Manganese molybdate has garnered considerable interest in supercapacitor research owing to its outstanding electrochemical properties and nanostructural stability but still suffers from the common problems of transition metal oxides not being able to reach the theoretical specific capacitance and lower electrical conductivity. Doping phosphorus elements is an effective approach to further enhance the electrochemical characteristics of transition metal oxides. In this study, MnMoO₄·H₂O nanosheets were synthesized on nickel foam via a hydrothermal route, and the MnMoO₄·H₂O nanosheet structure was successfully doped with a phosphorus element using a gas–solid reaction method. Phosphorus element doping forms phosphorus–metal bonds and oxygen vacancies, thereby increasing the charge storage and conductivity of the electrode material. The specific capacitance value is as high as 2.112 F cm⁻² (1760 F g⁻¹) at 1 mA cm⁻², which is 3.2 times higher than that of the MnMoO₄·H₂O electrode (0.657 F cm⁻²). The P–MnMoO₄//AC ASC device provides a high energy density of 41.9 Wh kg⁻¹ at 666.8 W kg⁻¹, with an 84.5% capacity retention after 10,000 charge/discharge cycles. The outstanding performance suggests that P–MnMoO₄ holds promise as an electrode material for supercapacitors.

As a laser beam passes through a multimode fiber (MMF), a speckle pattern is generated, which is sensitive to temperature, thereby making the MMF a temperature-sensing element. A deep learning technique is employed to the MMF-based temperature sensor, to obtain high-precision temperature sensing. We designed an MMF-based temperature-sensing configuration and developed a dual-output Convolutional Neural Network (CNN) for predicting both the temperature and the position of the heating point, and we constructed a dataset. It was shown that the location prediction accuracy reached 100%, while the temperature prediction accuracy (within a ±1 °C error margin) was 100% and 95.12% in the two experiments, respectively. The precision of the predicting heating point was less than 1 cm. Different types of MMFs were used in temperature measurements, showing that the accuracy remained quite high. This non-contact, high-precision MMF-based temperature measurement method, driven by deep learning, is suitable for applications in hazardous environments.

We theoretically investigated the properties of tightly focused ring Pearcey beams with a cross phase (CPRPB). The expressions of the distributions of both electric field and magnetic field in the focal region of an objective were first derived from the vectorial Debye theory, and then numerical calculations were carried out to obtain the focused intensity distribution and the Poynting vector of CPRPB near the focus. Numerical calculations indicate that as CPRPB is focused on an objective of high numerical aperture (NA), two nonuniform self-focusing spots occur at both sides of the geometrical focus of the objective symmetrically, and the angle between their directions is 90 degrees. The stronger is the strength of cross-phase modulation, the flatter are the ellipses of the self-focusing spots, and the smaller is the intensity at the geometrical focus of the objective. Numerical calculations also demonstrate that the optical gradient force produced by tightly focused CPRPB in the focal region can be manipulated in magnitude and in direction by tuning the strength of cross-phase modulation. Due to these properties of tightly focused CPRPB, they might find applications in the manipulation of micro- and nanoparticles and so on.

Phase imaging normally employs coherent a light source while an incoherent light source is not preferred due to its random wavefront. Another challenge for practical phase imaging is imaging through scattering media, which scatter the photons in a random manner and lead to seriously distorted images of speckles. Based on the convolutional neural network (CNN), this paper presents an approach for phase imaging through scattering media using an incoherent light source. A CNN was trained and utilized to reconstruct the target images from the captured images of speckles. Similarities of over 90% between the reconstructed images and their target images have been achieved. It was concluded that an incoherent light source can be used as an illumination source for scattering phase imaging with the assistance of deep learning technology. This phase imaging approach with an incoherent light source through scattering media can be used to record the refractive indices of transparent samples, which might lead to its application in biomedical imaging.

The tight focusing properties of circular partially coherent radially polarized circular Airy vortex beams (CPCRPCAVBs) are theoretically studied in this paper. After deriving the cross-spectral density matrix of CPCRPCAVBs in the focal region of a high-NA objective, numerical calculations were performed to indicate the influence of the topological charge of the vortex phase on intensity distribution, degree of coherence and degree of polarization of the tightly focused beam. An intensity profile along the propagation axis shows that a super-length optical needle (~15 λ) can be obtained with a topological charge of 1, and a super-length dark channel (~15 λ) is observed with a topological charge of 2 or 3. In the focal plane, the rise in the number of topological charge does not distort the shapes of the coherence distribution pattern and the polarization distribution pattern, but enlarges their sizes.

This paper introduces a machine vision system based on polarization imaging, which is applicable for automatically counting the number of internal layers in plywood. Industrial machine vision usually suffers from a low accuracy due to low contrast and high complexity of the images, which could be overcome by the introduction of polarization imaging. A polarization camera was utilized to capture images with polarization angles of 0°, 45°, 90°, and 135°, and then a degree of polarization (DOP) distribution image was obtained by calculating the DOP for each pixel. Compared with the intensity distribution image, the contrast of the DOP distribution image was increased by about 60% and the excessive information in the image including wood’s natural texture, dirty spots, dicing marks, and artifacts was mostly filtered. A gray value difference algorithm was applied to the images to determine the edges of the internal layers of plywood and count them up automatically. The experimental results illustrated that polarization imaging could improve the counting accuracy of the algorithm effectively.