °C, giving films of CeO 2 . Under carefully controlled conditions, films of ceria-supported palladium could be prepared by this method. The films were characterized by using X-ray photoelectron spectroscopy, scanning electron microscopy, and X-ray diffraction techniques.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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".