Two-dimensional material has been widely investigated for potential applications in sensor and flexible electronics. In this work, a self-powered flexible humidity sensing device based on poly(vinyl alcohol)/Ti₃C₂T_x (PVA/MXene) nanofibers film and monolayer molybdenum diselenide (MoSe₂) piezoelectric nanogenerator (PENG) was reported for the first time. The monolayer MoSe₂-based PENG was fabricated by atmospheric pressure chemical vapor deposition techniques, which can generate a peak output of 35 mV and a power density of 42 mW m⁻². The flexible PENG integrated on polyethylene terephthalate (PET) substrate can harvest energy generated by different parts of human body and exhibit great application prospects in wearable devices. The electrospinned PVA/MXene nanofiber-based humidity sensor with flexible PET substrate under the driven of monolayer MoSe₂ PENG, shows high response of ∼40, fast response/recovery time of 0.9/6.3 s, low hysteresis of 1.8% and excellent repeatability. The self-powered flexible humidity sensor yields the capability of detecting human skin moisture and ambient humidity. This work provides a pathway to explore the high-performance humidity sensor integrated with PENG for the self-powered flexible electronic devices.

An Ag-graphene layers-coated H-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor with a U-shaped grooves open structure for refractive index (RI) sensing is proposed and numerically simulated by the finite element method (FEM). The designed sensor could solve the problems of air-holes material coating and analyte filling in PCF. Two big air-holes in the x-axis produce a birefringence phenomenon leading to the confinement loss and sensitivity of x-polarized light being much stronger than y-polarized. Graphene is deposited on the layer of silver in the grooves; its high surface to volume ratio and rich π conjugation make it a suitable dielectric layer for sensing. The effect of structure parameters such as air-holes size, U-shaped grooves depth, thickness of the silver layer and number of graphene layers on the sensing performance of the proposed sensor are numerical simulated. A large analyte RI range from 1.33 to 1.41 is calculated and the highest wavelength sensitivity is 12,600 nm/RIU. In the linear RI sensing region of 1.33 to 1.36; the average wavelength sensitivity we obtained can reach 2770 nm/RIU with a resolution of 3.61 × 10⁻⁵ RIU. This work provides a reference for developing a high-sensitivity; multi-parameter measurement sensor potentially useful for water pollution monitoring and biosensing in the future.

The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

Hepatocellular carcinoma (HCC) is a harmful malady that truly debilitates human health, and hence it is of significance to isolate and on-line profile the phenotype of HCC cells for further diagnosis and therapy. We developed a novel strategy for efficient capture and in situ heterogeneous phenotype analysis of circulating tumor cells (CTCs) at the single-cell level based on surface-enhanced Raman scattering (SERS) fingerprint characteristics. Herein, a new microfluidic chip with lantern-like bypass structure was designed to capture CTCs by their large size from whole blood. Furthermore, two types of SERS-aptamer nanotags were fabricated, realizing spectral recognition of single CTCs in accordance with the surface membrane protein expression. Up to 84% of CTCs with a purity of 95% were captured from whole blood samples using the present SERS-aptamer based microfluidic chip at 20 μL min⁻¹. The results showed that the proposed strategy can successfully identify HCC cell subtypes by SERS measurements, which was related to the clinical surface biomarkers. This may open a new avenue for serving as a powerful tool of cancer diagnosis and prognosis evaluation.