Abstract: CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability.

Abstract: Density functional theory is widely used to gain insights into molecule−metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA)
density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the selfinteraction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface
reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule−metal surface adsorption
and activation energies. Hence, it is a better choice for constructing molecule−metal surface reaction networks.

Abstract: Determining temperature changes at the heating site to accurately control thermal treatments has been a major goal in the field of nanothermometry. In this study, we address the need to effectively monitor local temperature during the application of photothermal therapies, which is essential to prevent uncontrolled heating induced by nanoparticle sensitizers used in such treatments. For this purpose, we developed a synthetic protocol to produce a nanocomposite probe that allows local photothermal heating and simultaneous in situ optical nanothermometry, within the biological transparency windows. The nanocomposite material comprises gold nanorods for light-to-heat conversion and neodymium (Nd3+)-based nanoparticles for local temperature monitoring. An inert spacer made of mesoporous silica provides a core-shell structure and ensures uniform separation between both functionalities to prevent photoluminescence quenching. By using an 808 nm laser as the source for both heating and photoluminescence excitation, we demonstrate a direct correlation between local temperature and near infrared Nd3+ emission intensities, thereby providing precise local temperature monitoring. Different levels of local heating were studied by varying the incident laser power, resulting in a maximum temperature increase of 47 °C detected with the nanothermometers. Albeit presented here as a proof of concept, this concept can be translated to the design of materials for photothermal therapy.