Deposition of Hydrophobic Functional Groups on Wood Surfaces Using Atmospheric‐Pressure Dielectric Barrier Discharge in Helium‐Hexamethyldisiloxane Gas Mixtures
Bibliographic record
Abstract
Abstract This work examines the functionalization of sugar maple (Acer saccharum) and black spruce (Picea mariana) wood surfaces using an atmospheric‐pressure dielectric barrier discharge in He and He/HMDSO (hexamethyldisiloxane) gas mixtures. Wood samples were placed on one of the electrodes and the plasma was sustained by applying a 3.5 kV peak‐to‐peak voltage at 12 kHz. Analysis of the discharge stability through current–voltage (I–V) characteristics revealed a filamentary behaviour, in sharp contrast with the homogeneous He discharge obtained with a glass sample. Optical emission spectroscopy performed near the wood vicinity revealed strong N2 and ${\rm N}_{{\rm 2}}^{{\rm + }} $ emissions, suggesting that wood outgassing plays an important role in the evolution of the discharge regime. Analysis of the surface wettability through water contact angle (WCA) measurements indicated that freshly sanded wood samples treated in He/HMDSO plasmas became more hydrophobic with WCAs in the 120°–140° range depending on treatment time and wood species. Attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy measurements on samples exposed to He/HMDSO plasmas revealed the deposition of hydrophobic Si(CH3)3‐O‐Si(CH3)2, Si(CH3)3 and Si(CH3)2 functional groups as well as an increase of the CH‐to‐OH band intensity ratio. For relatively thick coatings, the WCA following natural aging under uncontrolled conditions remained constant at 132° ± 3° which highlights the stability of the plasma‐deposited thin films, a very promising result for structural and decorative outdoor applications. magnified image
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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".