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Photodetectors (PDs), as functional devices based on photon‐to‐electron conversion, are an indispensable component for the next‐generation Internet of Things system. The research of advanced and efficient PDs that meet the diverse demands is becoming a major task. Ferroelectric materials can develop a unique spontaneous polarization due to the symmetry‐breaking of the unit cell, which is switchable under an external electric field. Ferroelectric polarization field has the intrinsic characteristics of non‐volatilization and rewritability. Introducing ferroelectrics to effectively manipulate the band bending and carrier transport can be non‐destructive and controllable in the ferroelectric–optoelectronic hybrid systems. Hence, ferroelectric integration offers a promising strategy for high‐performance photoelectric detection. This paper reviews the fundamentals of optoelectronic and ferroelectric materials, and their interactions in hybrid photodetection systems. The first section introduces the characteristics and applications of typical optoelectronic and ferroelectric materials. Then, the interplay mechanisms, modulation effects, and typical device structures of ferroelectric–optoelectronic hybrid systems are discussed. Finally, in summary and perspective section, the progress of ferroelectrics integrated PDs is summed up and the challenges of ferroelectrics in the field of optoelectronics are considered. [ABSTRACT FROM AUTHOR]

Although previous studies have made some clear leap in learning latent dynamics from high‐dimensional representations, the performances in terms of accuracy and inference time of long‐term model prediction still need to be improved. In this study, a deep convolutional network based on the Koopman operator (CKNet) is proposed to model non‐linear systems with pixel‐level measurements for long‐term prediction. CKNet adopts an autoencoder network architecture, consisting of an encoder to generate latent states and a linear dynamical model (i.e., the Koopman operator) which evolves in the latent state space spanned by the encoder. The decoder is used to recover images from latent states. According to a multi‐step ahead prediction loss function, the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini‐batch manner. In this manner, gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self‐adaptively tune the latent state space in the training process, and the resulting model is time‐invariant in the latent space. Therefore, the proposed CKNet has the advantages of less inference time and high accuracy for long‐term prediction. Experiments are performed on OpenAI Gym and Mujoco environments, including two and four non‐linear forced dynamical systems with continuous action spaces. The experimental results show that CKNet has strong long‐term prediction capabilities with sufficient precision. [ABSTRACT FROM AUTHOR]

Developing pH-universal and stable electrocatalysts for processes such as the hydrogen evolution reaction (HER) is challenging but necessary to achieve low-cost commercialization of hydrogen production. In this work, a facile and versatile method of carbon coating is developed to construct a MoO2, Mo2C and C core–shell heterostructure (MoO2–Mo2C@C). This heterostructure showed excellent HER performance in both acidic and alkaline solutions. The catalysts required overpotentials of 176 mV and 129 mV to reach a current density of 10 mA cm−2 in acidic and alkaline solutions, respectively. Furthermore, the catalysts exhibited ultra-high structural stability over 200 hours of I–t tests. The results indicated that the porous carbon shell-coated heterojunction provides a MoO2–Mo2C heterostructure with high electrical conductivity, an increased number of active sites, and strong structural stability, thus boosting the HER performance. Theoretical calculations were also conducted to study the formation mechanism of the core–shell heterostructure, which indicated possible pathways for the transformation from MoO2 (011) to Mo2C (100). [ABSTRACT FROM AUTHOR]

Patients with COVID-19 have been reported to experience neurological complications, although the main cause of death in these patients was determined to be lung damage. Notably, SARS-CoV-2-induced pathological injuries in brains with a viral presence were also found in all fatal animal cases. Thus, an appropriate animal model that mimics severe infections in the lungs and brain needs to be developed. In this paper, we compared SARS-CoV-2 infection dynamics and pathological injuries between C57BL/6Smoc-Ace2em3(hACE2-flag-Wpre-pA)Smoc transgenic hACE2-C57 mice and Syrian hamsters. Importantly, the greatest viral distribution in mice occurred in the cerebral cortex neuron area, where pathological injuries and cell death were observed. In contrast, in hamsters, viral replication and distribution occurred mainly in the lungs but not in the cerebrum, although obvious ACE2 expression was validated in the cerebrum. Consistent with the spread of the virus, significant increases in IL-1β and IFN-γ were observed in the lungs of both animals. However, in hACE2-C57 mice, the cerebrum showed noticeable increases in IL-1β but only mild increases in IFN-γ. Notably, our findings revealed that both the cerebrum and the lungs were prominent infection sites in hACE2 mice infected with SARS-CoV-2 with obvious pathological damage. Furthermore, hamsters exhibited severe interstitial pneumonia from 3 dpi to 5 dpi, followed by gradual recovery. Conversely, all the hACE2-C57 mice experienced severe pathological injuries in the cerebrum and lungs, leading to mortality before 5 dpi. According to these results, transgenic hACE2-C57 mice may be valuable for studying SARS-CoV-2 pathogenesis and clearance in the cerebrum. Additionally, a hamster model could serve as a crucial resource for exploring the mechanisms of recovery from infection at different dosage levels. [ABSTRACT FROM AUTHOR]

Stress-strain analysis has been an interesting issue for the mechanical design of composite structures. In this paper, a three-dimensional mechanical model based on generalized method of cells is presented to study the thermal residual stress and loading rates influence on the mechanical responses of short fiber-reinforced (SFR) composites. The effects of the fiber shape on the elastic constant of the SFR were investigated. To verify the prediction method, the calculated elastic modulus was compared with the results of finite element method. On this basis, a unified constitutive model is used to acquire the nonlinear properties of matrix materials. For comparison, SFR composites with and without consideration of thermal residual stress influences on the nonlinear responses are both considered. The results show that the distinct difference for SFR composites can be found at an early stage of loading. Meanwhile, the thermal residual stress influences on the mechanical behaviors present two characteristic stages.