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1. A deep neural network for oxidative coupling of methane trained on high-throughput experimental data

   https://doi.org/10.1088/2515-7655/aca797  

   Ziu, Klea; Solozabal, Ruben; Rangarajan, Srinivas; Takáč, Martin (December 2022, Journal of Physics: Energy)

   Abstract In this work, we develop a deep neural network model for the reaction rate of oxidative coupling of methane from published high-throughput experimental catalysis data. A neural network is formulated so that the rate model satisfies the plug flow reactor design equation. The model is then employed to understand the variation of reactant and product composition within the reactor for the reference catalyst Mn–Na₂WO₄/SiO₂at different temperatures and to identify new catalysts and combinations of known catalysts that would increase yield and selectivity relative to the reference catalyst. The model revealed that methane is converted in the first half of the catalyst bed, while the second part largely consolidates the products (i.e. increases ethylene to ethane ratio). A screening study of $⩾3400$combinations of pairs of previously studied catalysts of the form M1(M2) ${}_{1-2}$M3O_x/support (where M1, M2 and M3 are metals) revealed that a reactor configuration comprising two sequential catalyst beds leads to synergistic effects resulting in increased yield of C₂compared to the reference catalyst at identical conditions and contact time. Finally, an expanded screening study of 7400 combinations (comprising previously studied metals but with several new permutations) revealed multiple catalyst choices with enhanced yields of C₂products. This study demonstrates the value of learning a deep neural network model for the instantaneous reaction rate directly from high-throughput data and represents a first step in constraining a data-driven reaction model to satisfy domain information. 

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2. Network-Supported, Metal-Mediated Catalysis: Progress and Perspective

   https://doi.org/10.1039/D0RE00229A  

   Bennett, Jeffrey A; Davis, Bradley; Efimenko, Kirill; Genzer, Jan; Abolhasani, Milad (January 2020, Reaction Chemistry & Engineering)

   Metal-mediated chemical reactions have been a vital area of research for over a century. Recently, there has been increasing effort to improve the performance of metal-mediated catalysis by optimizing the structure and chemical environment of active catalytic species towards process intensification and sustainability. Network-supported catalysts use a solid (rigid or flexible) support with embedded metal catalysts, ideally allowing for efficient precursor access to the catalytic sites and simultaneously not requiring a catalyst separation step following the reaction with minimal catalyst leaching. This minireview focuses on recent developments of network-supported catalysts to improve the performance of a wide range of metal-mediated catalytic reactions. We discuss in detail the different strategies to realize the combined benefits of homogeneous and heterogeneous catalysis in a metal catalyst support. We outline the unique versatility, tunability, properties, and activity of such hybrid catalysts in batch and continuous flow configurations. Furthermore, we present potential future directions to address some of the challenges and shortcomings of current flexible network-supported catalysts. 

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3. Electro‐ and Photochemical Reduction of CO ₂ by Molecular Manganese Catalysts: Exploring the Positional Effect of Second‐Sphere Hydrogen‐Bond Donors

   https://doi.org/10.1002/cssc.202001940  

   Roy, Sayontani_Sinha; Talukdar, Kallol; Jurss, Jonah_W (November 2020, ChemSusChem)

   Abstract A series of molecular Mn catalysts featuring aniline groups in the second‐coordination sphere has been developed for electrochemical and photochemical CO₂reduction. The arylamine moieties were installed at the 6 position of 2,2’‐bipyridine (bpy) to generate a family of isomers in which the primary amine is located at theortho‐(1‐Mn),meta‐(2‐Mn), orpara‐site (3‐Mn) of the aniline ring. The proximity of the second‐sphere functionality to the active site is a critical factor in determining catalytic performance. Catalyst1‐Mn, possessing the shortest distance between the amine and the active site, significantly outperformed the rest of the series and exhibited a 9‐fold improvement in turnover frequency relative to parent catalyst Mn(bpy)(CO)₃Br (901 vs. 102 s⁻¹, respectively) at 150 mV lower overpotential. The electrocatalysts operated with high faradaic efficiencies (≥70 %) for CO evolution using trifluoroethanol as a proton source. Notably, under photocatalytic conditions, a concentration‐dependent shift in product selectivity from CO (at high [catalyst]) to HCO₂H (at low [catalyst]) was observed with turnover numbers up to 4760 for formic acid and high selectivities for reduced carbon products. 

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4. Bioinspired Design of Hybrid Polymer Catalysts with Multicopper Sites for Oxygen Reduction

   https://doi.org/10.1002/cctc.202001333  

   Jin, Lei; Thanneeru, Srinivas; Cintron, Daniel; He, Jie (September 2020, ChemCatChem)

   Abstract Cu‐containing metalloenzymes are known to catalyze oxygen activation through cooperative catalysis. In the current work, we report the design of synthetic polymer Cu catalysts using pyrene‐labelled poly(2‐hydroxy‐3‐dipicolylamino) propyl methacrylate (Py‐PGMADPA) to coordinate multiple Cu sites along polymer chains. The catalysts feature a pyrene end group that can form supramolecular π‐π stacking with conductive carbon to allow efficient immobilization of catalysts to the graphite electrode. Cu‐containing Py‐PGMADPA was examined for electrocatalytic oxygen reduction. The hybrid catalyst showed an onset potential of 0.5 V (vs. RHE) at pH 7 and 0.79 V at pH 13. The kinetic study indicated that the catalyst had a 2e⁻reduction of oxygen mainly mediated by Cu⁺centers. We demonstrated the importance of cooperative catalysis among Cu sites which did not exist for other transition metal ions, like Mn²⁺, Fe²⁺, Co²⁺, and Ni²⁺. The confinement of polymer chains promotes the activity and stabilizes Cu catalysts even at an extremely low Cu loading. The rational design of bioinspired polymer catalysts offers an alternative way to prepare synthetic mimics of metalloenzymes. 

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5. A tailored multi-functional catalyst for ultra-efficient styrene production under a cyclic redox scheme

   https://doi.org/10.1038/s41467-021-21374-2  

   Zhu, Xing; Gao, Yunfei; Wang, Xijun; Haribal, Vasudev; Liu, Junchen; Neal, Luke M.; Bao, Zhenghong; Wu, Zili; Wang, Hua; Li, Fanxing (February 2021, Nature Communications)

   Abstract Styrene is an important commodity chemical that is highly energy and CO₂intensive to produce. We report a redox oxidative dehydrogenation (redox-ODH) strategy to efficiently produce styrene. Facilitated by a multifunctional (Ca/Mn)_1−xO@KFeO₂core-shell redox catalyst which acts as (i) a heterogeneous catalyst, (ii) an oxygen separation agent, and (iii) a selective hydrogen combustion material, redox-ODH auto-thermally converts ethylbenzene to styrene with up to 97% single-pass conversion and >94% selectivity. This represents a 72% yield increase compared to commercial dehydrogenation on a relative basis, leading to 82% energy savings and 79% CO₂emission reduction. The redox catalyst is composed of a catalytically active KFeO₂shell and a (Ca/Mn)_1−xO core for reversible lattice oxygen storage and donation. The lattice oxygen donation from (Ca/Mn)_1−xO sacrificially stabilizes Fe³⁺in the shell to maintain high catalytic activity and coke resistance. From a practical standpoint, the redox catalyst exhibits excellent long-term performance under industrially compatible conditions. 

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