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Polar molecular crystals, with their densely stacked polar nonlinear optical (NLO) active units, are favored for their large second harmonic generation (SHG) responses and birefringence. However, their potential for practical applications as Infrared (IR) NLO materials has historically been underappreciated due to the weak inter‐molecular interaction forces that may compromise their physicochemical properties. In this study, we propose molecular crystals with polar molecular cages as a treasure‐house for the development of superior IR NLO materials and a representative system, binary chalcogenide molecular crystals, composed of [P4Sn] (n=3–9) polar molecular cages, is introduced. These crystals may not only achieve wide band gap, large SHG response, and birefringence in a single structure, but also exhibit favorable physicochemical properties. We subsequently obtained a polar molecular crystal, α‐P4S5, which demonstrated exceptional IR optical properties, including a strong SHG response (1.1×AGS), wide band gap (3.02 eV), large birefringence (0.134@2050 nm), and a broad transmission range (0.41–14.7 μm). Moreover, it showed excellent water resistance and hardness. These findings highlight the potential of polar molecular crystals as a promising platform for the development of high‐performance IR NLO materials. [ABSTRACT FROM AUTHOR]

Acquiring high‐performance ultraviolet (UV) nonlinear optical (NLO) materials that simultaneously exhibit a strong second harmonic generation (SHG) coefficients, as short as possible SHG phase‐matching (PM) wavelength and non‐hygroscopic properties has consistently posed a significant challenge. Herein, through multicomponent modification of KBe2BO3F2 (KBBF), an excellent UV NLO crystal, Mg(C3O4H2)(H2O)2, was successfully synthesized in malonic system. This material possesses a unique 2D NLO‐favorable electroneutral [Mg(C3O4H2)3(H2O)2]∞ layer, resulting in the rare coexistence of a strong SHG response of 3×KDP (@1064 nm) and short PM wavelength of 200 nm. More importantly, it exhibits exceptional water resistance, which is rare among ionic organic NLO crystals. Theoretical calculations revealed that its excellent water‐resistant may be originated from its small available cavity volumes, which is similar to the famous LiB3O5 (LBO). Therefore, excellent NLO properties and stability against air and moisture indicate it should be a promising UV NLO crystal. [ABSTRACT FROM AUTHOR]

Ionic organic crystals containing organic planar π‐conjugated units has become one of the hot spots as nonlinear optical (NLO) materials. However, although this type of ionic organic NLO crystals commonly have remarkable second harmonic generation (SHG) responses, they also suffer from overlarge birefringences and relatively small band gaps that be hardly beyond 6.2 eV. Herein, a flexible π‐conjugated [C3H(CH3)O4]2− unit was theoretically revealed, showing great potential for designing NLO crystals with balanced optical properties. Accordingly, through the reasonable NLO‐favourable layered design, a new ionic organic material, NH4[LiC3H(CH3)O4], was successfully obtained. As expected, it achieves not only a large SHG effect (4×KDP), but also a suitable birefringence (0.06@546 nm) and an ultrawide band gap (>6.5 eV). This study provides a new flexible π‐conjugated NLO‐active unit, contributing to design more ionic organic NLO materials with excellent balanced optical properties. [ABSTRACT FROM AUTHOR]

The modern tram track automatic cleaning car is a crucial equipment in urban rail transportation systems, effectively removing trash, dust, and other debris from the slotted tracks of trams. However, due to the complex and variable structure of turnouts, the cleaning car often requires assistance in accurately detecting their positions. Consequently, the cleaning car needs help in adequately cleaning or bypassing turnouts, which adversely affects cleaning effectiveness and track maintenance quality. This paper presents a novel method for tracking turnout identification called PBE-YOLO based on the improved yolov5s framework. The algorithm enhances yolov5s by optimizing the lightweight backbone network, improving feature fusion methods, and optimizing the regression loss function. The proposed method is trained using a dataset of track turnouts collected through field shots on modern tram lines. Comparative experiments are conducted to analyze the performance of the improved lightweight backbone network, as well as performance comparisons and ablation experiments for the new turnout identification method. Experimental results demonstrate that the proposed PBE-YOLO method achieves a 52.71% reduction in model parameters, a 4.60% increase in mAP@0.5(%), and a 3.27% improvement in precision compared to traditional yolov5s. By improving the track turnout identification method, this paper enables the automatic cleaning car to identify turnouts' positions accurately. This enhancement leads to several benefits, including increased automation levels, improved cleaning efficiency and quality, reduced reliance on manual intervention, and mitigation of collision risks between the cleaning car and turnouts. [ABSTRACT FROM AUTHOR]