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Si quantum dot (QD)-molecule hybrid systems have emerged as a popular architecture in many research fields due to the ability to select for the advantages conferred by the inorganic Si component and the organic sections. This perspective will focus on the optical properties of Si QDs, the parameters that affect Si QD photophysics or energy transfer in Si QD-molecule hybrid structures, and their resultant hybrid optoelectronic devices. Examples of recent applications that employ Si QD-molecule hybrid materials are presented. Finally, we discuss current issues involving basic structure–property relationships that need to be addressed for Si QDs and conclude with an outlook on the bright future of Si QD-molecule hybrid materials. 

Plasmonic nanoparticles with chiral resonances in the visible wavelengths complement optical dissymmetry in the ultraviolet and near-infrared wavelengths in natural products and metamaterials respectively. Here, we show that under oxidative conditions, hot holes photogenerated with circularly polarized light in gold nanoprisms can spatially direct the photodeposition of lead oxide (PbO2), resulting in chiral nanostructures tunable with the polarization and wavelength of light. We observe a g-factor of 3.6 × 10–3, which can be attributed to the enhanced optical dissymmetry with PbO2 deposition of the side of nanoprisms upon illumination with green 532 nm light. Our finite-difference time-domain calculations support the site-specific photodeposition of PbO2 onto nanoprisms. This work shows that plasmonic nanoparticles can have tunable chiral properties imbued as a function of the wavelength and polarization of light. 

Photon upconversion is a process that combines low-energy photons to form useful high-energy photons. There are potential applications in photovoltaics, photocatalysis, biological imaging, etc. Semiconductor quantum dots (QDs) are promising for the absorption of these low-energy photons due to the high extinction coefficient of QDs, especially in the near infrared (NIR). This allows the intriguing use of diffuse light sources such as solar irradiation. In this review, we describe the development of this organic-QD upconversion platform based on triplet-triplet annihilation, focusing on the dark exciton in QDs with triplet character. Then we introduce the underlying energy transfer steps, starting from QD triplet photosensitization, triplet exciton transport, triplet-triplet annihilation, and ending with the upconverted emission. Design principles to improve the total upconversion efficiency are presented. We end with limitations in current reports and proposed future directions. This review provides a guide for designing efficient organic-QD upconversion platforms for future applications, including overcoming the Shockley-Queisser limit for more efficient solar energy conversion, NIR-based phototherapy, and diagnostics in vivo. 

Abstract Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.