Vacuum Polymerization of Active Devices
Bibliographic record
Abstract
environmental fluctuations can cause crazing failures of these brittle materials and delamination [1][2][3][4][5].Plasma treatment of the polymer surface before deposition forms a thick interphase layer consisting of an inorganic coating and the polymer.This gives a more uniform stress distribution and improves adhesion and mechanical properties [6].To reduce mechanical failure, additional elastic coatings are used to reduce the mismatch.One possible approach is to use organic-inorganic (O-I) materials.For example, the use of O-I SiOCH layers deposited by plasma-enhanced chemical vapor deposition (PECVD) obtained low-k dielectrics with improved mechanical properties for new microelectronic devices [7,8].Higher durability and improved color stability of O-I coatings were observed in prosthetic dentistry applications [9].In AR coatings, Schulz et al. proposed the use of an O-I approach consisting of a low index material obtained by plasma etching of a polymer layer sandwiched between two SiO 2 layers [10].Ion-beam-assisted chemical vapor deposition (IBACVD) was introduced into the laboratory process where O-I optical coatings are prepared by using ion bombardment through a precursor atmosphere toward a substrate.The O-I coatings exhibit enhanced elastic recovery, higher strain to failure, and lower water uptake compared to standard mineral SiO 2 layers [11].When incorporated in AR coating stacks on plastic optics, their presence results in enhanced thermomechanical properties and higher color stability.Improved elastoplastic properties were also shown for highindex O-I materials such as TiOSiCH [12].The coating's hydrophobic characteristics improve the color stability [13] by preventing water from entering the pores, making O-I materials very attractive for use in ophthalmic applications.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".