Machine learning in scanning transmission electron microscopy
Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful
tool for structural and functional imaging of materials on the atomic level. Driven by …
tool for structural and functional imaging of materials on the atomic level. Driven by …
Machine learning for nanoplasmonics
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling
the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical …
the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical …
Atomically dispersed iron sites with a nitrogen–carbon coating as highly active and durable oxygen reduction catalysts for fuel cells
Nitrogen-coordinated single atom iron sites (FeN4) embedded in carbon (Fe–N–C) are the
most active platinum group metal-free oxygen reduction catalysts for proton-exchange …
most active platinum group metal-free oxygen reduction catalysts for proton-exchange …
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy
Over the past several decades, electron and scanning probe microscopes have become
critical components of condensed matter physics, materials science and chemistry research …
critical components of condensed matter physics, materials science and chemistry research …
Experimental discovery of structure–property relationships in ferroelectric materials via active learning
Emergent functionalities of structural and topological defects in ferroelectric materials
underpin an extremely broad spectrum of applications ranging from domain wall electronics …
underpin an extremely broad spectrum of applications ranging from domain wall electronics …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Interface aspects in all‐solid‐state Li‐based batteries reviewed
Extensive efforts have been made to improve the Li‐ionic conductivity of solid electrolytes
(SE) for developing promising all‐solid‐state Li‐based batteries (ASSB). Recent studies …
(SE) for developing promising all‐solid‐state Li‐based batteries (ASSB). Recent studies …
[图书][B] Electron energy-loss spectroscopy in the electron microscope
RF Egerton - 2011 - books.google.com
Within the last 30 years, electron energy-loss spectroscopy (EELS) has become a standard
analytical technique used in the transmission electron microscope to extract chemical and …
analytical technique used in the transmission electron microscope to extract chemical and …
Electron energy-loss spectroscopy in the TEM
RF Egerton - Reports on Progress in Physics, 2008 - iopscience.iop.org
Electron energy-loss spectroscopy (EELS) is an analytical technique that measures the
change in kinetic energy of electrons after they have interacted with a specimen. When …
change in kinetic energy of electrons after they have interacted with a specimen. When …
Automated and autonomous experiments in electron and scanning probe microscopy
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …
part of physics research, with domain applications ranging from theory and materials …