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.