Regioisomeric Benzidine-Fullerenes: Tuning of the Diverse Hole-Distribution to Influence Charge Separation Patterns.

Understanding the influence of molecular structure on charge distribution and charge separation (CS) provides essential guidance for optoelectronic materials design. Here we propose a regioisomeric strategy to tune the diverse hole-distribution, and probe the influence on CS patterns. Para-, meta- and ortho-substituted benzidine-fullerene, named 1 p, 1 m and 1 o are designed. Following CS, hole-delocalization occurs in 1 p, while hole-localization exists in 1 m and 1 o. The rates of charge separation (4.02×1011  s-1 ) and recombination (9.8×109  s-1 ) of 1 p is about 20 and 12 times faster than 1 m and 1 o, indicating that para-determined delocalization promotes ultrafast CS, while meta- and ortho-generated localization contributes to long-lived CS states. Computational analysis further implies that localization results from the destruction of electronic conjugation for 1 m, and limitation of conformational relaxation for 1 o. Given that the universality and simplicity of regional isomerism, this work opens up new thoughts for molecular design with tunable charge separation patterns.

Efficient Traffic Video Dehazing Using Adaptive Dark Channel Prior and Spatial⁻Temporal Correlations.

In order to restore traffic videos with different degrees of haziness in a real-time and adaptive manner, this paper presents an efficient traffic video dehazing method using adaptive dark channel prior and spatial-temporal correlations. This method uses a haziness flag to measure the degree of haziness in images based on dark channel prior. Then, it gets the adaptive initial transmission value by establishing the relationship between the image contrast and haziness flag. In addition, this method takes advantage of the spatial and temporal correlations among traffic videos to speed up the dehazing process and optimize the block structure of restored videos. Extensive experimental results show that the proposed method has superior haze removing and color balancing capabilities for the images with different degrees of haze, and it can restore the degraded videos in real time. Our method can restore the video with a resolution of 720 × 592 at about 57 frames per second, nearly four times faster than dark-channel-prior-based method and one time faster than image-contrast-enhanced method.

An efficient multi-scale learning method for image super-resolution networks.

The image super-resolution (SR) operation holds multiple solutions with the one-to-many mapping from low-resolution (LR) to high-resolution (HR) space. However, the SR of different scales for the same image is usually regarded as independent tasks in the existing SR networks. Therefore, these networks are inflexible to effectively utilize feature learning experience and require much more computing time to recover HR images in higher resolutions. Recent arbitrary scale SR methods still cannot solve these problems. To efficiently and effectively recover HR images, this paper presents an efficient multi-scale learning method for image SR networks based on a novel self-generating (SG) mechanism. This method (briefly named SG-SR) utilizes the feature learning results of SR networks to generate upscale filters by using the novel SG upscale module, which is proposed to replace the traditional upscale module. For each scale factor, the SG upscale module provides the corresponding amount of the spatial weights to filter the LR tensor and then converts filtered tensors with the original tensor to corresponding HR images. The proposed method is evaluated through extensive experiments and compared with state-of-the-art (SOTA) methods on widely used benchmark datasets. The experimental results show that our method has superior performance compared with SOTA methods, and the SG upscale module can improve the performance of existing SR networks effectively. What is more, our module has a much less calculation cost than the other upscale modules.

Efficient Charge Separation and Transport in Fullerene-CuPcOC8 Donor-Acceptor Nanorod Enhancing Photocatalytic Hydrogen Generation.

