Metal Poly(heptazine imides) as Multifunctional Photocatalysts for Solar Fuel Production.

Solar-driven photocatalysis employing particulate semiconductors represents a promising approach for sustainable production of valuable chemical feedstock. Metal poly(heptazine imide) (MPHI), a novel 2D ionic carbon nitride, has been recognized as an emerging photocatalyst with distinctive properties. In this minireview, we first delineate the forefront innovations of MPHI photocatalysts, spanning from synthetic strategies and solving structures to the exploration of novel properties. We place special emphasis on the structural design principles aimed at developing high-performance MPHI systems toward photocatalytic solar fuel production such as H2 evolution, H2O oxidation, H2O2 production and CO2 reduction. Finally, we discuss crucial insights and challenges in leveraging highly active MPHIs for efficient solar-to-chemical energy conversion.

Bridging electrocatalyst and cocatalyst studies for solar hydrogen production via water splitting.

Solar-driven water-splitting has been considered as a promising technology for large-scale generation of sustainable energy for succeeding generations. Recent intensive efforts have led to the discovery of advanced multi-element-compound water-splitting electrocatalysts with very small overpotentials in anticipation of their application to solar cell-assisted water electrolysis. Although photocatalytic and photoelectrochemical water-splitting systems are more attractive approaches for scaling up without much technical complexity and high investment costs, improving their efficiencies remains a huge challenge. Hybridizing photocatalysts or photoelectrodes with cocatalysts has been an effective scheme to enhance their overall solar energy conversion efficiencies. However, direct integration of highly-active electrocatalysts as cocatalysts introduces critical factors that require careful consideration. These additional requirements limit the design principle for cocatalysts compared with electrocatalysts, decelerating development of cocatalyst materials. This perspective first summarizes the recent advances in electrocatalyst materials and the effective strategies to assemble cocatalyst/photoactive semiconductor composites, and further discusses the core principles and tools that hold the key in designing advanced cocatalysts and generating a deeper understanding on how to further push the limits of water-splitting efficiency.

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system.

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSET = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSET = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.