Multi-responsive actuators driven by different stimuli (e.g. light, humidity, electricity) have attracted intense attention recently for the advantages of being used in various environments and show enormous actuation. In this work, we propose humidity- and light-driven actuators based on carbon nanotube (CNT)-coated paper and a biaxially oriented polypropylene (BOPP) composite. The CNT-paper/BOPP actuator shows large bending actuation when driven by humidity change (curvature of 1.2 cm-1) and near infrared (NIR) light irradiation (curvature up to 1.6 cm-1). The great actuation performances outperform most other paper-based actuators. Finally, a smart gripper, of which the initial opening width can be enlarged, is fabricated on the basis of the CNT-paper/BOPP actuators. By utilizing the bidirectional bending motion of the actuator, the opening width of the gripper can increase to a width that is 4 times larger than its initial width, so as to grasp a large object. The gripper is also able to raise and move an object that is 20 times heavier than one actuator of the gripper. We assume that this new type of actuator has great potential in artificial muscle, soft robotics and biomimetic applications.

Clarifying the hidden but intrinsic feature of single nanoparticles by nanoelectrochemistry could help understand its potential for diverse applications. The uncontrolled interface and bandwidth limitation in the electrochemical measurement put the obstacle in single particle collision. Here, we demonstrate a well-defined 30 nm nanopore electrode with a rapid chemical-electrochemical fabrication method which provides a high reproducibility in both size and performance. A capacitance-based detection mechanism is demonstrated to achieve a high current resolution of 0.6 pA ±0.1 pA (RMS) and a high the temporal resolution of 0.01 ms. By utilizing this electrode, the dynamic interactions of every single particle in the mixture could be directly read during the collision process. The collision frequency is two orders of magnitude higher than previous reports, which helps reveal the hidden features of nanoparticles during the complex and multidimensional interaction processes.

Here, we show a designed solid-state nanopore sensor for the direct sensing and quantification of prostate-specific antigen (PSA) as cancer biomarker in serum without any pretreatment. This nanopore technique provides a convenient, fast, and low-cost quantification of cancer biomarkers in clinical samples.

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Herein, the unzipping and translocation of DNA duplexes through a sub-2 nm silicon nitride (SiN) solid-state nanopore have been demonstrated by well-resolved three-level blockades. In order to examine our observations, we applied a simple model which is applicable to the unzipping and translocation processes of DNA duplexes through solid-state nanopores. The generation of these highly recognizable signatures is an important step towards the real applications of solid-state nanopores in complex samples.

Light-regulated ion channel-transport across lipid bilayers was realized using structurally simple azobenzene-based amphiphilic small molecules. UV or visible irradiation triggers molecular photoisomerization, which induces structural and membrane affinity changes in self-assembled channels, thus resulting in light-regulated ion transmembrane transport.

Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

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