Photocatalytic hydrogen generation via water decomposition is a promising avenue in the pursuit of large-scale, cost-effective renewable hydrogen energy generation. However, the design of an efficient photocatalyst plays a crucial role in achieving high yields in hydrogen generation. Herein, we have engineered a fullerene-2,3,9,10,16,17,23,24-octa(octyloxy)copper phthalocyanine (C60-CuPcOC8) photocatalyst, achieving both efficient hydrogen generation and high stability. The significant donor-acceptor (D-A) interactions facilitate the efficient electron transfer from CuPcOC8 to C60. The rate of photocatalytic hydrogen generation for C60-CuPcOC8 is 8.32 mmol·g-1·h-1, which is two orders of magnitude higher than the individual C60 and CuPcOC8. The remarkable increase in hydrogen generation activity can be attributed to the development of a robust internal electric field within the C60-CuPcOC8 assembly. It is 16.68 times higher than that of the pure CuPcOC8. The strong internal electric field facilitates the rapid separation within 0.6 ps, enabling photogenerated charge transfer efficiently. Notably, the hydrogen generation efficiency of C60-CuPcOC8 remains above 95%, even after 10 h, showing its exceptional photocatalytic stability. This study provides critical insight into advancing the field of photocatalysis.

Aggregation promotes charge separation in fullerene-indacenodithiophene dyad.

Fast photoinduced charge separation (CS) and long-lived charge-separated state (CSS) in small-molecules facilitate light-energy conversion, while simultaneous attainment of both remains challenging. Here we accomplish this through aggregation based on fullerene-indacenodithiophene dyads. Transient absorption spectroscopy reveals that, compared to solution, the CS time in aggregates is accelerated from 41.5 ps to 0.4 ps, and the CSS lifetime is prolonged from 311.4 ps to 40 µs, indicating that aggregation concomitantly promotes fast CS and long-lived CSS. Fast CS arises from the hot charge-transfer states dissociation, opening up additional resonant channels to free carriers (FCs); subsequently, charge recombination into intramolecular triplet CSS becomes favorable mediated by spin-uncorrelated FCs. Different from fullerene/indacenodithiophene blends, the unique CS mechanism in dyad aggregates reduces the long-lived CSS dependence on molecular order, resulting in a CSS lifetime 200 times longer than blends. This endows the dyad aggregates to exhibit both photoelectronic switch properties and superior photocatalytic capabilities.

FREE-RDW: A Multi-user Redirected Walking Method for Supporting Non-forward Steps.

Multi-user redirected walking (RDW) is widely used in large-scale virtual scenes because it allows more users to move synchronously in both virtual and physical environments. To ensure the freedom of virtual roaming, which can be used in various situations, some redirected algorithms have been dedicated to non-forward movements, such as vertical movement and jumping. However, the existing RDW methods still mainly focus on forward steps, ignoring sideward and backward steps, which are also common and necessary in virtual reality. RDW algorithms for non-forward steps can enrich the movement direction of users' virtual roaming and improve the realism of VR roaming. In addition, the non-forward motions have a larger curvature gain, which can be used to better reduce resets in RDW. Therefore, this paper presents a new method of multi-user redirected walking for supporting non-forward steps (FREE-RDW), which adds the options of sideward and backward steps to extend the VR locomotion. Our method adopts a user collision avoidance strategy based on optimal reciprocal collision avoidance (ORCA) and optimizes it into a linear programming problem to obtain the optimal velocity for users. Furthermore, our method uses APF to expose the user to repulsive forces from other users and walls, thus further reducing potential collisions and improving the utilization of physical space. The experiments show that our method performs well in virtual scenes with forward and non-forward steps. In addition, our method can significantly reduce the number of resets compared with reactive RDW algorithms such as DDB-RDW and APF-RDW in multi-user forward-step virtual scenes.

A time-critical adaptive approach for visualizing natural scenes on different devices.

To automatically adapt to various hardware and software environments on different devices, this paper presents a time-critical adaptive approach for visualizing natural scenes. In this method, a simplified expression of a tree model is used for different devices. The best rendering scheme is intelligently selected to generate a particular scene by estimating the rendering time of trees based on their visual importance. Therefore, this approach can ensure the reality of natural scenes while maintaining a constant frame rate for their interactive display. To verify its effectiveness and flexibility, this method is applied in different devices, such as a desktop computer, laptop, iPad and smart phone. Applications show that the method proposed in this paper can not only adapt to devices with different computing abilities and system resources very well but can also achieve rather good visual realism and a constant frame rate for natural scenes.