Formamidinium lead triiodide (FAPbI3) has been demonstrated as the most efficient perovskite system to date, due to its excellent thermal stability and an ideal bandgap approaching the Shockley-Queisser limit. Whereas, there are intrinsic quantum confinement effects in FAPbI3, which lead to unwanted non-radiative recombination. Additionally, the black α-phase of FAPbI3 is unstable under room temperature due to the significant residual tensile stress in the film. To simultaneously address the above issues, a thermally-activated delayed fluorescence polymer P1 is designed in the study to modify the FAPbI3 film. Owing to the spectral overlap between the photoluminescence of P1 and absorption of the above-bandgap quantum wells of FAPbI3, the Förster energy transfer occurs at the P1/FAPbI3 interface, which further triggers the Dexter energy transfer within FAPbI3. The exciton “recycling” can thus be realized, which reduces the non-radiative recombination losses in perovskite solar cells (PSCs). Moreover, P1 is found to introduce compressive stress into FAPbI3, which relieves the tensile stress in perovskite. Consequently, the PSCs with P1 treatment achieve an outstanding power conversion efficiency (PCE) of 23.51%. Moreover, with the alleviation of stress in the perovskite film, flexible PSCs (f-PSCs) also deliver a high PCE of 21.40%. 相似文献
Alloying-type metal sulfides with high theoretical capacities are promising anodes for sodium-ion batteries, but suffer from sluggish sodiation kinetics and huge volume expansion. Introducing intercalative motifs into alloying-type metal sulfides is an efficient strategy to solve the above issues. Herein, robust intercalative In S motifs are grafted to high-capacity layered Bi2S3 to form a cation-disordered (BiIn)2S3, synergistically realizing high-rate and large-capacity sodium storage. The In S motif with strong bonding serves as a space-confinement unit to buffer the volume expansion, maintaining superior structural stability. Moreover, the grafted high-metallicity Indium increases the bonding covalency of Bi S, realizing controllable reconstruction of Bi S bond during cycling to effectively prevent the migration and aggregation of atomic Bi. The novel (BiIn)2S3 anode delivers a high capacity of 537 mAh g−1 at 0.4 C and a superior high-rate stability of 247 mAh g−1 at 40 C over 10000 cycles. Further in situ and ex situ characterizations reveal the in-depth reaction mechanism and the breakage and formation of reversible Bi S bonds. The proposed space confinement and bonding covalency enhancement strategy via grafting intercalative motifs can be conducive to developing novel high-rate and large-capacity anodes. 相似文献
3D conformable electronic devices on freeform surfaces show superior performance to the conventional, planar ones. They represent a trend of future electronics and have witnessed exponential growth in various applications. However, their potential is largely limited by a lack of sophisticated fabrication techniques. To tackle this challenge, a new direct freeform laser (DFL) fabrication method enabled by a 5-axis laser processing platform for directly fabricating 3D conformable electronics on targeted arbitrary surfaces is reported. Accordingly, representative laser-induced graphene (LIG), metals, and metal oxides are successfully fabricated as high-performance sensing and electrode materials from different material precursors on various types of substrates for applications in temperature/light/gas sensing, energy storage, and printed circuit board for circuit. Last but not the least, to demonstrate an application in smart homes, LIG-based conformable strain sensors are fabricated and distributed in designated locations of an artificial tree. The distributed sensors have the capability of monitoring the wind speed and direction with the assistance of well-trained machine-learning models. This novel process will pave a new and general route to fabricating 3D conformable electronic devices, thus creating new opportunities in robotics, biomedical sensing, structural health, environmental monitoring, and Internet of Things applications. 相似文献
Maximizing network lifetime is the main goal of designing a wireless sensor network. Clustering and routing can effectively balance network energy consumption and prolong network lifetime. This paper presents a novel cluster-based routing protocol called EECRAIFA. In order to select the optimal cluster heads, Self-Organizing Map neural network is used to perform preliminary clustering on the network nodes, and then the relative reasonable level of the cluster, the cluster head energy, the average distance within the cluster and other factors are introduced into the firefly algorithm (FA) to optimize the network clustering. In addition, the concept of decision domain is introduced into the FA to further disperse cluster heads and form reasonable clusters. In the inter-cluster routing stage, the inter-cluster routing is established by an improved ant colony optimization (ACO). Considering factors such as the angle, distance and energy of the node, the heuristic function is improved to make the selection of the next hop more targeted. In addition, the coefficient of variation in statistics is introduced into the process of updating pheromones, and the path is optimized by combining energy and distance. In order to further improve the network throughput, a polling control mechanism based on busy/idle nodes is introduced during the intra-cluster communication phase. The simulation experiment results prove that under different application scenarios, EECRAIFA can effectively balance the network energy consumption, extend the network lifetime, and improve network throughput.
With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas. 相似文献
Due to the complexity of blockchain technology, it usually costs too much effort to build, maintain and monitor a blockchain system that supports a targeted application. To this end, the emerging “Blockchain as a Service” (BaaS) makes the blockchain and distributed ledgers more accessible, particularly for businesses, by reducing costs and overheads. BaaS combines the high computing power of cloud computing, the pervasiveness of IoT and the decentralization of blockchain, allowing people to build their own applications while ensuring the transparency and openness of the system. This paper surveys the research outputs of both academia and industry. First, it introduces the representative architectures of BaaS systems and then summarizes the research contributions of BaaS from the technologies for service provision, roles, container and virtualization, interfaces, customization and evaluation. The typical applications of BaaS in both academic and practical domains are also introduced. At present, the research on the blockchain is abundant, but research on BaaS is still in its infancy. Six challenges of BaaS are concluded in this paper for further study directions. 相似文献