Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis.

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor.

Colloidal platinum nanoparticles (Pt NPs) possess a myriad of technologically relevant applications. A potentially sustainable route to synthesize Pt NPs is via polyol reduction in ionic liquid (IL) solvents; however, the development of this synthetic method is limited by the fact that reaction kinetics have not been investigated. In-line analysis in a flow reactor is an appealing approach to obtain such kinetic data; unfortunately, the optical featurelessness of Pt NPs in the visible spectrum complicates the direct analysis of flow chemistry products via ultraviolet-visible (UV-vis) spectrophotometry. Here, we report a machine learning (ML)-based approach to analyze in-line UV-vis spectrophotometric data to determine Pt NP product concentrations. Using a benchtop flow reactor with ML-interpreted in-line analysis, we were able to investigate NP yield as a function of residence time for two IL solvents: 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf). While these solvents are structurally similar, the polyol reduction shows radically different yields of Pt NPs depending on which solvent is used. The approach presented here will help develop an understanding of how the subtle differences in the molecular structures of these solvents lead to distinct reaction behavior. The accuracy of the ML prediction was validated by particle size analysis and the error was found to be as low as 4%. This approach is generalizable and has the potential to provide information on various reaction outcomes stemming from solvent effects, for example, differential yields, orders of reaction, rate coefficients, NP sizes, etc.

Structure and mechanism of the alkane-oxidizing enzyme AlkB.

Alkanes are the most energy-rich form of carbon and are widely dispersed in the environment. Their transformation by microbes represents a key step in the global carbon cycle. Alkane monooxygenase (AlkB), a membrane-spanning metalloenzyme, converts straight chain alkanes to alcohols in the first step of the microbially-mediated degradation of alkanes, thereby playing a critical role in the global cycling of carbon and the bioremediation of oil. AlkB biodiversity is attributed to its ability to oxidize alkanes of various chain lengths, while individual AlkBs target a relatively narrow range. Mechanisms of substrate selectivity and catalytic activity remain elusive. Here we report the cryo-EM structure of AlkB, which provides a distinct architecture for membrane enzymes. Our structure and functional studies reveal an unexpected diiron center configuration and identify molecular determinants for substrate selectivity. These findings provide insight into the catalytic mechanism of AlkB and shed light on its function in alkane-degrading microorganisms.

Investigation of the prevalence and catalytic activity of rubredoxin-fused alkane monooxygenases (AlkBs).

Interest in understanding the environmental distribution of the alkane monooxygenase (AlkB) enzyme led to the identification of over 100 distinct alkane monooxygenase (AlkB) enzymes containing a covalently bound, or fused, rubredoxin. The rubredoxin-fused AlkB from Dietzia cinnamea was cloned as a full-length protein and as a truncated protein with the rubredoxin domain deleted. A point mutation (V91W) was introduced into the full-length protein, with the goal of assessing how steric bulk in the putative substrate channel might affect selectivity. Based on activity studies with alkane and alkene substrates, the rubredoxin-fused AlkB oxidizes a similar range of alkane substrates relative to its rubredoxin domain-deletion counterpart. Oxidation of terminal alkenes generated both an epoxide and a terminal aldehyde. The products of V91W-mutant-catalyzed oxidation of alkenes had a higher aldehyde-to-epoxide ratio than the products formed in the presence of the wild type protein. These results are consistent with this mutation causing a structural change impacting substrate positioning.