Analysis of dissolution kinetics of narrow polydispersity poly(4-hydroxystyrene) in alkaline aqueous solution using machine learning

2021 ◽  
Author(s):  
Naoki Tanaka ◽  
Kyoko Watanabe ◽  
Kyoko Matsuoka ◽  
Kazuki Azumagawa ◽  
Takahiro KOZAWA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aqueous Solution ◽  
Dissolution Kinetics ◽  
Alkaline Aqueous Solution ◽  
Kinetics Of

Effects of film thickness and alkaline concentration on dissolution kinetics of poly(4-hydroxystyrene) in alkaline aqueous solution

2022 ◽  
Author(s):  
Naoki Tanaka ◽  
Kyoko Matsuoka ◽  
Takahiro KOZAWA ◽  
Takuya Ikeda ◽  
Yoshitaka Komuro ◽  
...  
Keyword(s):  
Aqueous Solution ◽  
Film Thickness ◽  
Thick Films ◽  
Dissolution Kinetics ◽  
Dissolution Behavior ◽  
Chemically Amplified ◽  
Fundamental Understanding ◽  
Chemically Amplified Resists ◽  
Kinetics Of ◽  
Simple Combination

Abstract The dissolution behavior of a simple combination of poly(4-hydroxystyrene) (PHS) films and tetramethylammonium hydroxide (TMAH) aqueous solution was analyzed to gain a fundamental understanding of the effects of film thickness and alkaline concentration on the dissolution kinetics of chemically amplified resists (CARs). Films of four different thicknesses, from thick (approximately 900 nm) to thin (approximately 50 nm), were developed in 22 different developers of different concentrations. The dissolution behavior of each combination was observed using a quartz crystal microbalance (QCM). Differences in dissolution kinetics due to film thickness were observed even between relatively thick films such as 900- and 500-nm thick films in dilute developers. These differences were considered to be caused by the diffusion of the solution into the films. Thin films also showed characteristic behavior with dilution. This behavior was due to the interaction between the substrate and the films, unlike in the case of thick films.

scholarly journals Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Han Liu ◽  
Tony Zhang ◽  
N. M. Anoop Krishnan ◽  
Morten M. Smedskjaer ◽  
Joseph V. Ryan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dissolution Kinetics ◽  
Silicate Glasses ◽  
Kinetics Of

scholarly journals THE KINETICS OF PENETRATION

1932 ◽  
Vol 16 (1) ◽  
pp. 157-163 ◽  
Author(s):  
W. J. V. Osterhout
Keyword(s):  
Aqueous Solution ◽  
Steady State ◽  
Living Cell ◽  
External Solution ◽  
Living Cells ◽  
Similar Process ◽  
Alkaline Aqueous Solution ◽  
Solution Bathing ◽  
Aqueous Layer ◽  
Kinetics Of

In a model consisting of a non-aqueous layer (representing the protoplasm) placed between an inner, more acid, aqueous layer (representing the sap) and an outer, more alkaline, aqueous solution (representing the external solution bathing a living cell) the penetration of potassium creates an outwardly directed potential against which potassium continues to diffuse inward, thereby increasing the outward potential. This continues until the steady state is reached. The potassium sets up less potential in entering than in escaping and the net result is an outwardly directed potential. A similar process appears to take place in certain living cells.

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