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Recent progress in the fabrication and application of diverse spherical titania nanostructures, including mesoporous spheres, spherical fl aky assemblies, and dendritic particles of variable diameter and monodispersity in size, is summarized in this article. Utilizing different synthesis strategies, spherical titania nanostructures with tailored polymorphs (including amorphous, anatase, rutile, brookite and TiO 2 -B), particle sizes (from tens of nanometers to millimeters), monodispersity, porosity, and variable surface properties have been produced. Such spherical titania nanostructures show realized and potential applications in the areas of chromatographic separation, lithium-ion batteries, dye-sensitized solar cells, photocatalytic oxidation and water splitting, photoluminescence, electrorheological fl uids, catalysis, gas sensing, and anticancer intracellular drug delivery. Gaining further understanding of both synthesis design and application of these materials will promote the commercialization of such spherical titania nanostructures in the future.

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/ photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/ photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

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its high specific capacity of 1675 mA h g −1 , which is based on the redox reaction that converts sulfur to lithium sulfide (Li 2 S) reversibly via various intermediate lithium polysulfides (Li 2 S n , n = 2-8). [2] This and the abundance of low-cost sulfur makes the LSB one of the more promising future battery technologies. [2b,3] However, commercialization of LSBs has generally been hampered by low sulfur utilization and poor long-term cyclability. This is due to the poor conductivity of the pristine sulfur, large volumetric expansion during the discharge process (80% volumetric expansion from S to Li 2 S), high solubility of the lithium polysulfides and passivation of the reactive surface of the metallic lithium anode. [2d,4] Dissolution of lithium polysulfides triggers capacity loss via the so-called "shuttle effect" during cycling, which lowers the Coulombic efficiency (CE) of the cell. This problem can be addressed, in part, by producing a cathode in which the sulfur is encapsulated. [5] To tackle these issues, novel host materials have been developed for the sulfur cathode to limit the movement of the lithium polysulfides. These include various carbonaceous materials such as mesoporous carbon, [3b,6] porous carbon capsules, [7] graphene, [8] conductive polymers (such as polypyrrole [9] and polyaniline [10] ), and carbon interlayers. [11] While carbonaceous host materials on the cathode all give the desirable properties of low density and high electronic conductivity, the non-polar nature of the carbon surface offers limited attraction to the polar lithium polysulfides. Therefore, while improved electrochemical performance can be achieved with the available carbonaceous materials, the general reliance on physical trapping and/ or weak chemical bonding limits the level of sulfur loading. [8b] Furthermore, modest improvements have been realized through different morphologies of the carbonaceous materials. Hierarchically porous nanostructures interconnected with conductive walls such as microspheres composed of graphene and nanotubes, demonstrated enhanced entrapment of lithium polysulfides. [8c,12] Overall, even the best carbonaceous host materials do not appear likely to meet the ultimate demands of LSB technology.A number of inorganic oxides have attracted research attention as cathode additives due to the prospect of providing Various host materials have been investigated to address the intrinsic drawbacks of lithium sulfur batteries, such as the low electronic conductivity of sulfur and inevitable decay in capacity during cycling. Besides the widely investigated carbonaceous materials, metal oxides have drawn much attention because they form strong chemical bonds with the soluble lithium polysulfides. Here, mesoporous Magnéli Ti 4 O 7 microspheres are prepared via an in situ carbothermal reduction that exhibit interconnected mesopores (20.4 nm), large pore volume (0.39 cm 3 g −1 ), and high surface area (197.2 m 2 g −1 ). When the sulfur cathode is embedded in a matrix of mesoporous Magnéli T...