All-component 3D-printed lithium-ion batteries are fabricated by printing graphene-oxide-based composite inks and solid-state gel polymer electrolyte. An entirely 3D-printed full cell features a high electrode mass loading of 18 mg cm(-2) , which is normalized to the overall area of the battery. This all-component printing can be extended to the fabrication of multidimensional/multiscale complex-structures of more energy-storage devices.

Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL).LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on largescale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using subgraph training strategy are more efficient as compared to prior approaches.

Transient technology is an emerging field that requires materials, devices, and systems to be capable of disappearing with minimal or non-traceable remains over a period of stable operation. Electronics with the capability of disintegrating or vanishing after stable operation are becoming an interesting research topic and have attracted increasing attentions. In recent years, transience technology has been extended to intelligence applications, bioelectronics and environmental monitoring systems, and energy harvesters and storage. Although the transient concept has only a few years of development, this emerging transient technology is believed to find more opportunities in the fast development of advanced electronics. In this review, we will examine recent progress in the development of transient electronics. First, an overview of various transient materials, including metals, polymers, and semiconductor materials, is described. Second, recent progress in the design and development of transient electronics is reviewed. Third, transient energy storage, focusing on primary batteries and secondary batteries, is explored. We end the review with a conclusion and outlook, pointing out further designs and developments of transient technology based on transient materials towards high-performance evanescent electronics and energy storage.

Transient battery is a new type of technology that allows the battery to disappear by an external trigger at any time. In this work, we successfully demonstrated the first transient rechargeable batteries based on dissoluble electrodes including V2O5 as the cathode and lithium metal as the anode as well as a biodegradable separator and battery encasement (PVP and sodium alginate, respectively). All the components are robust in a traditional lithium-ion battery (LIB) organic electrolyte and disappear in water completely within minutes due to triggered cascade reactions. With a simple cut-and-stack method, we designed a fully transient device with an area of 0.5 cm by 1 cm and total energy of 0.1 J. A shadow-mask technique was used to demonstrate the miniature device, which is compatible with transient electronics manufacturing. The materials, fabrication methods, and integration strategy discussed will be of interest for future developments in transient, self-powered electronics. The demonstration of a miniature Li battery shows the feasibility toward system integration for all transient electronics.

Wood is one of the most abundant, sustainable, and aesthetically pleasing structural materials and is commonly used in building and furniture construction. Unfortunately, the fire hazard of wood is a major safety concern for its practical applications. Herein, an effective and environmentally friendly method is demonstrated to substantially improve the fire-retardant properties of wood materials by delignification and densification. The densification process eliminates the spaces between the cell walls, leading to a highly compact laminated structure that can block oxygen from infiltrating the material. In addition, an insulating wood char layer self-formed during the burning process obstructs the transport of heat and oxygen diffusion. These synergistic effects contribute to the material's excellent fire-retardant and self-extinguished properties, including a 2.08-fold enhancement in ignition time (t ig ) and 34.6% decrease in maximum heat release rate. Meanwhile, the densified wood shows a more than 82-fold enhancement in compressive strength compared with natural wood after exposure to flame for 90 s, which could effectively prevent the collapse and destruction of wooden structures, and gain precious rescue time when a fire occurs. The facile top-down chemical delignification and densification process enabling both substantially enhances fire-retardant performance and mechanical robustness represents a promising direction toward fire-retardant and high-strength structural materials.

Sugar transport across tonoplasts is essential for maintaining cellular sugar homeostasis and metabolic balance in plant cells. It remains unclear, however, how this process is regulated among different classes of sugar transporters. Here, we identified a tonoplast H+/glucose symporter, MdERDL6-1, from apples, which was highly expressed in fruits and exhibited expression patterns similar to those of the tonoplast H+/sugar antiporters MdTST1 and MdTST2. Overexpression ofMdERDL6-1unexpectedly increased not only glucose (Glc) concentration but also that of fructose (Fru) and sucrose (Suc) in transgenic apple and tomato leaves and fruits. RNA sequencing (RNA-seq) and expression analyses showed an up-regulation ofTST1andTST2in the transgenic apple and tomato lines overexpressingMdERDL6-1. Further studies established that the increased sugar concentration in the transgenic lines correlated with up-regulation ofTST1andTST2expression. Suppression or knockout ofSlTST1andSlTST2in theMdERDL6-1–overexpressed tomato background reduced or abolished the positive effect ofMdERDL6-1on sugar accumulation, respectively. The findings demonstrate a regulation ofTST1andTST2byMdERDL6-1, in which Glc exported by MdERDL6-1 from vacuole up-regulatesTST1andTST2to import sugars from cytosol to vacuole for accumulation to high concentrations. The results provide insight into the regulatory mechanism of sugar accumulation in vacuoles mediated by the coordinated action of two classes of tonoplast sugar transporters.

Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and effectiveness. In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation. These blocks can be inserted into U-Net as size-preserving processes, as well as down-sampling and up-sampling layers. We perform thorough experiments on the 3D multimodality isointense infant brain MR image segmentation task to evaluate the non-local U-Nets. Results show that our proposed models achieve top performances with fewer parameters and faster computation.

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