Metal organic framework (MOF)-derived nanoporous carbons (NPCs) have been proposed as promising electrode materials for energy storage and conversion devices. However, MOF-derived NPCs typically suffer from poor electrical conductivity due to the lack of connectivity between these particles and a micropore-dominated storage mechanism, which hinder mass and electron transfer, thereby leading to poor electrochemical performance. In recent years, one-dimensional (1D) MOF-derived carbon nanostructures obtained using an electrospinning method have emerged as promising materials for both electrochemical energy storage (EES) and energy conversion applications. In this mini review, the recent progress in the development of MOF-derived 1D porous or hollow carbon nanofibers using the electrospinning method and their application in energy storage (e.g., supercapacitors and rechargeable batteries) and conversion devices (e.g., fuel cells) is presented. The synthetic method, formation mechanism and the structure–activity relationship of such porous or hollow carbon nanofibers are also discussed in detail. Finally, future perspectives on the development of electrospun MOF-derived carbon nanomaterials for energy storage and conversion applications are provided. This review will provide some guidance for future derivations of 1D hollow carbon nanomaterials from MOFs using electrospinning technology.

Federated learning emerges as a solution to the dilemma of data silos while safeguarding data privacy, particularly relevant in the consumer electronics sector where user data privacy is paramount. However, federated learning is generally employed in a heterogeneous scenario, consisting of various factors that influence the training efficiency and accuracy of the federated learning models. There are many classic references focusing on federated communications, federated robustness and federated fairness, conversely, few of them clarify and summary systematically the influence of heterogeneity on the effect of federated learning. Therefore, we provide an overview of three heterogeneous challenges faced by federated learning in practical applications: device heterogeneity, data heterogeneity and model heterogeneity, and analyze their influence on federated learning. This is especially crucial in consumer electronics, where heterogeneity directly influence the performance and user experience of AI-driven features. And then, we highlight current solutions, ideas and challenges to compare different strategies for facing heterogeneous problems and outline several directions of future work that are relevant to a wide range of research communities.

Constructing MOF‐on‐MOF heterojunction with elaborate charge transfer mechanism and interface is a promising strategy for improving the photocatalytic properties of MOFs. Herein, a Step‐scheme (S‐scheme) MIL‐125‐NH₂@CoFe Prussian blue analogue (PBA) heterojunction is reported for the first time. The MOF‐on‐MOF heterostructure exhibits a sandwich‐like morphology with hollow CoFe PBA nanocages selectively assembled on the top‐down surfaces of MIL‐125‐NH₂ nanocakes. Experimental findings and theoretical simulation results reveal the formation of internal electric field via interfacial TiOCo bonds at the heterojunction, providing driving force and atomic transportation highway for accelerating the S‐scheme charge transfer and enhancing the redox performance. Contributed further by the hollow sandwich‐like structures with increased active site exposure, the designed MOF‐on‐MOF heterojunction exhibits significantly enhanced photocatalytic activity for degradation of various organic pollutants. This study provides insights toward the rational design of semiconducting MOF‐based heterojunctions with improved properties.

A novel N-doped MOF-based hierarchical carbon fiber (NPCF) towards supercapacitors was prepared by the pyrolysis of MOF nanofibers.