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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. 

Assortment optimization finds many important applications in both brick-and-mortar and online retailing. Decision makers select a subset of products to offer to customers from a universe of substitutable products, based on the assumption that customers purchase according to a Markov chain choice model, which is a very general choice model encompassing many popular models. The existing literature predominantly assumes that the customer arrival process and the Markov chain choice model parameters are given as input to the stochastic optimization model. However, in practice, decision makers may not have this information and must learn them while maximizing the total expected revenue on the fly. In “Online Learning for Constrained Assortment Optimization under the Markov Chain Choice Model,” S. Li, Q. Luo, Z. Huang, and C. Shi developed a series of online learning algorithms for Markov chain choice-based assortment optimization problems with efficiency, as well as provable performance guarantees. 

We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. This problem is motivated from fast emerging studies on the safety evaluation of intelligent systems, robustness quantification of learning models, and other potential applications to large-scale simulation in which machine learning tools can be used to approximate complex rare-event set boundaries. We investigate an importance sampling scheme that integrates the dominating point machinery in large deviations and sequential mixed integer programming to locate the underlying dominating points. Our approach works for a range of neural network architectures including fully connected layers, rectified linear units, normalization, pooling and convolutional layers, and random forests built from standard decision trees. We provide efficiency guarantees and numerical demonstration of our approach using a classification model in the UCI Machine Learning Repository. 

Abstract The outstanding performance of organic-inorganic metal trihalide solar cells benefits from the exceptional photo-physical properties of both electrons and holes in the material. Here, we directly probe the free-carrier dynamics in Cs-doped FAPbI₃thin films by spatiotemporal photoconductivity imaging. Using charge transport layers to selectively quench one type of carriers, we show that the two relaxation times on the order of 1 μs and 10 μs correspond to the lifetimes of electrons and holes in FACsPbI₃, respectively. Strikingly, the diffusion mapping indicates that the difference in electron/hole lifetimes is largely compensated by their disparate mobility. Consequently, the long diffusion lengths (3~5 μm) of both carriers are comparable to each other, a feature closely related to the unique charge trapping and de-trapping processes in hybrid trihalide perovskites. Our results unveil the origin of superior diffusion dynamics in this material, crucially important for solar-cell applications.