Color-Shifting Iontronic Skin for On-Site, Nonpixelated Pressure Mapping Visualization.

Mimicking the function of human skin is highly desired for electronic skins (e-skins) to perceive the tactile stimuli by both their intensity and spatial location. The common strategy using pixelated pressure sensor arrays and display panels greatly increases the device complexity and compromises the portability of e-skins. Herein, we tackled this challenge by developing a user-interactive iontronic skin that simultaneously achieves electrical pressure sensing and on-site, nonpixelated pressure mapping visualization. By merging the electrochromic and iontronic pressure sensing units into an integrated multilayer device, the interlayer charge transfer is regulated by applied pressure, which induces both color shifting and a capacitance change. The iontronic skin could visualize the trajectory of dynamic forces and reveal both the intensity and spatial information on various human activities. The integration of dual-mode pressure responsivity, together with the scalable fabrication and explicit signal output, makes the iontronic skin highly promising in biosignal monitoring and human-machine interaction.

In Situ Synthesis of Rare-Earth Hybridized Functional Core-Shell Architectures from Microporous Salt Templates and Capacitance-Adsorption Correlation Mechanisms.

Biochar Porous Carbon (BPC) has become a research hotspot in the fields of energy storage, conversion, catalysis, adsorption, and separation engineering. However, the key problem of pore structure liable to collapse has not yet been addressed effectively. Here, an innovative salt ionic coordination modulation technique is reported to synthesize a new core-shell structure of BPC (Dual-doped porous carbonaceous materials, RHPC3@LaYO3) by the asymmetric load of the f orbital ion, which prevents pore structural collapse. The result shows that the novel asymmetric supercapacitors (ASCs) with an excellent energy density (193.11 Wh·kg-1) and capacitance (267.14 F·g-1) by assembling the prepared porous BPC carrier and RHPC3@LaYO3, which surpass the typical supercapacitor. In order to elucidate the association between adsorption and capacitance, the adsorption coexistence equation (MACE) is constructed with the aim of providing a comprehensive explanation for the mechanism of single-multilayer adsorption. Furthermore, a specific linkage mechanism is discovered using adsorption/ desorption properties to validate the pros/cons of capacitive properties. These results demonstrate the potential of renewable biomass materials as ASCs, which can provide new ideas for the construction of an evaluation approach for the performance of future efficient multi-reaction energy storage devices.

Welded Carbon Nanotube-Graphene Hybrids with Tunable Strain Sensing Behavior for Wide-Range Bio-Signal Monitoring.

Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal strategy to prepare welded CNT-graphene hybrids with tunable compositions and a tunable bonding strength between components by the in situ reduction of CNT-graphene oxide (GO) hybrid by thermal annealing. The stiffness of the hybrid film could be tailored by both initial CNT/GO dosage and annealing temperature, through which its electromechanical behaviors could also be defined. The strain sensor based on the CNT-graphene hybrid could be applied to collect epidermal bio-signals by both capturing the faint skin deformation from wrist pulse and recording the large deformations from joint bending, which has great potential in health monitoring, motion sensing and human-machine interfacing.

Protein Complexes Detection Based on Semi-Supervised Network Embedding Model.

A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, the subsets of proteins that are tightly coupled, from it. Network embedding is a technique to learn low-dimensional representations of vertices in networks. It has been proved quite useful for community detection in social networks in recent years. However, unlike social networks, PPI network does not contain rich metadata, so that existing network embedding methods cannot fully capture the network structure of PPI to improve the effect of protein complexes detection significantly. We propose a semi-supervised network embedding model by adopting graph convolutional networks to detect densely connected subgraphs effectively. We compare the performance of our model with state-of-the-art approaches on three popular PPI networks with various data sizes and densities. The experimental results show that our approach significantly outperforms other approaches on all three PPI networks.