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Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches

Pilz, Lena 1; Natzeck, Carsten 1; Wohlgemuth, Jonas 1; Scheuermann, Nina 1; Weidler, Peter G. 1; Wagner, Ilona ORCID iD icon 1; Wöll, Christof 1; Tsotsalas, Manuel ORCID iD icon 1
1 Institut für Funktionelle Grenzflächen (IFG), Karlsruher Institut für Technologie (KIT)

Abstract:

Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer-by-layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi-parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML-based approach allowing to quickly identify optimized growth conditions for HKUST-I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000154998
Veröffentlicht am 23.01.2023
Originalveröffentlichung
DOI: 10.1002/admi.202201771
Scopus
Zitationen: 13
Dimensions
Zitationen: 15
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2196-7350
KITopen-ID: 1000154998
HGF-Programm 43.33.11 (POF IV, LK 01) Adaptive and Bioinstructive Materials Systems
Erschienen in Advanced Materials Interfaces
Verlag John Wiley and Sons
Band 10
Heft 3
Seiten Art.-Nr. 2201771
Vorab online veröffentlicht am 04.12.2022
Nachgewiesen in Dimensions
Web of Science
Scopus
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