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Traditional numerical reconstruction methods in digital holography (DH) are faced with problems such as inaccurate and time-consuming unwrapping or the need to capture multiple holograms with different diffraction distances. In recent years, deep learning, believed to be a new and effective optimization tool, has been widely used in digital holography. However, most supervised deep learning methods require large-scale paired data, and their preparation is time-consuming and laborious.
View Article and Find Full Text PDFUV-Resistant Nanostructured Anti-reflective Film for Achieving Efficiency Enhancement of Perovskite Solar Cells and Potential of Fabricating Large-Scale Cu(In, Ga)Se Solar Cells.
ACS Appl Mater Interfaces
January 2025
Guangxi Key Laboratory of Optical and Electronic Material and Devices, School of Materials Science and Engineering, Guilin University of Technology, 12 Jiangan Road, Guilin, Guangxi 541004, China.
Sticker-type transparent antireflective film (STAF) is applied to perovskite solar cells (PSCs) to reduce the reflection and improve the light-trapping ability of PSCs. However, the development of STAF is hindered by many factors, such as expensive materials, low actual service life, unsatisfactory antireflective effect, and a lack of research on stability. This work proposes an ultraviolet (UV)-resistant enhanced sticker-type nanostructure acrylic resin antireflective film (SNAAF), which is applied to the incident surface of PSCs.
View Article and Find Full Text PDFBioinspired Conductivity-Enhanced, Self-Healing, and Renewable Silk Fibroin Hydrogel for Wearable Sensors with High Sensitivity.
ACS Appl Mater Interfaces
January 2025
Hebei Provincial Key Laboratory of Photoelectric Control on Surface and Interface, and College of Science, Hebei University of Science and Technology, Yuxiang Road 26, Shijiazhuang 050080, PR China.
The development of silk fibroin-based hydrogels with excellent biocompatibility, aqueous processability, and facile controllability in structure is indeed an exciting advancement for biological research and strain sensor applications. However, silk fibroin-based hydrogel strain sensors that combine high conductivity, high stretchability, reusability, and high selectivity are still desired. Herein, we report a simple method for preparing double-network hydrogels including silk fibroin and poly(acrylic acid) sodium-polyacrylate (PAA-PAAS) networks.
View Article and Find Full Text PDFRoom-Temperature Selective Detection of H by Pd Nanoparticle-Decorated SnO@WO Core-Shell Hollow Structures.
Anal Chem
January 2025
School of Metallurgy, Northeastern University, Shenyang 110819, China.
Sensitive H sensors play key roles in the large-scale and safe applications of H. In this study, we developed novel ternary Pd-loaded SnO@WO core-shell structures by hydrothermal and reduction methods. The compositions of the optimized ternary core-shell structures (Pd-SW-2) are prepared on the basis of the optimal binary core-shell structures (SW-X) according to the sensing performances to H.
View Article and Find Full Text PDFDeep-Learning-Enabled Fast Raman Identification of the Twist Angle of Bi-Layer Graphene.
Small
January 2025
College of Physical Science and Technology, Xiamen University, Xiamen, 361005, P. R. China.
Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle-dependent electrical, optical, and mechanical properties, yet preparing and identifying samples at specific angles on a large scale remains challenging and labor-intensive. Here, a data-driven strategy that leverages Raman spectroscopy is proposed in combination with deep learning to rapidly and non-destructively decode and predict the twist angle of TBG across the full angular range. By processing high-dimensional Raman data, the deep learning model extracts hidden information to achieve precise twist angle identification.
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