Demonstration of Copper:pLA-like nanocomposite-based distributed Bragg reflector gas sensor
Bibliographic record
Abstract
Plasma polymerization, commonly considered a type of plasma-enhanced chemical vapour deposition (PECVD), is a popular method for depositing organic thin films.However, it often produces films with limited molecular complexity due to the necessity to use relatively low molar mass precursors that can be vaporized.To address these limitations, plasma-assisted vapour thermal deposition (PAVTD) was developed.In PAVTD, a solid polymer undergoes thermal degradation (evaporation) in a crucible, producing oligomers with higher molar masses (10 2 -10 3 g.mol-1 ) than typical PECVD precursors.These oligomers are then re-polymerized in RF plasma, allowing PAVTD films to exhibit properties characteristic of classical polymer physics and chemistry, a rarity for plasma polymers.This process enables the precise control of properties such as biodegradability and hydrolyzability, as demonstrated in polylactic acid (PLA)-based films.PAVTD effectively bridges the gap between classical and plasma polymers.To enhance stability and deposition rates, continuous-PAVTD has been developed using standard FDM 3D printing filaments, achieving deposition rates up to several nanometers per second.This advancement addresses deposition duration and stability issues, making PAVTD a practical tool for studying plasma polymerization.Furthermore, PAVTD can be combined with other vacuum-based thin film deposition techniques like gas aggregation source of nanoparticles (GAS).This capability was demonstrated by fabricating Cu:PLA-like nanocomposite-based distributed Bragg reflector (DBR), where the reproducibility of the deposition rate matters significantly.This reflector was tested as a gas sensor for ethanol vapours, exhibiting strong reflectance peak shifts.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".