Designing Materials Acceleration Platforms for Heterogeneous CO2 Photo(thermal)catalysis

Materials acceleration platforms (MAPs) combine automation and artificial intelligence to accelerate the discovery of molecules and materials. They have potential to play a role in addressing complex societal problems such as climate change. Solar chemicals and fuels generation via heterogeneous CO2 photo(thermal)catalysis is a relatively unexplored process that holds potential for contributing towards an environmentally and economically sustainable future, and therefore a very promising application for MAP science and engineering. Here, we present a brief overview of how design and innovation in heterogeneous CO2 photo(thermal)catalysis, from materials discovery to engineering and scale-up, could benefit from MAPs. We discuss relevant design and performance descriptors and the level of automation of state-of-the-art experimental techniques, and we review examples of artificial intelligence in data analysis. Based on these precedents, we finally propose a MAP outline for autonomous and accelerated discoveries in the emerging field of solar chemicals and fuels sourced from CO2 photo(thermal)catalysis

The next big thing for silicon nanostructures – CO₂ photocatalysis

Silicene is a relatively new member of the growing family of two-dimensional single-element materials. Both top-down and bottom-up approaches provide access to silicene, the former via vapor deposition on a substrate and the latter via exfoliation of the layered CaSi₂ precursor. Most top-down research has been concerned with understanding the various electronic, optical, magnetic, mechanical, electrical, thermal transport and gas-adsorption properties of silicene. By contrast, the focus on bottom-up silicene has primarily been on its synthesis, structure and chemical properties as they relate to its function and utility. Herein, emphasis is placed on the bottom-up strategy because of its scalability and the ease of subsequent silicene modification, with both qualities being important prerequisites for heterogeneous catalysis applications. In this context, synthetic freestanding silicene exists as single sheets or multilayer assemblies, depending on the CaSi₂ exfoliation synthesis conditions. The structure of a sheet comprises three connected chair-configuration silicon 6-rings. This connectivity creates buckled sheets in which the hybridization around the unsaturated silicon atoms is sp²–sp³. By adjusting the CaSi₂ exfoliation synthesis conditions, either layered silane (Si₆H₆) or siloxene (Si₆H₃(OH)₃) nanosheets can be obtained. In our studies, we have explored the nucleation and growth of different transition metal nanoparticles on and within the layer spaces of these nanosheets, and explored their thermochemical and photochemical reactivity in CO₂ hydrogenation reactions. An overview of these findings, related works and a new-and-optimized catalyst are provided in this article

High entropy liquid electrolytes for lithium batteries

High-entropy alloys/compounds have large configurational entropy by introducing multiple components, showing improved functional properties that exceed those of conventional materials. However, how increasing entropy impacts the thermodynamic/kinetic properties in liquids that are ambiguous. Here we show this strategy in liquid electrolytes for rechargeable lithium batteries, demonstrating the substantial impact of raising the entropy of electrolytes by introducing multiple salts. Unlike all liquid electrolytes so far reported, the participation of several anionic groups in this electrolyte induces a larger diversity in solvation structures, unexpectedly decreasing solvation strengths between lithium ions and solvents/anions, facilitating lithium-ion diffusivity and the formation of stable interphase passivation layers. In comparison to the single-salt electrolytes, a low-concentration dimethyl ether electrolyte with four salts shows an enhanced cycling stability and rate capability. These findings, rationalized by the fundamental relationship between entropy-dominated solvation structures and ion transport, bring forward high-entropy electrolytes as a composition-rich and unexplored space for lithium batteries and beyond.</p

Entropy-Driven Liquid Electrolytes for Lithium Batteries

Developing liquid electrolytes with higher kinetics and enhanced interphase stability is one of the key challenges for lithium batteries. However, the poor solubility of lithium salts in solvents sets constraints that compromises the electrolyte properties. Here, it is shown that introducing multiple salts to form a high-entropy solution, alters the solvation structure, which can be used to raise the solubility of specific salts and stabilize electrode–electrolyte interphases. The prepared high-entropy electrolytes significantly enhance the cycling and rate performance of lithium batteries. For lithium-metal anodes the reversibility exceeds 99%, which extends the cycle life of batteries even under aggressive cycling conditions. For commercial batteries, combining a graphite anode with a LiNi0.8Co0.1Mn0.1O2 cathode, more than 1000 charge–discharge cycles are achieved while maintaining a capacity retention of more than 90%. These performance improvements with respect to regular electrolytes are rationalized by the unique features of the solvation structure in high-entropy electrolytes. The weaker solvation interaction induced by the higher disorder results in improved lithium-ion kinetics, and the altered solvation composition leads to stabilized interphases. Finally, the high-entropy, induced by the presence of multiple salts, enables a decrease in melting temperature of the electrolytes and thus enables lower battery operation temperatures without changing the solvents.</p

High entropy liquid electrolytes for lithium batteries

High-entropy alloys/compounds have large configurational entropy by introducing multiple components, showing improved functional properties that exceed those of conventional materials. However, how increasing entropy impacts the thermodynamic/kinetic properties in liquids that are ambiguous. Here we show this strategy in liquid electrolytes for rechargeable lithium batteries, demonstrating the substantial impact of raising the entropy of electrolytes by introducing multiple salts. Unlike all liquid electrolytes so far reported, the participation of several anionic groups in this electrolyte induces a larger diversity in solvation structures, unexpectedly decreasing solvation strengths between lithium ions and solvents/anions, facilitating lithium-ion diffusivity and the formation of stable interphase passivation layers. In comparison to the single-salt electrolytes, a low-concentration dimethyl ether electrolyte with four salts shows an enhanced cycling stability and rate capability. These findings, rationalized by the fundamental relationship between entropy-dominated solvation structures and ion transport, bring forward high-entropy electrolytes as a composition-rich and unexplored space for lithium batteries and beyond.RST/Storage of Electrochemical EnergyRID/TS/Instrumenten groepBT/Biocatalysi

Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models

Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks (i.e., nodes and linkers) in different topologies. Many of them have high internal surface areas and other desirable properties for gas storage, separation, and other applications. The notable variety of the possible building blocks and the diverse ways they can be assembled endow reticular frameworks with a near-infinite combinatorial design space, making reticular chemistry both promising and challenging for prospective materials design. Here, we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder (SmVAE) for the generative design of reticular materials with desired functions. We demonstrate the automated design process with a class of metal-organic framework (MOF) structures and the goal of separating CO2 from natural gas or flue gas. Our model exhibits high fidelity in capturing structural features and reconstructing MOF structures. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported. This platform lays the groundwork for the design of reticular frameworks for desired applications