Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
Abstract Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of...
Enregistré dans:
Auteurs principaux: | Hiroyuki Aoki, Yuwei Liu, Takashi Yamashita |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/d168e5a353f1460c89ea1c0ecd10bf71 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Towards a neutron and X-ray reflectometry environment for the study of solid–liquid interfaces under shear
par: Alexander J. Armstrong, et autres
Publié: (2021) -
Lipid bilayer degradation induced by SARS-CoV-2 spike protein as revealed by neutron reflectometry
par: Alessandra Luchini, et autres
Publié: (2021) -
Neutron reflectometry and NMR spectroscopy of full-length Bcl-2 protein reveal its membrane localization and conformation
par: Ameeq Ul Mushtaq, et autres
Publié: (2021) -
Optomechanical time-domain reflectometry
par: Gil Bashan, et autres
Publié: (2018) -
Deep Learning Approaches for Impulse Noise Mitigation and Classification in NOMA-Based Systems
par: Muhammad Hussain, et autres
Publié: (2021)