Developing green materials applied in lithium-ion batteries is of significant importance for the present-day society. Herein, a feasible strategy to construct Fe ₃ O ₄ nanoparticles (NPs) embedded in three-dimensional (3D) honeycomb biochar derived from pleurotus eryngii was proposed. The obtained material consists of Fe ₃ O ₄ NPs (35–85 nm) encapsulated in 3D honeycomb biochar possesses a high specific capacity of 723 mAh g ⁻¹ at 1.5 A g ⁻¹ after 1000 cycles. The effectively enhanced cycling life of Fe ₃ O ₄ @C nanocomposites can be ascribed to the small Fe ₃ O ₄ NPs provide lower degree of cracking and high specific capacity, while the honeycomb biochar function like a cage to inhibit huge volume change of Fe ₃ O ₄ NPs during the charge–discharge process.

Text embedded in images provides important semantic information about a scene and its content. Detecting text in an unconstrained environment is a challenging task because of the many fonts, sizes, backgrounds, and alignments of the characters. We present a novel attention model for detecting arbitrary oriented and curved scene text. Inspired by the attention mechanisms in the human visual system, our model utilizes a spatial glimpse network to processes the attended area and deploys a recurrent neural network that aggregates the information over time to determine the attention movement. Combining this with an off-the-shelf region proposal method, the model achieves the state-of-the-art performance on the highly cited ICDAR2013 dataset, and the MSRA-TD500 dataset which contains arbitrary oriented text.