Rationalizing the Mechanism of Peroxyformate Decomposition: Computational Insights To Understand Solvent Influence

Abstract

The heterolytic decomposition of tert‐butyl peroxyformate to tert‐butanol and carbon dioxide, catalyzed by pyridine, is a long‐known example of a reaction whose kinetics are strongly affected by solvent polarity. From DFT and ab initio methods together with the SMD implicit solvation model, an extension on the formerly accepted mechanism is proposed. This novel proposal involves the formation of a carbonic acid ester intermediate and its further decomposition, through an unreported pyridine‐mediated stepwise route. Computed barriers for this mechanism at DLPNO/CCSD(T)‐def2‐TZVP are in excellent agreement with experimental kinetic data across different solvents. Furthermore, the strong relationships between activation energies, geometric parameters in the transition state and the characteristics of the different solvents are also analyzed in depth.

Chemical reaction network knowledge graphs: the OntoRXN ontology

“…We will be using our recent mechanistic study on the decomposition of tert-butyl peroxyformate [ 44 ], in Fig. 6 , to showcase the generation of knowledge graphs from the OntoRXN ontology.…”

“…This query produces a table listing all unique combinations of stages and solvents, combining the energies from the appropiate calculations across the complex KG containing results for 29 total implicit solvents and 300 individual calculations. From there, we may easily obtain plots comparing activation energies with the polarity of the solvent, as done in the initial mechanistic study [ 44 ] (see Additional file 1 , where Fig. S1 reproduces Figure 2 from that original study).…”

Chemical Reaction Network Knowledge Graphs: the OntoRXN Ontology

“…We will be using our recent mechanistic study on the decomposition of tert-butyl peroxyformate, 43 in Scheme 1, to showcase the generation of knowledge graphs from the OntoRXN ontology. This system provided us with a relatively simple mechanism (which can be encoded in a small reaction network), whose assorted calculations were already available at ioChem-BD, thus supposing an ideal test case for both On-toRXN and ontorxn-tools.…”

“…This query produces a table listing all unique combinations of stages and solvents, combining the energies from the appropiate calculations across the complex KG containing results for 29 total implicit solvents and 300 individual calculations. From there, we may easily obtain plots comparing activation energies with the polarity of the solvent, as done in the initial mechanistic study 43 (see Supporting Information, where Figure S1 reproduces Figure 2 from that original study). Listing 1: SPARQL query example to collect electronic energy, Gibbs free energy, solvent and solvent dielectric constant for all stages in the KG.…”

rNets: A standalone package to visualize reaction networks.

“…Typically, these more complex networks have been illustrated through manually drawn graphs, tracing the reaction mechanism, as depicted in the left part of Figure 1. The most used tools to trace these reaction mechanisms and linear energy diagrams manually are Adobe Illustrator [17,22], OriginLab [18,23,24], Inkscape [15,25,26], Matplotlib [27][28][29], and ChemDraw [10][11][12]30], among others. From there, it is possible to switch to a representation that is more akin to graphs in other domains of science, explicitly showing nodes as circles and edges as full connections between them (right side of Figure 1), using tools such as Graphviz [31].…”