MXenes, a member of 2D inorganic compounds that contain few-atom-thick layers of transition metal carbides, nitrides, and polar surface functional groups, are extraordinary materials for many applications including stimuliresponsive actuators. Here, an extensive review on MXene-based actuators in comparison with other 2D materials-based actuators is reported, highlighting the main differences in view of chemical structure, mechanical properties, and electrical functionalities. First, since MXenes are newcomers in the field of actuators, their properties are explained including cation and ionic liquid intercalation, high capacitance, good electrical and thermal conductivity, excellent electromagnetic wave absorption, hydrophilicity, and outstanding dispersion in many polar solvents. Second, electro-ionic, electrochemical, electrothermal, photothermal, and humidity-responsive MXene-based actuators are comprehensively addressed with detailed actuation mechanisms, focusing on electro-ionic soft actuators. Third, several applications of those actuators are summarized with an emphasis on soft robotics and future directions of MXene-based actuators are suggested.

Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidencebased learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017. 1

In the field of bioinspired soft robotics, to accomplish sophisticated tasks in human fingers, electroactive artificial muscles are under development. However, most existing actuators show a lack of high bending displacement and irregular response characteristics under low input voltages. Here, based on metal free covalent triazine frameworks (CTFs), we report an electro-ionic soft actuator that shows high bending deformation under ultralow input voltages that can be implemented as a soft robotic touch finger on fragile displays. The as-synthesized CTFs, derived from a polymer of intrinsic microporosity (PIM-1), were combined with poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) to make a flexible electrode for a high-performance electro-ionic soft actuator. The proposed soft touch finger showed high peak-to-peak displacement of 17.0 mm under ultralow square voltage of ±0.5 V, with 0.1 Hz frequency and 4 times reduced phase delay in harmonic response compared with that of a pure PEDOT-PSS-based actuator. The significant actuation performance is mainly due to the unique physical and chemical configurations of CTFs electrode with highly porous and electrically conjugated networks. On a fragile display, the developed soft robotic touch finger array was successfully used to perform soft touching, similar to that of a real human finger; device was used to accomplish a precise task, playing electronic piano.

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The APS Journal Legacy Content is the corpus of 100 years of historical scientific research from the American Physiological Society research journals. This package goes back to the first issue of each of the APS journals including the American Journal of Physiology, first published in 1898. The full text scanned images of the printed pages are easily searchable. Downloads quickly in PDF format.