Super resolution correlative far-field submicron simultaneous IR and raman microscopy: a new paradigm in vibrational spectroscopy

2020 ◽  
Author(s):  
Mustafa Kansiz ◽  
Craig B. Prater
Keyword(s):  
Vibrational Spectroscopy ◽  
Super Resolution ◽  
Raman Microscopy ◽  
Far Field ◽  
New Paradigm ◽  
Ir And Raman

scholarly journals Far-Field Super-Resolution Vibrational Spectroscopy

2019 ◽  
Vol 91 (14) ◽  
pp. 8723-8731 ◽  
Author(s):  
Christian T. Graefe ◽  
David Punihaole ◽  
Celina M. Harris ◽  
Michael J. Lynch ◽  
Ryan Leighton ◽  
...  
Keyword(s):  
Vibrational Spectroscopy ◽  
Super Resolution ◽  
Far Field

Submicron Simultaneous IR and Raman Spectroscopy (IR+Raman): Breakthrough Developments in Optical Photothermal IR (O-PTIR) Combined for Enhanced Failure Analysis

2019 ◽  
Author(s):  
Jay Anderson ◽  
Mustafa Kansiz ◽  
Michael Lo ◽  
Curtis Marcott
Keyword(s):  
Raman Spectroscopy ◽  
Failure Analysis ◽  
Data Quality ◽  
Raman Spectra ◽  
Spatial Resolution ◽  
Far Field ◽  
Field Mode ◽  
Microscopic Scale ◽  
Analytical Tools ◽  
Ir And Raman

Abstract Failure analysis of organics at the microscopic scale is an increasingly important requirement, with traditional analytical tools such as FTIR and Raman microscopy, having significant limitations in either spatial resolution or data quality. We introduce here a new method of obtaining Infrared microspectroscopic information, at the submicron level in reflection (far-field) mode, called Optical-Photothermal Infrared (O-PTIR) spectroscopy, that can also generate simultaneous Raman spectra, from the same spot, at the same time and with the same spatial resolution. This novel combination of these two correlative techniques can be considered to be complimentary and confirmatory, in which the IR confirms the Raman result and vice-versa, to yield more accurate and therefore more confident organic unknowns analysis.

scholarly journals MINSTED fluorescence localization and nanoscopy

2021 ◽  
Author(s):  
Michael Weber ◽  
Marcel Leutenegger ◽  
Stefan Stoldt ◽  
Stefan Jakobs ◽  
Tiberiu S. Mihaila ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Stimulated Emission ◽  
Far Field ◽  
Inner Membrane ◽  
Mitochondrial Inner Membrane ◽  
Molecular Scale ◽  
Localization Precision ◽  
Super Resolution Microscopy

AbstractWe introduce MINSTED, a fluorophore localization and super-resolution microscopy concept based on stimulated emission depletion (STED) that provides spatial precision and resolution down to the molecular scale. In MINSTED, the intensity minimum of the STED doughnut, and hence the point of minimal STED, serves as a movable reference coordinate for fluorophore localization. As the STED rate, the background and the required number of fluorescence detections are low compared with most other STED microscopy and localization methods, MINSTED entails substantially less fluorophore bleaching. In our implementation, 200–1,000 detections per fluorophore provide a localization precision of 1–3 nm in standard deviation, which in conjunction with independent single fluorophore switching translates to a ~100-fold improvement in far-field microscopy resolution over the diffraction limit. The performance of MINSTED nanoscopy is demonstrated by imaging the distribution of Mic60 proteins in the mitochondrial inner membrane of human cells.

scholarly journals Optimising superoscillatory spots for far-field super-resolution imaging

2018 ◽  
Vol 26 (7) ◽  
pp. 8095 ◽  
Author(s):  
Katrine S. Rogers ◽  
Konstantinos N. Bourdakos ◽  
Guang Hui Yuan ◽  
Sumeet Mahajan ◽  
Edward T. F. Rogers
Keyword(s):  
Super Resolution ◽  
Far Field ◽  
Resolution Imaging

scholarly journals Far-field super-resolution ghost imaging with a deep neural network constraint

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Fei Wang ◽  
Chenglong Wang ◽  
Mingliang Chen ◽  
Wenlin Gong ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Far Field ◽  
Ghost Imaging ◽  
Practical Applications ◽  
Sampling Ratio ◽  
Network Output ◽  
Single Pixel

AbstractGhost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.

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