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Abstract Autonomous exploration is a critical technology to realize robotic intelligence as it allows unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a novel Deep Reinforcement Learning (DRL) based autonomous exploration strategy is proposed to efficiently reduce the unknown area of the workspace and provide accurate 2D map construction for mobile robots. Different from existing human-designed exploration techniques that usually make strong assumptions about the scenarios and the tasks, we utilize a model-free method to directly learn an exploration strategy through trial-and-error interactions with complex environments. To be specific, the Generalized Voronoi Diagram (GVD) is first utilized for domain conversion to obtain a high-dimensional Topological Environmental Representation (TER). Then, the Generalized Voronoi Networks (GVN) with spatial awareness and episodic memory is designed to learn autonomous exploration policies interactively online. For complete and efficient exploration, Invalid Action Masking (IAM) is employed to reshape the configuration space of exploration tasks to cope with the explosion of action space and observation space caused by the expansion of the exploration range. Furthermore, a well-designed reward function is leveraged to guide the learning of policies. Extensive baseline tests and comparative simulations show that our strategy outperforms the state-of-the-art strategies in terms of map quality and exploration speed. Sufficient ablation studies and mobile robot experiments demonstrate the effectiveness and superiority of our strategy.

Photocatalysis technology based on solar-powered semiconductors is widely recognized as a promising approach for achieving eco-friendly, secure, and sustainable degradation of organic contaminants. Nevertheless, conventional photocatalysts exhibit drawbacks such as a wide bandgap, and rapid recombination of photoinduced electron/hole pairs, in addition to complicated separation and recovery procedures. In this research, we cultivated BiOBr in situ on the surface of copper foam to fabricate a functional photocatalyst (denoted as BiOBr/Cu foam), which was subsequently employed for the photodegradation of Methylene Blue. Based on photodegradation experiments, the 0.3 BiOBr/Cu foam demonstrates superior photocatalytic efficacy compared to other photocatalysts under solar light irradiation. Furthermore, its ease of separation from the solution enhances its potential for reuse. The analysis of charge transfer revealed that the copper foam functions as an effective electron scavenger within the BiOBr/Cu foam, thereby facilitating charge separation and the generation of photo-induced holes. This phenomenon contributes to a significantly enhanced production of hydroxyl radicals. This study provides a valuable perspective on the design and synthesis of photocatalysts with heightened practicality, employing a conductive substrate.

Water droplets containing the SARS-CoV-2 virus, responsible for coronavirus 2019 transmission, were introduced into a controlled-temperature and -humidity chamber. The SARS-CoV-2 virus with green fluorescent protein tag in droplets was used to infect Caco-2 cells, with viability assessed through flow cytometry and microscopic counting. Whereas temperature fluctuations within typical indoor ranges (20°C–30°C) had minimal impact, we observed a notable decrease in infection rate as the surrounding air’s relative humidity increased. By investigating humidity levels between 20% and 70%, we identified a threshold of ≥40% relative humidity as most effective in diminishing SARS-CoV-2 infectivity. We also found that damage of the viral proteins under high relative humidity may be responsible for the decrease in their activity. This outcome supports previous research demonstrating a rise in the concentration of reactive oxygen species within water droplets with elevated relative humidity.

Abstract One‐step deposition approaches have been widely applied and developed in the fabrication of quasi‐2D perovskites. However, the regulation of quantum wells (QWs) and crystalline orientation is difficult and complicated when using this methodology. Sequential deposition is another widespread synthetic approach for preparing perovskite films and perovskite dimension engineering. In this article, δ‐CsPbI3 intermediate phase assisted sequential (IPAS) deposition is successfully carried out to fabricate MA‐free quasi‐2D ACI perovskites. The amount of the δ‐CsPbI3 intermediate phase in the PbI2 layer and the concentration of GAI molecule in the IPA solution both play important roles in the production of MA‐free quasi‐2D ACI perovskite films. The n value of the MA‐free quasi‐2D ACI perovskites can be adjusted, which affects the photovoltaic performance and device stability. Compared with one‐step deposition, the MA‐free quasi‐2D ACI perovskites prepared via IPAS deposition have opposite reverse‐graded QW distribution and improved vertical orientation, leading to a remarkable PEC of up to 18.86% and allowing the preparation of unpackaged devices with prominent working stability (80%, ~400 h). The underlying mechanism and crystallization pathway of IPAS deposition confirm that sequential deposition has unique superiority in regulating the QW distribution and crystalline orientation of quasi‐2D perovskites.

When plants encounter external environmental stimuli, they can adapt to environmental changes through a complex network of metabolism–gene expression–metabolism within the plant cell. In this process, changes in the characteristics of plant cells are a phenotype that is responsive and directly linked to this network. Accurate identification of large numbers of plant cells and quantitative analysis of their cellular characteristics is a much-needed experiment for in-depth analysis of plant metabolism and gene expression. This study aimed to develop an automated, accurate, high-throughput quantitative analysis method, ACFVA, for single-plant-cell identification. ACFVA can quantitatively address a variety of biological questions for a large number of plant cells automatically, including standard assays (for example, cell localization, count, and size) and complex morphological assays (for example, different fluorescence in cells). Using ACFVA, phenomics studies can be carried out at the plant cellular level and then combined with ever-changing sequencing technologies to address plant molecular biology and synthetic biology from another direction.