Thermal Stability of Metal-Organic Frameworks (MOFs): Concept, Determination, and Model Prediction Using Computational Chemistry and Machine Learning

Harold U. Escobar-Hernandez, Lisa M. Pérez, Pingfan Hu, Fernando A. Soto, Maria I. Papadaki, Hong Cai Zhou, Qingsheng Wang

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The indubitable rise of metal-organic framework (MOF) technology has opened the potential for commercialization as alternative materials with a versatile number of applications that range from catalysis to greenhouse gas capture. However, there are several factors that constrain the direct scale-up of MOFs from laboratory to industrial plant given the insufficient knowledge about the overall safety in synthesis processes. This article focuses on the study of MOF thermal stability, from concept to prediction, and the factors that influence such stability. The core of this work is a thermal stability prediction model for MOFs. This model can be applied to existing and new MOF structures, and it will allow for an estimation of the thermal stability temperature range of MOFs. This work contributes to the overall advancement of MOF technology and the efforts for its commercial use at industrial scale, combining both experimental data and computational techniques.

Original languageEnglish (US)
Pages (from-to)5853-5862
Number of pages10
JournalIndustrial and Engineering Chemistry Research
Volume61
Issue number17
DOIs
StatePublished - May 4 2022

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering

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