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Nanostructured polymeric dielectric composites, based on poly (vinylidene fluoride) (PVDF), graphene oxide (GO), and an ionic liquid (IL), 1-vinyl-3-ethylimidazolium tetrafluoroborate ([VEIM] [BF 4 ]), denoted as Nano-PVDF/IL/GO, were fabricated through casting method, electron beam (EB) irradiation and microphase separation processes. Through the strategy, GO nanosheets were dispersed homogeneously in PVDF matrix and the organic nanoclusters formed by microphase separation of IL grafted PVDF (PVDF- g -IL) chain segments tended to adhere onto the surface of GO nanosheets. The Nano-PVDF/IL/GO composites displayed a drastic reduction in dc conductivity compared with PVDF/GO and PVDF/IL/GO composites because of impeding the direct contact between GO nanosheets by the nanoclusters. Furthermore, the mircocapacitors between the adjacent GO nanosheets and nanoclusters contributed the increase of dielectric constant. At the same time, the dielectric loss from the current leakage of GO nanosheets was almost fully eliminated due to the strong interfacial bonding between polymer matrix and fillers which restrained the formation of conductive network. Therefore, we proposed an effective strategy for using the organic nanoclusters to modify the inorganic conductive fillers and impeding the formation of conductive network in the dielectric materials, which paves new possibility to prepare polymer nanocomposites with both high dielectric constant and low dielectric loss. [ABSTRACT FROM AUTHOR]

Tactile sensors can enable a robotic manipulator to identify the object in contact. However, due to the dynamics and diversity of target objects, as well as the complexity of real environment, accurate recognition of objects by existing tactile sensors has been very challenging. This paper proposes a hybrid tactile sensor that integrates a triboelectric active sensing unit with an electromagnetic inductance transducer. The triboelectric signal relates strongly to the specific charge condition of the surface material of a target object, while the inductive signal manifests the electromagnetic characteristics at a certain depth inside the object. With the help of machine learning, the triboelectric signals and inductive signals can be used for object identification. We demonstrate a robotic gripper with random operation settings can recognize eight different fruits with an accuracy as high as 98.75%. Furthermore, the hybrid sensor can recognize objects packaged in different ways. The recognition accuracy of four different fruits in three different packages can reach 95.93%. This study demonstrates the potential of hybrid tactile sensor to improve the artificial intelligence of robots, in particular their ability to distinguish objects in complex settings and sorting them effectively. [Display omitted] • A hybrid tactile sensor was developed based on the effects of triboelectrification and electromagnetic induction. • An object recognition strategy was exploit based on this hybrid tactile sensor with the aid of machine learning. • As a demonstration, the recognition accuracy among several kinds of fruits with different packages reaches over 95%. [ABSTRACT FROM AUTHOR]

We propose a novel vortex-induced vibration (VIV) based triboelectric nanogenerator (TENG) that makes it possible for efficient energy harvesting from wind at low speed. A theoretical model for VIV-TENG is constructed to investigate the vibration response and output voltage varying with wind speed, which is compared with and validated by experiments. Results show that there is a lock-in region where the vibration amplitude and output voltage are large. When the wind speed is 2.78 m/s, the average power is achieved to be 392.72 μW and the average power density is 96.79 mW/m2, which are much higher than those reported in previous studies. In order to broaden the lock-in region, we further design a new model of tandem vortex-induced vibration triboelectric nanogenerators. The resonance wind speed region is remarkably widened due to interactions between two cylinders subjected to VIV. The demonstration of continuously powering wireless sensors is performed to show the practicability of the designed VIV-TENG. Overall, the present study enables applications of TENG for realizing self-powered wireless sensing under low wind speed environments. [Display omitted] • A novel VIV-TENG is proposed for efficient energy harvesting from wind at low speed. • The average power of 392.72 μW and the average power density of 96.79 mW/m2 are obtained at a wind speed of 2.78 m/s. • The resonance region is broadened to be between 2m/s and 7m/s with a further design of T-VIV-TENG. • The VIV-TENG is applied to drive wireless sensors at low wind speeds, realizing the self-powered wireless sensing system. [ABSTRACT FROM AUTHOR]

Abstract: The eco-friendly poly(propylene carbonate) (PPC)/cellulose acetate butyrate (CAB) blends were prepared by melt-blending in a batch mixer for the first time. PPC and CAB were partially miscible because of the drastically shifted glass transition temperatures of both PPC and CAB, which originated from the specific interactions between carbonyl groups and hydroxyl groups. The incorporation of CAB into PPC matrix enhanced not only tensile strength and modulus of PPC dramatically, but also improved heat resistance and thermal stability of PPC significantly. The tensile strength and the modulus of PPC/CAB=50/50 blend are 27.7MPa and 1.24GPa, which are 21 times and 28 times higher than those of the unmodified PPC, respectively. Moreover, the elongation at break of PPC/CAB=50/50 blend is as high as 117%. In addition, the obtained blends exhibited good transparency, which is very important for the package materials. The results in this work pave new possibility for the massive application of eco-friendly polymer materials. [Copyright &y& Elsevier]