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Abstract Chalcogenide perovskites, particularly BaZrS₃, hold promise for optoelectronic devices owing to their exceptional light absorption and inherent stability. However, thin films obtained at lower processing temperatures typically result in small grain sizes and inferior transport properties. Here we introduce an approach employing co-sputtering elemental Ba and Zr targets followed by CS₂sulfurization, with a judiciously applied NaF capping layer. NaF acts as a flux agent during sulfurization, leading to marked increase in grain size and improved crystallinity. This process results in near-stoichiometric films with enhanced photoresponse. Terahertz spectroscopy further reveals a carrier mobility more than two orders of magnitude higher than those obtained from field-effect transistor measurements, suggesting that bulk transport is limited by grain boundary scattering. Our results demonstrate flux-assisted sulfurization as an effective strategy to improve the crystallinity of chalcogenide perovskite thin films for optoelectronic applications. Graphical abstract 

This paper presents a study on traffic flow models in one-dimensional (1D) and two-dimensional (2D) lattices. The models incorporate generalized look-ahead rules that consider nonlocal slowdown effects. The proposed cellular automata (CA) models use stochastic rules to determine the movement of cars based on the traffic configuration ahead of each car. Specifically, a look-ahead rule is used that considers both the car density ahead and a generalized interaction function based on the distance between cars. The CA models are simulated using an efficient kinetic Monte Carlo (KMC) algorithm. The numerical results in 1D demonstrate that the flows from the KMC simulations align with the macroscopic averaged fluxes for the look-ahead rule, across various parameter settings. In the 2D results, a sharp phase transition is observed from freely flowing traffic to global jamming, depending on the initial density of cars. 

A well-reasoned model of data movie in single molecule localization microscopy (SMLM) is desired. A model of data movie can be decomposed into a model of emitter activation process and a model of data frame. In this paper, we focus on Markov chain modeling and analyzing of emitter activation process for both cycled and continuous illuminations. First, a two-phase Markov chain is proposed to model the activation process for a pair of conjugated activator and emitter with cycled illumination. By converting the frame-based Markov chain into several cycle-based Markov chains, the stationary state distribution in the photoactivatable period is derived. Further obtained are several formulas that capture the characterization of the two-phase Markov chain. Second, the Markov chain and analytical result are extended to the continuous illumination where an emitter is excited continuously in all frames. Finally, incorporating the model of emitter activation process with our previous model of data frame, the model of data movie for both cycled and continuous illuminations in 3D and 2D imaging are simulated by custom codes. It is shown that the model can synthesize data movies well and the analytical formulas predict the simulation results accurately. The models provide a means to be broadly utilized in generating well-reasoned data movies for training of neural networks and evaluation of localization algorithms.