Layer-by-layer structured nanocomposite deposits from plasma-synthesized organosilicon nanoparticles and organosilicon nanoparticles decorated with Ag nanoparticles by taking advantage of cyclic nanoparticle formation in Ar/HMDSO reactive plasmas
Bibliographic record
Abstract
Rational engineering of thin nanocomposite layers, deposited in reactive plasmas, requires knowledge on the plasma behavior in order to produce multifunctional deposits with tailored properties (structural, optical, electrical, etc.) This work presents an experimental study of nanoparticles synthesized in the plasma gas-phase and their subsequent use as building-blocks to form layer-by-layer nanostructures. The experiment is performed in a plasma process that successfully combines plasma polymerization of an organosilicon molecular precursor (hexamethyldisiloxane, HMDSO) and sputtering of a metallic (silver) target. Pulsed injection of the precursor is found to promote cyclic nanoparticle formation in Ar/HMDSO reactive plasmas. The plasma electron temperature is found to vary in the range 1.6—2.2 eV as derived from time-resolved optical emission spectroscopy of the plasma energetic conditions. This diagnostic method is also shown to provide a reliable tool for online monitoring of the nanoparticle synthesis process. Two types of layer-by-layer structured nanocomposites can be obtained depending on the type of nanoparticles synthesized: (i) organosilicon nanoparticles of size less than 100 nm in all studied plasma conditions for a large quantity of injected HMDSO and (ii) raspberry-like nanoparticles of size less than 150 nm when the quantity of injected HMDSO is reduced. The organosilicon nanoparticle growth follows a polydimethylsiloxane (PDMS)-like oligomerization scheme in which the R2-Si(-O)2 silicon bond tends towards the formation of polymeric structure in a R3-Si(-O)1 silicon chemical environment, containing Si-(CH2)-Si type bridges that are involved in cross-linking. The elemental composition of the raspberry-like nanoparticles is similar to that of the organosilicon nanoparticles, supplemented by the Ag component. The decorating silver nanoparticles are ∼15 nm of size, round in shape and polycrystalline. There is no evidence for silver oxides in the nanostructures. The Si-O-Ag bridges, revealed by infrared spectroscopy, suggest the presence of junction sites between the metallic and the organosilicon parts of the raspberry-like nanoparticles. The silver nanoparticles are found to decorate the organosilicon nanoparticles to form the raspberry-like nanoparticles in the plasma gas-phase, before being deposited. This reveals a very interesting phenomenon of simultaneous growth of the silver- and organosilicon-parts in the plasma without mixing during the nucleation phase.
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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".