Advancing lithium-sulfur battery efficiency: utilizing a 2D/2D g-C3N4@MXene heterostructure to enhance sulfur evolution reactions and regulate polysulfides under lean electrolyte conditions.

Lithium-sulfur batteries (LSBs) show promise for achieving a high energy density of 500 W h kg-1, despite challenges such as poor cycle life and low energy efficiency due to sluggish redox kinetics of lithium polysulfides (LiPSs) and sulfur's electronic insulating nature. We present a novel 2D Ti3C2 Mxene on a 2D graphitic carbon nitride (g-C3N4) heterostructure designed to enhance LiPS conversion kinetics and adsorption capacity. In a pouch cell configuration with lean electrolyte conditions (∼5 µL mg-1), the g-C3N4-Mx/S cathode exhibited excellent rate performance, delivering ∼1061 mA h g-1 at C/8 and retaining ∼773 mA h g-1 after 190 cycles with a Coulombic efficiency (CE) of 92.7%. The battery maintained a discharge capacity of 680 mA h g-1 even at 1.25 C. It operated reliably at an elevated sulfur loading of 5.9 mg cm-2, with an initial discharge capacity of ∼900 mA h g-1 and a sustained CE of over 83% throughout 190 cycles. Postmortem XPS and EIS analyses elucidated charge-discharge cycle-induced changes, highlighting the potential of this heterostructured cathode for commercial garnet LSB development.

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.