The Effect of Vibratory Grinding Time on Moisture Sorption, Particle Size Distribution, and Phenolic Bioaccessibility of Carob Powder. [PDF]
Červenka L +4 more
europepmc +1 more source
Dislocation cutting of γ′ precipitates in Ni‐based superalloys is investigated by linking atomistic simulations with discrete dislocation dynamics. The critical cutting stress is shown to be governed by the antiphase boundary energy, while line tension effects promote edge‐preferred cutting.
Frédéric Houllé +9 more
wiley +1 more source
Nationwide Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in Indoor Dust Across China: Pollution Characteristics, Sources, and Particle Size Distribution. [PDF]
Tian MH +6 more
europepmc +1 more source
Atmospheric aerosol particle size distribution from Lidar data based on the lognormal distribution mode. [PDF]
Shi Y +6 more
europepmc +1 more source
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi +3 more
wiley +1 more source
Uni-Axial Densification of Slurry-Casted Li₆PS₅Cl Tapes: The Role of Particle Size Distribution and Densification Pressure. [PDF]
Tran QA +14 more
europepmc +1 more source
This work studies the anisotropic behavior of circular notched tensile specimens of Ni‐base superalloy single crystals during high‐temperature tensile creep along [001], [110], and [111]. Correlative scale‐bridging imaging of specimens reveals early rupture along [110] because 1) plastic deformation proceeds faster at notch center; 2) more (brittle ...
Leonardo Agudo Jácome +6 more
wiley +1 more source
Crystallization and particle size distribution of hydrothermally synthesized SAPO-34: an experimental and population balance study. [PDF]
Verki MT +3 more
europepmc +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source

