Workshop: Combining Technologies to Accelerate MOF Materials Development
Groundbreaking Approach to Combining Technologies Opens Up New Horizons in MOF Development
One of the key limiting bottle necks in new Materials Development, especially when it comes to Metal Organic Frameworks, is time. Time to theorize, time to formulate, time to analyze. With potenially limitless permuatations to consider for synthesis, how are research labs that have limited resources supposed to perservere.
At Karlsruher Institut fuer Technologie (KIT), researchers in the Institute of Functional Interfaces (IFG) have been pioneering innovative ways of combining new AI technologies with the most advanced sorption characterization techniques to drastically reduce the time and work required to synthesise new MOF materials for Industrial applications. Employing special AI tools to simulate the countless variations possible in new MOF materials, and narrow down promising materials for testing, researchers are able to use sorption analysis techniques with much greater efficiency, analyzing AI-recommended materials for their potential in specific industrial applications.
This in-person workshop will see researchers from IFG, KIT, and Surface Measurement Systems take you on a exploratory tour of their work. Showcasing the innovative way their innovative methods have hugely improved research efficiency, and how when combined with accurate and precise analytical techniques, new MOF development has never been more promising. Register free now!
Dr. Vittorio Cappello, Surface Measurement Systems
Things will kick off witha quick welcome from Dr. Vittorio Cappello, who will give a brief introudction to the topic and agenda for the day, and prepare the audience for what's to come.
Prof. Dr. Pascal Freiderich, Institute for Theoretical Computer Science (ITI) - AiMat Group, Karlsruhe Institute of Technology
-Abstract coming soon-
Dr. Celso Ricardo Caldeira Rego, Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT)
In this talk, I'll present the ScienceAI project, which aims to combine predictive and generative AI into a single tool that connects experimental and theoretical science. This approach allows researchers from different domains to use AI-driven solutions without needing deep expertise in specific subfields. We designed ScienceAI to be versatile and applicable across various disciplines such as Chemistry, Physics, Material Science, Engineering, Agriculture, and more. We powered the project with the Llama family model, a frontier open-weight Large Language Model (LLM) modified to scientific needs. ScienceAI presently works in a minimally viable product mode, where students and researchers can register a personal account to exploit and extend LLM for the following research tasks: Document analysis and summary for PDF and CSV files, Content curation & semantic data analysis, Automated protocol generation for virtual materials design, Processing of large datasets (beyond ChatGPT), Privacy & data provenance, Connection with KIT-owned databases, API access (upcoming). ScienceAI introduces a new paradigm of scientific workflows. These workflows generate simulation protocols that are transformed into digital objects using the SimStack framework or similar tools.
Dr. Paul Iacomi, Surface Measurement Systems
The recent drive towards decarbonization has made CCUS (carbon capture, utilization and storage) a focal point of research. Identifying, characterizing and scaling-up solid adsorption-based carbon capture materials has become crucial to implement solutions with high recovery and low energy use. The multifunctional and tunable nature of MOFs has made them prime candidates for this application, with several promising materials already on the path towards commercialization.
Nevertheless, in the continuing search for MOFs suitable for post-combustion CO2 capture, one of the biggest challenges remains the screening of materials in realistic conditions [2] that can evaluate (i) the influence of contaminants such as H2O and SOx/NOx on equilibrium CO2 uptakes (ii) the kinetics of adsorption of all components and (iii) the long-term cycling performance.
In this talk, several prototypical MOFs and a reference material (Zeolite 13X) are screened in realistic conditions for CO2 capture using advanced dynamic gravimetric sorption and packed bed methods, which shed light on all above information. We explore the differences between the expected performance based on single component predictions and true multicomponent sorption, as well as degradation related to the effect of water and cycling.
References
[1] Z. Hu, Y. Wang, B. B. Shah, D. Zhao, Advanced Sustainable Systems 2018, 1800080.
[2] J. A. Mason, T. M. McDonald, T.-H. Bae, J. E. Bachman, K. Sumida, J. J. Dutton, S. S. Kaye, J. R. Long, Journal of the American Chemical Society 2015, 137, 4787–4803.
[3] N. S. Wilkins, J. A. Sawada, A. Rajendran, Adsorption 2020, 26, 765–779.
Dr. Anett Kondor, Surface Measurement Systems
-Abstract Coming Soon-
Tour the facilities on-site to see these techniques and instruments in action, and engage directly with leading experts in the field.