Stretchable conductors are essential components in next-generation deformable and wearable electronic devices. The ability of stretchable conductors to achieve sufficient electrical conductivity, however, remains limited under high strain, which is particularly detrimental for charge storage devices. In this study, we present stretchable conductors made from multiple layers of gradient assembled polyurethane (GAP) comprising gold nanoparticles capable of self-assembly under strain. Stratified layering affords control over the composite internal architecture at multiple scales, leading to metallic conductivity in both the lateral and transversal directions under strains of as high as 300%. The unique combination of the electrical and mechanical properties of GAP electrodes enables the development of a stretchable lithium-ion battery with a charge-discharge rate capability of 100 mAh g−1 at a current density of 0.5 A g−1 and remarkable cycle retention of 96% after 1000 cycles. The hierarchical GAP nanocomposites afford rapid fabrication of advanced charge storage devices.

Printing is one of the easy and quick ways to make a stretchable wearable electronics. Conventional printing methods deposit conductive materials "on" or "inside" a rubber substrate. The conductors made by such printing methods cannot be used as device electrodes because of the large surface topology, poor stretchability, or weak adhesion between the substrate and the conducting material. Here, a method is presented by which conductive materials are printed in the way of being surface-embedded in the rubber substrate; hence, the conductors can be widely used as device electrodes and circuits. The printing process involves a direct printing of a metal precursor solution in a block-copolymer rubber substrate and chemical reduction of the precursor into metal nanoparticles. The electrical conductivity and sensitivity to the mechanical deformation can be controlled by adjusting the number of printing operations. The fabrication of highly sensitive vibration sensors is thus presented, which can detect weak pulses and sound waves. In addition, this work takes advantage of the viscoelasticity of the composite conductor to fabricate highly conductive stretchable circuits for complicated 3D structures. The printed electrodes are also used to fabricate a stretchable electrochemiluminescence display.

Soft energy storage devices, such as supercapacitors, are an essential component for powering integrated soft microsystems. However, conventional supercapacitors are mainly manufactured using hard/brittle materials that easily crack and eventually delaminate from the current collector by mechanical deformation. Therefore, to realize all-soft supercapacitors, the electrodes should be soft, stretchable, and highly conductive without compromising the electrochemical performance. This paper presents all-soft supercapacitors for integrated soft microsystems based on gallium–indium liquid metal (eutectic gallium–indium alloy, EGaIn) electrodes with integrated functionalized carbon nanotubes (CNTs). Oxygen functional groups on the surface of the CNTs ensure strong adhesion between the functionalized CNTs and the thin native oxide layer on the surface of EGaIn, which enables delamination-free soft and stretchable electrodes even under mechanical deformation. The electrochemical performances of fabricated all-soft supercapacitors in a parallel-plate arrangement were investigated without and with applied mechanical deformation. The fabricated supercapacitors exhibit areal capacitances as high as 12.4 mF cm–2 and show nearly unchanged performance under 30% applied strain. They maintain >95% of their original capacitance after >4200 charging and discharging cycles with a periodic applied strain of 30%. Finally, fabricated supercapacitors have been successfully integrated with a commercial light-emitting diode to demonstrate an integrated soft microsystem.

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources to process information; this demand for additional resources may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) employing a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. The 1D EEG signals are converted to 3D EEG images to enable the 3D CNN to learn the spectral and spatial information over the scalp. The multilevel feature fusion framework integrates local and global neuronal activities by workload tasks in the 3D CNN algorithm. Multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The weighting factor is adaptively estimated for each EEG image by a backpropagation process. Furthermore, we generate subframes from each EEG image and propose a temporal attention technique based on the long short-term memory model (LSTM) to extract a significant subframe at each multilevel feature that is strongly correlated with task difficulty. To verify the performance of our network, we performed the Sternberg task to measure the mental workload of the participant, which was classified according to its difficulty as low or high workload condition. We showed that the difficulty of the workload was well designed, which was reflected in the behavior of the participant. Our network is trained on this dataset and the accuracy of our network is 90.8 %, which is better than that of conventional algorithms. We also evaluated our method using the public EEG dataset and achieved 93.9 % accuracy. INDEX TERMS Convolutional neural network, electroencephalogram (EEG), feature fusion, mental workload, working memory.

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