3D microstructure reconstruction and characterization of porous materials using a cross-sectional SEM image and deep learning.

Accurate assessment of the three-dimensional (3D) pore characteristics within porous materials and devices holds significant importance. Compared to high-cost experimental approaches, this study introduces an alternative method: utilizing a generative adversarial network (GAN) to reconstruct a 3D pore microstructure. Unlike some existing GAN models that require 3D images as training data, the proposed model only requires a single cross-sectional image for 3D reconstruction.

Freeze-Casting of Alumina and Permeability Analysis Based on a 3D Microstructure Reconstructed Using Generative Adversarial Networks.

In this study, alumina ceramics with hierarchical pores were successfully fabricated using freeze casting. Experimental studies show that both the solid loading of the slurry and the thermal insulation layer at the interface of the slurry and cooling plate can influence the pore characteristics of cast samples. In order to examine the pore characteristics and evaluate the permeability of the freeze-cast samples fabricated under different conditions, a generative adversarial network (GAN) method was employed to reconstruct the three-dimensional (3D) microstructure from two-dimensional (2D) scanning electron microscopy (SEM) images of the samples.

Nonlinear spectral compression in high-power narrow-linewidth polarization maintaining fiber amplifiers for SBS suppression.

In this paper, we propose a method for narrowing the spectrum in high-power narrow-linewidth polarization-maintaining (PM) fiber amplifiers and investigate its potential for suppressing the stimulated Brillouin scattering (SBS). In this method, in addition to common phase modulation to suppress SBS, precisely designed amplitude modulation is induced to generate self-phase modulation in a high-power PM fiber amplifier. In this co-modulation way, the spectrum can be gradually compressed along the fiber.

Investigating mixing and segregation using discrete element modelling (DEM) in the Freeman FT4 rheometer.

Mixing and segregation in a Freeman FT4 powder rheometer, using binary mixtures with varied particle size ratio and volume fraction, were studied using the Discrete Element Method (DEM). As the blade moves within the particle bed, size induced segregations can occur via a sifting mechanism. A larger particle size ratio and/or a larger volume fraction of large particles lead to a quicker segregation process.