Partial Least-Squares Regression as a Tool To Predict Fluoropolymer Surface Modification by Dielectric Barrier Discharge in a Corona Process Configuration in a Nitrogen–Organic Gaseous Precursor Environment,Industrial & Engineering Chemistry Research

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Partial Least-Squares Regression as a Tool To Predict Fluoropolymer Surface Modification by Dielectric Barrier Discharge in a Corona Process Configuration in a Nitrogen–Organic Gaseous Precursor Environment
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-05-24 , DOI: 10.1021/acs.iecr.8b00228
Julien Vallade _{1,

2} , Stéphane Turgeon _{1,

2} , Gaétan Laroche _{1,

2}

Affiliation

A dielectric barrier discharge in a corona process configuration is used to treat the surface of fluoropolymers in a nitrogen–organic precursor environment. The surface chemistry, thickness, and water contact angle of the deposited coatings are measured and used to build up an output matrix to be correlated with an input matrix built using electrical parameters of the discharge, the gas mixture chemical composition, and spectroscopic parameters measured in both the infrared and ultraviolet–visible emission spectral regions. A partial least-squares regression (PLSR) model enables determining the most important plasma parameters to drive the coating physicochemical characteristics. From the PLSR model, it is determined that the plasma electrical parameters drive the surface modification process, at the expense of other plasma characteristics such as gas flow, gaseous precursor concentration, nitrogen vibrational temperature, and the level of gaseous precursor conversion within the plasma.

中文翻译：

最小二乘回归作为工具预测氮有机气体前体环境中电晕工艺配置中介电势垒放电引起的含氟聚合物表面改性

电晕工艺配置中的介电势垒放电用于在氮有机前体环境中处理含氟聚合物的表面。测量沉积涂层的表面化学性质，厚度和水接触角，并用于建立输出矩阵，该输出矩阵与使用放电的电参数，气体混合物的化学成分和光谱参数测量得到的输入矩阵相关联。红外和紫外可见发射光谱区域。偏最小二乘回归（PLSR）模型可确定最重要的血浆参数，以驱动涂层的物理化学特性。根据PLSR模型，可以确定等离子体电参数会驱动表面改性过程，

更新日期：2018-05-25

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