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Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h−1 gcat−1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability. Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains challenging. Here the authors successfully demonstrate an active learning strategy by integrating Bayesian optimization into experimental workflows to accelerate the design of highly active and stable FeCoCuZr catalysts for HAS. [ABSTRACT FROM AUTHOR]

Metal promotion could unlock high performance in zinc-zirconium catalysts, ZnZrOx, for CO2 hydrogenation to methanol. Still, with most efforts devoted to costly palladium, the optimal metal choice and necessary atomic-level architecture remain unclear. Herein, we investigate the promotion of ZnZrOx catalysts with small amounts (0.5 mol%) of diverse hydrogenation metals (Re, Co, Au, Ni, Rh, Ag, Ir, Ru, Pt, Pd, and Cu) prepared via a standardized flame spray pyrolysis approach. Cu emerges as the most effective promoter, doubling methanol productivity. Operando X-ray absorption, infrared, and electron paramagnetic resonance spectroscopic analyses and density functional theory simulations reveal that Cu0 species form Zn-rich low-nuclearity CuZn clusters on the ZrO2 surface during reaction, which correlates with the generation of oxygen vacancies in their vicinity. Mechanistic studies demonstrate that this catalytic ensemble promotes the rapid hydrogenation of intermediate formate into methanol while effectively suppressing CO production, showcasing the potential of low-nuclearity metal ensembles in CO2-based methanol synthesis.The ideal metal selection and atomic-level arrangement for catalysts in CO2 hydrogenation are still uncertain. Here, copper is identified as the most effective promoter for enhancing ZnZrOx catalysts when precisely structured into CuZn ensembles, offering new insights for designing superior catalysts for CO2-based methanol synthesis. [ABSTRACT FROM AUTHOR]

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model’s adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.Herein, the authors develop a transformer-based language model to automate synthesis protocol extraction from heterogeneous catalysis literature. Embracing digital advances in catalysis demands a shift in data reporting norms, and they offer guidelines for writing protocols, which improve machine readability. [ABSTRACT FROM AUTHOR]

Carbon supports are ubiquitous components of heterogeneous catalysts for acetylene hydrochlorination to vinyl chloride, from commercial mercury-based systems to more sustainable metal single-atom alternatives. Their potential co-catalytic role has long been postulated but never unequivocally demonstrated. Herein, we evidence the bifunctionality of carbons and metal sites in the acetylene hydrochlorination catalytic cycle. Combining operando X-ray absorption spectroscopy with other spectroscopic and kinetic analyses, we monitor the structure of single metal atoms (Pt, Au, Ru) and carbon supports (activated, non-activated, and nitrogen-doped) from catalyst synthesis, using various procedures, to operation at different conditions. Metal atoms exclusively activate hydrogen chloride, while metal-neighboring sites in the support bind acetylene. Resolving the coordination environment of working metal atoms guides theoretical simulations in proposing potential binding sites for acetylene in the support and a viable reaction profile. Expanding from single-atom to ensemble catalysis, these results reinforce the importance of optimizing both metal and support components to leverage the distinct functions of each for advancing catalyst design. Carbon is a key support for metal-catalyzed acetylene hydrochlorination to vinyl chloride but its role remains elusive. Here, the authors, by means of operando spectroscopy, demonstrate the co-catalytic function of neighboring carbon and isolated metal atoms, constituting the active ensemble. [ABSTRACT FROM AUTHOR]

Recently discovered phosphate-derived Ni catalysts have opened a new pathway towards multicarbon products via CO2 electroreduction. However, understanding the influence of basic parameters such as electrode potential, pH, and buffer capacity is needed for optimized C3+ product formation. To this end, rigorous catalyst evaluation and sensitive analytical tools are required to identify potential new products and minimize increasing quantification errors linked to long-chain carbon compounds. Herein, we contribute to enhance testing accuracy by presenting sensitive 1H NMR spectroscopy protocols for liquid product assessment featuring optimized water suppression and reduced experiment time. When combined with an automated NMR data processing routine, samples containing up to 12 products can be quantified within 15 min with low quantification limits equivalent to Faradaic efficiencies of 0.1%. These developments disclosed performance trends in carbon product formation and the detection of four hitherto unreported compounds: acetate, ethylene glycol, hydroxyacetone, and i-propanol. Phosphate-derived nickel catalysts enable access to multicarbon products via CO2 electroreduction, but it remains unclear how operating conditions influence product formation. Here, refined 1H NMR spectroscopy protocols are introduced and combined with an automated NMR data processing routine, enabling quantification of carbon product formation as a function of various parameters. [ABSTRACT FROM AUTHOR]

Palladium promotion and deposition on monoclinic zirconia are effective strategies to boost the performance of bulk In2O3 in CO2-to-methanol and could unlock superior reactivity if well integrated into a single catalytic system. However, harnessing synergic effects of the individual components is crucial and very challenging as it requires precise control over their assembly. Herein, we present ternary Pd-In2O3-ZrO2 catalysts prepared by flame spray pyrolysis (FSP) with remarkable methanol productivity and improved metal utilization, surpassing their binary counterparts. Unlike established impregnation and co-precipitation methods, FSP produces materials combining low-nuclearity palladium species associated with In2O3 monolayers highly dispersed on the ZrO2 carrier, whose surface partially transforms from a tetragonal into a monoclinic-like structure upon reaction. A pioneering protocol developed to quantify oxygen vacancies using in situ electron paramagnetic resonance spectroscopy reveals their enhanced generation because of this unique catalyst architecture, thereby rationalizing its high and sustained methanol productivity. Assembling multicomponent catalysts to harness synergic effects is challenging. Now, flame spray pyrolysis permits the synthesis of ternary Pd-In2O3-ZrO2 catalysts with an optimal architecture and an enriched density of oxygen vacancies for maximal performance in CO2-based methanol synthesis. [ABSTRACT FROM AUTHOR]

Recently discovered phosphate-derived Ni catalysts have opened a new pathway towards multicarbon products via CO 2 electroreduction. However, understanding the influence of basic parameters such as electrode potential, pH, and buffer capacity is needed for optimized C 3+ product formation. To this end, rigorous catalyst evaluation and sensitive analytical tools are required to identify potential new products and minimize increasing quantification errors linked to long-chain carbon compounds. Herein, we contribute to enhance testing accuracy by presenting sensitive 1 H NMR spectroscopy protocols for liquid product assessment featuring optimized water suppression and reduced experiment time. When combined with an automated NMR data processing routine, samples containing up to 12 products can be quantified within 15 min with low quantification limits equivalent to Faradaic efficiencies of 0.1%. These developments disclosed performance trends in carbon product formation and the detection of four hitherto unreported compounds: acetate, ethylene glycol, hydroxyacetone, and i-propanol., (© 2023. The Author(s).)