Monitoring Polymeric Fouling in a Continuous Reactor by Electrochemical Impedance Spectroscopy

Vanessa Nesslinger,Soeren Rust, Jan Atlanov,Werner Pauer,Guido Grundmeier

CHEMIE INGENIEUR TECHNIK（2024）

Cited 1|Views11

Abstract

Monitoring early stages of polymeric deposit formation and its prevention were studied by in-situ electrochemical impedance spectroscopy (EIS) in a continuously operating reactor employed for polymer production. An EIS flow cell was designed and employed during the emulsion polymerization of vinyl acetate. The electrochemical analysis of the complex impedance at the solution/reactor interface allows the time-resolved detection of film formation processes. In comparison to oxide-covered stainless steel, an anti-adhesive sol-gel coated alloy showed a significant inhibition of poly(vinyl acetate) fouling. The EIS-based approach proved to be a valuable tool for monitoring both thin barrier film performance and fouling processes under harsh process conditions. In-situ electrochemical impedance spectroscopy was applied for detection of fouling in the initial stages of film formation during emulsion polymerization of vinyl acetate in a technical microreactor. Fouling formation was followed time dependent on stainless steel and on a barrier coating which showed anti-adhesive properties against polymeric deposits. image

Translated text

Key words

Anti-adhesive films,Electrochemical impedance spectroscopy,Emulsion polymerization,Polymer fouling,Poly(vinyl acetate)

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper