Comparison of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of Aromatic Nitro Compounds

Towards Machine Learning in Heterogeneous Catalysis-A Case Study of 2,4-Dinitrotoluene Hydrogenation

Utilization of multivariate data analysis in catalysis research has extraordinary importance. The aim of the MIRA21 (MIskolc RAnking 21) model is to characterize heterogeneous catalysts with bias-free quantifiable data from 15 different variables to standardize catalyst characterization and provide an easy tool to compare, rank, and classify catalysts. The present work introduces and mathematically validates the MIRA21 model by identifying fundamentals affecting catalyst comparison and provides support for catalyst design. Literature data of 2,4-dinitrotoluene hydrogenation catalysts for toluene diamine synthesis were analyzed by using the descriptor system of MIRA21. In this study, exploratory data analysis (EDA) has been used to understand the relationships between individual variables such as catalyst performance, reaction conditions, catalyst compositions, and sustainable parameters. The results will be applicable in catalyst design, and using machine learning tools will also be possible.

Overview of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of 2,4-Dinitrotoluene

Although 2,4-dinitrotoluene (DNT) hydrogenation to 2,4-toluenediamine (TDA) has become less significant in basic and applied research, its industrial importance in polyurethane production is indisputable. The aim of this work is to characterize, rank, and compare the catalysts of 2,4-dinitrotoluene catalytic hydrogenation to 2,4-toluenediamine by applying the Miskolc Ranking 21 (MIRA21) model. This ranking model enables the characterization and comparison of catalysts with a mathematical model that is based on 15 essential parameters, such as catalyst performance, reaction conditions, catalyst conditions, and sustainability parameters. This systematic overview provides a comprehensive picture of the reaction, technological process, and the previous and new research results. In total, 58 catalysts from 15 research articles were selected and studied with the MIRA21 model, which covers the entire scope of DNT hydrogenation catalysts. Eight catalysts achieved the highest ranking (D1), whereas the transition metal oxide-supported platinum or palladium catalysts led the MIRA21 catalyst ranking list.

Palladium Decorated N-Doped Carbon Foam as a Highly Active and Selective Catalyst for Nitrobenzene Hydrogenation

Carbon foam was synthesized by the carbonization of 4-nitroaniline. The reaction is an alternative of the well-known “carbon snake” (or sugar snake) demonstration experiment, which leads to the formation of nitrogen-doped carbon foils due to its nitrogen content. The synthesized carbon foils were grinded to achieve an efficient catalyst support. Palladium nanoparticles were deposited onto the surface of the support, which showed continuous distribution. The prepared Pd nanoparticle decorated carbon foils showed high catalytic activity in nitrobenzene hydrogenation. By applying the designed catalyst, total nitrobenzene conversion, a 99.1 n/n% aniline yield, and an exceptionally high selectivity (99.8 n/n%) were reached. Furthermore, the catalyst remained active during the reuse tests (four cycles) even without regeneration.