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 …
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 …
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
C Févotte, N Dobigeon - IEEE Transactions on Image …, 2015 - ieeexplore.ieee.org
We introduce a robust mixing model to describe hyperspectral data resulting from the
mixture of several pure spectral signatures. The new model extends the commonly used …
mixture of several pure spectral signatures. The new model extends the commonly used …
Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization
Advances in scanning transmission electron microscopy (STEM) techniques have enabled
us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) …
us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) …
Big, deep, and smart data in scanning probe microscopy
Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and
nanotechnology by enabling imaging and manipulation of the structure and functionality of …
nanotechnology by enabling imaging and manipulation of the structure and functionality of …
Automated experiments of local non‐linear behavior in ferroelectric materials
An automated experiment in multimodal imaging to probe structural, chemical, and
functional behaviors in complex materials and elucidate the dominant physical mechanisms …
functional behaviors in complex materials and elucidate the dominant physical mechanisms …
Characterising degradation of perovskite solar cells through in-situ and operando electron microscopy
FU Kosasih, C Ducati - Nano Energy, 2018 - Elsevier
Organic-inorganic hybrid perovskite solar cells have exhibited power conversion efficiencies
comparable to more established PV technologies thanks to their favourable optoelectronic …
comparable to more established PV technologies thanks to their favourable optoelectronic …
Nanosized conducting filaments formed by atomic-scale defects in redox-based resistive switching memories
Redox-based resistive switching phenomena are found in many metal oxides and hold great
promise for applications in next-generation memories and neuromorphic computing …
promise for applications in next-generation memories and neuromorphic computing …
Atomic-Scale Insights into Nickel Exsolution on LaNiO3 Catalysts via In Situ Electron Microscopy
Using a combination of in situ bulk and surface characterization techniques, we provide
atomic-scale insight into the complex surface and bulk dynamics of a LaNiO3 perovskite …
atomic-scale insight into the complex surface and bulk dynamics of a LaNiO3 perovskite …