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Hybrid perovskites have become a leading candidate for next-generation photovoltaic applications, but they still suffer from several intrinsic and extrinsic sources of instability. Residual tensile stress has emerged as a major source of extrinsic instability, as it originates from thin film fabrication and choice of substrate. Here, we propose an experimentally validated mechanical stress-diffusion framework which links dynamic stress relaxation behavior of perovskites to vacancy-mediated grain boundary (GB) halide diffusional, namely, Coble creep. Using temperature-dependent in situ analysis of stress and strain relaxation across the perovskite composition, we quantitatively link these mechanisms. We extend this framework to link these mechanisms to material stability in the context of ambient manufacturing in the presence of humidity. The Coble creep mechanism is circumvented by the fast diffusion of water molecules, with potentially severe impact on material degradation. We show how pre-relaxation of perovskite films in a dry environment (0% RH and N2) extends lifetime from <100 hours to >1,000 hours in moderately humid air by removing the driving force for moisture uptake. This work identifies the fundamental mechanisms of stress relaxation and diffusion responsible for moisture uptake and proposes stress relaxation approaches which facilitate ambient manufacturing of existing materials and fabrication approaches under moderate humidity conditions. 

To accelerate materials discovery using self-driving labs (SDLs), we present a machine learning pipeline that predicts the electrical conductivity of doped conjugated polymers using rapid, non-destructive optical spectroscopy. 

Global health challenges, including the continued discovery of new antibiotic resistant bacteria strains, hospital-acquired infections, and transmission of both endemic and novel diseases (e.g., COVID-19) motivate the need for large-scale access to surfaces capable of self-sterilization. Among the most promising methods for such applications, antimicrobial photodynamic inactivation (aPDI) utilizes the visible light illumination of a light absorbing material to produce reactive oxygen species to inflict non-specific oxidative damage to pathogens. Unfortunately, these techniques can suffer from challenges, including photosensitizer instability at elevated temperatures (which limits the available processing methods), and photobleaching under prolonged light exposure. In this work, we present a facile and scalable hydrolysis-based route for the synthesis of copper-doped titania (TiO2) particles for highly effective light-activated antiviral and antibacterial applications. The performance of the synthesized Cu-doped TiO2 particles is then evaluated using solution-phase antimicrobial photodynamic inactivation assays. We demonstrate that the Cu-doped TiO2 particles can successfully inactivate a wide range of pathogens with exposure to light for 90 min, including bacteria ranging from methicillin-resistant Staphylococcus aureus (99.9999%, ~6 log units) to Klebsiella pneumoniae (99.93%, ~3.3 log units), and viruses including feline calicivirus (99.94%, ~3.4 log units) and HCoV-229E (99.996%, ~4.6 log units), with the particles demonstrating excellent robustness towards photobleaching. Furthermore, a spray-coated polymer film, loaded with the synthesized Cu-doped TiO2 particles achieves inactivation of methicillin-resistant Staphylococcus aureus up to 99.998% (~4.8 log units). The presented results provide a clear advance forward in the use of metal-doped titania for aPDI applications, including the scalable synthesis (kg/day) of well-characterized and robust particles, their facile incorporation into a non-toxic, photo-stable coating that may be easily and cheaply applied to a multitude of surfaces, and a broad efficacy against drug-resistant Gram-positive and Gram-negative bacteria, as well as against enveloped and non-enveloped viruses. 

Abstract Hybrid magnonic systems are a newcomer for pursuing coherent information processing owing to their rich quantum engineering functionalities. One prototypical example is hybrid magnonics in antiferromagnets with an easy-plane anisotropy that resembles a quantum-mechanically mixed two-level spin system through the coupling of acoustic and optical magnons. Generally, the coupling between these orthogonal modes is forbidden due to their opposite parity. Here we show that the Dzyaloshinskii–Moriya-Interaction (DMI), a chiral antisymmetric interaction that occurs in magnetic systems with low symmetry, can lift this restriction. We report that layered hybrid perovskite antiferromagnets with an interlayer DMI can lead to a strong intrinsic magnon-magnon coupling strength up to 0.24 GHz, which is four times greater than the dissipation rates of the acoustic/optical modes. Our work shows that the DMI in these hybrid antiferromagnets holds promise for leveraging magnon-magnon coupling by harnessing symmetry breaking in a highly tunable, solution-processable layered magnetic platform. 

The vast chemical space of emerging semiconductors, like metal halide perovskites, and their varied requirements for semiconductor applications have rendered trial-and-error environmentally unsustainable. In this work, we demonstrate RoboMapper, a materials acceleration platform (MAP), that achieves 10-fold research acceleration by formulating and palletizing semiconductors on a chip, thereby allowing high-throughput (HT) measurements to generate quantitative structure-property relationships (QSPRs) considerably more efficiently and sustainably. We leverage the RoboMapper to construct QSPR maps for the mixed ion FA 1-y Cs y Pb(I 1-x Br x ) 3 halide perovskite in terms of structure, bandgap, and photostability with respect to its composition. We identify wide-bandgap alloys suitable for perovskite-Si hybrid tandem solar cells exhibiting a pure cubic perovskite phase with favorable defect chemistry while achieving superior stability at the target bandgap of 1.7 eV. RoboMapper’s palletization strategy reduces environmental impacts of data generation in materials research by more than an order of magnitude, paving the way for sustainable data-driven materials research.