This report is to address the question if black γ-polymorph of cesium tin tri-iodide (B-γ-CsSnI3) can be used as a solid-state hole-transport material in the conventional DSSCs with the N719 dye to replace the liquid electrolyte as reported by I. Chung et al. on Nature 485, 486, (2012). Here we demonstrate rigorously that B-γ-CsSnI3 is not energetically possible to collect photogenerated holes because of the large energy barrier at the interface of N719/B-γ-CsSnI3. Therefore, it cannot serve as a hole-transporter for the conventional DSSCs although it is a good hole-conducting material. A solution-based method was employed to synthesize the B-γ-CsSnI3 polycrystalline thin-films used for this work. These thin-films were then characterized by X-ray diffraction, Hall measurements, optical reflection, and photoluminescence (PL). Particularly, spatially resolved PL intensity images were taken after B-γ-CsSnI3 was incorporated in the DSSC structure to insure the material integrity. The means of ultraviolet photoemission spectroscopy (UPS) was used to reveal why B-γ-CsSnI3 could not act as the substitute of liquid electrolyte in the conventional DSSCs. For the completeness, other two related compounds, one is the yellow polymorph of CsSnI3 and other is Cs2SnI6 with tetravalent tin instead of double-valent tin in CsSnI3 were also investigated by UPS.

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How to generate testing scenario library for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario library. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model will be generated as the result of the adaptive process. To estimate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newdesigned acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in and a highway exit case are studied for safety and functionality evaluation respectively. For both two cases, the proposed method can further accelerate the evaluation process by a few orders of magnitudes.

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