Dual carbon engineering enabling 1T/2H MoS2 with ultrastable potassium ion storage performance.

Potassium-ion batteries (PIBs) as a promising and low-cost battery technology offer the advantage of utilizing abundant and cost-effective K-salt sources. However, the effective adoption of PIBs necessitates the identification of suitable electrode materials. The 1T phase of MoS2 exhibits enhanced electronic conductivity and greater interlayer spacing compared to the 2H phase, leading to a capable potassium ion storage ability. Herein, we fabricated dual carbon engineered 1T/2H MoS2via a secure and straightforward ammonia-assisted hydrothermal method. The 1T/2H MoS2@rGO@C structure demonstrated an expanded interlayer spacing (9.3 Å). Additionally, the sandwich-like structural design not only enhanced material conductivity but also effectively curbed the agglomeration of nanosheets. Remarkably, 1T/2H MoS2@rGO@C exhibited impressive potassium storage ability, delivering capacities of 351.0 mA h g-1 at 100 mA g-1 and 233.8 mA h g-1 at 1000 mA g-1 following 100 and 1000 cycles, respectively. Moreover, the construction of a K-ion full cell was successfully achieved, utilizing perylene tetracarboxylic dianhydride (PTCDA) as the cathode, and manifesting a capacity of 294.3 mA h g-1 at 100 mA g-1 after 160 cycles. This underscores the substantial potential of employing the 1T/2H MoS2@rGO@C electrode material for PIBs.

Spatial Alignment for Unsupervised Domain Adaptive Single-Stage Object Detection.

Domain adaptation methods are proposed to improve the performance of object detection in new domains without additional annotation costs. Recently, domain adaptation methods based on adversarial learning to align source and target domain image distributions are effective. However, for object detection tasks, image-level alignment enforces the alignment of non-transferable background regions, which affects the performance of important target regions. Therefore, how to balance the alignment of background and target remains a challenge. In addition, the current research with good effect is based on two-stage detectors, and there are relatively few studies on single-stage detectors. To address these issues, in this paper, we propose a selective domain adaptation framework for the spatial alignment of a single-stage detector. The framework can identify the background and target and pay different attention to them. On the premise that the single-stage detector does not generate region suggestions, it can achieve domain feature alignment and reduce the influence of the background, enabling transfer between different domains. We validate the effectiveness of our method for weather discrepancy, camera angles, synthetic to real-world, and real images to artistic images. Extensive experiments on four representative adaptation tasks show that the method effectively improves the performance of single-stage object detectors in different domains while maintaining good scalability.