Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design.

Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band-gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material-photoreactor synergy. These factors include achieving photogeneration and charge-carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi-dimensional problem necessitates harnessing machine learning techniques to analyze real-world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics-informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions.

Plasmonic Titanium Nitride Facilitates Indium Oxide CO2 Photocatalysis.

Nanoscale titanium nitride TiN is a metallic material that can effectively harvest sunlight over a broad spectral range and produce high local temperatures via the photothermal effect. Nanoscale indium oxide-hydroxide, In2 O3- x (OH)y , is a semiconducting material capable of photocatalyzing the hydrogenation of gaseous CO2 ; however, its wide electronic bandgap limits its absorption of photons to the ultraviolet region of the solar spectrum. Herein, the benefits of both nanomaterials in a ternary heterostructure: TiN@TiO2 @In2 O3- x (OH)y are combined. This heterostructured material synergistically couples the metallic TiN and semiconducting In2 O3- x (OH)y phases via an interfacial semiconducting TiO2 layer, allowing it to drive the light-assisted reverse water gas shift reaction at a conversion rate greatly surpassing that of its individual components or any binary combinations thereof.

Black indium oxide a photothermal CO2 hydrogenation catalyst.

Nanostructured forms of stoichiometric In2O3 are proving to be efficacious catalysts for the gas-phase hydrogenation of CO2. These conversions can be facilitated using either heat or light; however, until now, the limited optical absorption intensity evidenced by the pale-yellow color of In2O3 has prevented the use of both together. To take advantage of the heat and light content of solar energy, it would be advantageous to make indium oxide black. Herein, we present a synthetic route to tune the color of In2O3 to pitch black by controlling its degree of non-stoichiometry. Black indium oxide comprises amorphous non-stoichiometric domains of In2O3-x on a core of crystalline stoichiometric In2O3, and has 100% selectivity towards the hydrogenation of CO2 to CO with a turnover frequency of 2.44 s-1.