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Relevant bibliographies by topics / Super resolution spectroscopy

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Journal articles

Academic literature on the topic 'Super resolution spectroscopy'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Super resolution spectroscopy"

1

Harris, T. D., R. D. Grober, J. K. Trautman, and E. Betzig. "Super-Resolution Imaging Spectroscopy." Applied Spectroscopy 48, no. 1 (1994): 14A—21A. http://dx.doi.org/10.1366/0003702944027589.

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2

Tomita, Motohiro, Hiroki Hashiguchi, Takuya Yamaguchi, Munehisa Takei, Daisuke Kosemura, and Atsushi Ogura. "Super-Resolution Raman Spectroscopy by Digital Image Processing." Journal of Spectroscopy 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/459032.

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We demonstrate the results of a strain (stress) evaluation obtained from Raman spectroscopy measurements with the super-resolution method (the so-called super-resolution Raman spectroscopy) for a Si substrate with a patterned SiN film (serving as a strained Si sample). To improve the spatial resolution of Raman spectroscopy, we used the super-resolution method and a high-numerical-aperture immersion lens. Additionally, we estimated the spatial resolution by an edge force model (EFM) calculation. One- and two-dimensional stress distributions in the Si substrate with the patterned SiN film were obtained by super-resolution Raman spectroscopy. The results from both super-resolution Raman spectroscopy and the EFM calculation were compared and were found to correlate well. The best spatial resolution, 70 nm, was achieved by super-resolution Raman measurements with an oil immersion lens. We conclude that super-resolution Raman spectroscopy is a useful method for evaluating stress in miniaturized state-of-the-art transistors, and we believe that the super-resolution method will soon be a requisite technique.

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3

Candela, Alberto, David R. Thompson, David Wettergreen, et al. "Probabilistic Super Resolution for Mineral Spectroscopy." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13241–47. http://dx.doi.org/10.1609/aaai.v34i08.7030.

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Earth and planetary sciences often rely upon the detailed examination of spectroscopic data for rock and mineral identification. This typically requires the collection of high resolution spectroscopic measurements. However, they tend to be scarce, as compared to low resolution remote spectra. This work addresses the problem of inferring high-resolution mineral spectroscopic measurements from low resolution observations using probability models. We present the Deep Gaussian Conditional Model, a neural network that performs probabilistic super resolution via maximum likelihood estimation. It also provides insight into learned correlations between measurements and spectroscopic features, allowing for the tractability and interpretability that scientists often require for mineral identification. Experiments using remote spectroscopic data demonstrate that our method compares favorably to other analogous probabilistic methods. Finally, we show and discuss how our method provides human-interpretable results, making it a compelling analysis tool for scientists.

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4

Buckin, Vitaly, and Evegny Kudryashov. "Super sonic: High-resolution ultrasonic spectroscopy." Biochemist 24, no. 4 (2002): 25–27. http://dx.doi.org/10.1042/bio02404025.

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High-resolution ultrasonic spectrometry is a novel analytical technique with enormous potential for the investigation of a wide range of samples and dynamic processes. The non-destructive technique is based on measuring the changes that take place to ultrasonic waves as they pass through materials.

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5

Graefe, Christian T., David Punihaole, Celina M. Harris, Michael J. Lynch, Ryan Leighton, and Renee R. