Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology

Abstract

Metal-Organic Frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, Zeolitic Imidazolate Framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view of the system's behavior. To provide a unified view, we have generated a comprehensive dataset of synthetic conditions and their final outputs and applied Machine Learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given portion of the chemical space and to reveal the underlying impact of synthetic variables on the final particle size and morphology. By doing so, we draw connections between the role of each synthetic variable over ZIF-8 synthesis and provided with a rule of thumb to control the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream trends, which can guide the rational design of ZIF-8 particles with specific sizes and morphology, determining their suitability for any targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind synthesis procedures employed for their synthesis.

From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach

“…In the realm of chemical sciences, machine learning applications frequently utilize extensive data sets amassed through assorted automated algorithms. ,, However, as data set size escalates, the capability to oversee the quality of individual entries diminishes. Specifically, while manual scrutiny and elimination of questionable or low-quality data points is feasible for smaller data sets (250–500 records), it becomes impractical for larger data sets.…”