Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Home2025September 2025AI-/ML-based hybrid modeling for enhanced resid fluid catalytic cracking optimization September 2025 IP: 173.255.226.241 This article discusses the development and deployment of an artificial intelligence (AI)-/machine-learning (ML)-based hybrid model to improve prediction accuracy in resid fluid catalytic cracking (RFCC) processes.
The structural and electronic transformations of liquid silane as a function of pressure are studied using first-principles molecular dynamics simulations. We present results for two isotherms, 1000 K and 2000 K, in the pressure range from ambient up to 265 GPa. The phases of dense liquid silane bear some similarities with its crystalline phases but there are significant differences as well.
Abstract We report the nature of magnetic structure, and microscopic spin-spin correlations and their dependence on the underlying crystal structure of the geometrically frustrated layered spin-32 maple leaf lattice (MLL) antiferromagnet Na2Mn3O7 by a comprehensive neutron diffraction study. Crystal structural studies by x-ray and neutron diffraction reveal that the MLL layers (constituted by Mn3O72− units) are well separated by nonmagnetic Na layers.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
