Revisiting Catalytic Cycles: A Broader View through the Energy Span Model

Bioinspired Binickel Catalyst for Carbon Dioxide Reduction: The Importance of Metal-ligand Cooperation

Catalyst design for the efficient CO2 reduction reaction (CO2RR) remains a crucial challenge for the conversion of CO2 to fuels. Natural Ni-Fe carbon monoxide dehydrogenase (NiFe-CODH) achieves reversible conversion of CO2 and CO at nearly thermodynamic equilibrium potential, which provides a template for developing CO2RR catalysts. However, compared with the natural enzyme, most biomimetic synthetic Ni-Fe complexes exhibit negligible CO2RR catalytic activities, which emphasizes the significance of effective bimetallic cooperation for CO2 activation. Enlightened by bimetallic synergy, we herein report a dinickel complex, NiIINiII(bphpp)(AcO)2 (where NiNi(bphpp) is derived from H2bphpp = 2,9-bis(5-tert-butyl-2-hydroxy-3-pyridylphenyl)-1,10-phenanthroline) for electrocatalytic reduction of CO2 to CO, which exhibits a remarkable reactivity approximately 5 times higher than that of the mononuclear Ni catalyst. Electrochemical and computational studies have revealed that the redox-active phenanthroline moiety effectively modulates the electron injection and transfer akin to the [Fe3S4] cluster in NiFe-CODH, and the secondary Ni site facilitates the C-O bond activation and cleavage through electron mediation and Lewis acid characteristics. Our work underscores the significant role of bimetallic cooperation in CO2 reduction catalysis and provides valuable guidance for the rational design of CO2RR catalysts.

Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.

Chemical reaction network knowledge graphs: the OntoRXN ontology

Abstract

The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-022-00610-x.

Homogeneous Catalytic CO2 Hydrogenation by [Fe]-Hydrogenase Bioinspired Complexes: A Computational Study

Computational modeling at the DLPNO-CCSD(T)/CBS//M06-L/def2-TZVP level of theory was used to propose four different iron catalysts whose structures were inspired on the [Fe]-hydrogenase active site: [Fe(^MeP^NNH^NP)(acmp)] (C(1), ^MeP^NNH^NP = 2,6-bis(dimethylphosphine), acmp = acylmethylpyridine), [Fe(C^NNH^NC)(acmp)] (C(2), C^NNH^NC = 2,6-bis(methylimidazol-2-ylidene)), [Fe(^MeP^NN^NP)(acmp)] (D(1), ^MeP^NN^NP = 2,6-bis((dimethylphosphine)pyridine)), and [Fe(C^NN^NC)(acmp)] (D(2), C^NN^NC = 2,6-bis((methylimidazol-2-ylidene) pyridine)). Through these electronic structure calculations, the catalytic mechanism of the reaction was explored. The intermediates and transition states present along the reaction coordinate were identified and described as to their equilibrium geometries, vibrational frequencies, and energies. Quasi-harmonic corrections were performed considering conditions analogous to those used experimentally. To compare the catalytic activities of the studied catalysts, turnover frequencies (TOFs) were calculated. Based on the explored catalytic cycles and TOF values (D(1) > C(1) > D(2) > C(2)), the most suitable iron catalysts are those with tridentate phosphine pincer-type ligands coordinated to the metal center. These systems are new promising iron catalysts to promote the CO₂ hydrogenation to formic acid without any use of bases or additives.

First-principles-informed energy span and microkinetic analysis of ethanol catalytic conversion to 1,3-butadiene on MgO

First-principles-informed models elucidate the impact of energetic and kinetic limitations on selectivity and activity of ethanol conversion to 1,3-butadiene.