Bachelor's degree programmes

Paint raw materials and foundation courses - sub-module in paint chemistry I - 4430

Coating raw materials internship I - sub-module in coating chemistry I - certificate 4431

Coating raw materials internship II - sub-module in coating technology I - test 4441

Master's degree programmes

Paint Raw Materials II - sub-module in Paint Chemistry III - 4814

Paint formulation - sub-module in Paint Chemistry III - 4814

Manufacturing process - sub-module in paint technology II - 4815

Optimization project / DoE - from advanced practical course in coating - 4900

Coatings I Internship - from Advanced Internship - 4900

Moodle courses for the courses

FB 01 Chemistry / 01 Bachelor / Bachelor of Engineering / Fundamentals of paint raw materials and formulations - Moodle-Link ->

FB 01 Chemistry / 01 Master / Master of Engineering / Coating Engineering / Paint Chemistry III - Raw Materials and Formulations - Moodle-Link ->

FB 01 Chemistry / 01 Master / Master of Engineering / Coating Engineering / Paint Technology II - Manufacturing Processes - Moodle-Link ->

FB 01 Chemistry / 01 Master / Master of Engineering / Coating Engineering / Optimization Project Advanced Practical Course Coating Technology - Moodle-Link ->

Research

Our research covers topics related to the development of new coating materials and raw materials. An important aspect is digitalization and the handling of large amounts of data for the optimization of processes and products by means of digital models of the systems. An important link is the interdisciplinary research with the other faculties of the Hochschule Niederrhein together at the HIT Institute to develop solutions of automation of paint development and data management.

Current research topics:

• Data description of chemical compounds in paint formulations for predictive machine learning models.
• Models of paint formulations based on polarity scales in raw material synthesis
• Data analysis and statistical design of experiments with self-optimizing algorithms

In-house work

Degree and project work on research topics in digital paint formulation can be done at any time. There is the possibility to be supervised in topics related to the elucidation of structure-property relationships using modern measurement techniques, automation of paint chemistry processes and data evaluation using programming in Python.

Book chapter

Schmitz, Cremanns, Bissadi, Application of machine learning algorithms for use in material chemistry in Computational and Data-Driven Chemistry Using Artificiel Intelligence, 2022, 161-192, https://doi.org/10.1016/B978-0-12-822249-2.00001-3

Journals

Schmitz, Schucht, Verjans, Krupka, Data-analysis method for material optimization by forecasting long-term chemical stability in Journal of Chemometrics, 2022, e3383, https://doi.org/10.1002/cem.3383

Zhang, Schmitz, Fimmers, Quix, Hoseini, Deep learning-based automated characterization of crosscut tests for coatings via image segmentation in Journal of Coatings Technology and Research, 2022, 19, 671-683, https://doi.org/10.1007/s11998-021-00557-y

Strehmel, Schmitz, Kütahya, Pang, Drewitz, Mustroph, Photophysics and photochemistry of NIR absorbers derived from cyanines: key to new technologies based on chemistry 4.0 in Beilstein J. Org. Chem., 2020, 16, 415-444, https://doi.org/10.3762/bjoc.16.40

Strehmel, Schmitz, Cremanns, Göttert, Photochemistry with Cyanines in the Near Infrared: A Step to Chemistry 4.0 Technologies in Chemistry: A European Journal, 2019, 25, 12855-12864, https://doi.org/10.1002/chem.201901746