We present a sugar-templated polydimethylsiloxane (PDMS) sponge for the selective absorption of oil from water. The process for fabricating the PDMS sponge does not require any intricate synthesis processes or equipment and it is not environmentally hazardous, thus promoting potential in environmental applications. The proposed PDMS sponge can be elastically deformed into any shape, and it can be compressed repeatedly in air or liquids without collapsing. Therefore, absorbed oils and organic solvents can be readily removed and reused by simply squeezing the PDMS sponge, enabling excellent recyclability. Furthermore, through appropriately combining various sugar particles, the absorption capacity of the PDMS sponge is favorably optimized.

Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.

Inspired by the human brain, a neuromorphic system combining complementary metal-oxide semiconductor (CMOS) and adjustable synaptic devices may offer new computing paradigms by enabling massive neural-network parallelism. In particular, synaptic devices, which are capable of emulating the functions of biological synapses, are used as the essential building blocks for an information storage and processing system. However, previous synaptic devices based on two-terminal resistive devices remain challenging because of their variability and specific physical mechanisms of resistance change, which lead to a bottleneck in the implementation of a high-density synaptic device network. Here we report that a three-terminal synaptic transistor based on carbon nanotubes can provide reliable synaptic functions that encode relative timing and regulate weight change. In addition, using system-level simulations, the developed synaptic transistor network associated with CMOS circuits can perform unsupervised learning for pattern recognition using a simplified spike-timing-dependent plasticity scheme.

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A silicon nanowire field effect transistor (FET) straddled by the double-gate was demonstrated for biosensor application. The separated double-gates, G1 (primary) and G2 (secondary), allow independent voltage control to modulate channel potential. Therefore, the detection sensitivity was enhanced by the use of G2. By applying weakly positive bias to G2, the sensing window was significantly broadened compared to the case of employing G1 only, which is nominally used in conventional nanowire FET-based biosensors. The charge effect arising from biomolecules was also analyzed. Double-gate nanowire FET can pave the way for an electrically working biosensor without a labeling process.

The demands for transparent, flexible electronic devices are continuously increasing due to their potential applications to the human body. In particular, skin-like, transparent, flexible strain sensors have been developed to realize multifunctional human-machine interfaces. Here, we report a sandwich-like structured strain sensor with excellent optical transparency based on highly purified, solution-processed, 99% metallic CNT-polydimethylsiloxane (PDMS) composite thin films. Our CNT-PDMS composite strain sensors are mechanically compliant, physically robust, and easily fabricated. The fabricated strain sensors exhibit a high optical transparency of over 92% in the visible range with acceptable sensing performances in terms of sensitivity, hysteresis, linearity, and drift. We also found that the sensitivity and linearity of the strain sensors can be controlled by the number of CNT sprays; hence, our sensor can be applied and controlled based on the need of individual applications. Finally, we investigated the detections of human activities and emotions by mounting our transparent strain sensor on various spots of human skins.

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Neuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). Previously investigated synaptic devices can emulate the functionality of biological synapses successfully by utilizing various nano-electronic phenomena; however, the impact of intrinsic synaptic device variability on the system performance has not yet been studied. Here, we perform a device-tosystem level simulation of different synaptic device variation parameters in a designed neuromorphic system that has the potential for unsupervised learning and pattern recognition. The effects of variations in parameters such as the weight modulation nonlinearity (NL), the minimum-maximum weight (G min and G max ), and the weight update margin (ΔG) on the pattern recognition accuracy are analyzed quantitatively. These simulation results can provide guidelines for the continued design and optimization of a synaptic device for realizing a functional large-scale neuromorphic computing system.The mammalian neocortex offers extremely energy-efficient information processing performance in tasks such as pattern recognition with a power consumption of only 10-20 watts 1 . By mimicking both the functional and structural advantages of this biological neural system, the recent development of power-efficient computing systems, i.e., neuromorphic systems (hardware neural networks) 2 , has been expected to offer a promising breakthrough for applications, ranging from mobile platforms 3 to artificial intelligence operations 4 , where power consumption is a concern.A unique feature of neuromorphic systems is efficient parallel data processing, where the processing of information can be performed by modulating the connection strength of synapses (referred to as the synaptic weight) 5 . This synaptic weight can be modulated by either potentiating or depressing neural spikes (pulses) from pre-and post-synaptic neurons, following appropriate learning rules, such as spike-timing-dependent plasticity (STDP) 6 . Therefore, a key element in the neuromorphic system is the implementation of an ideal synaptic device that can emulate the functionality of biological synapses.To date, various nano-electronic devices have successfully reproduced a specific learning rule of biological synapses through their internal analog conductance states that can be modulated intentionally with an applied pulse's timing or level [7][8][9][10][11][12][13][14][15] . Moreover, the potential of ultralow energy consumption per synaptic operation 16 , as well as the possibility of realizing three-dimensional integration 17 , have shown the promising feasibility of large-scale neuromorphic system implementation in the near future. However, the sustainability of such devices is still in dou...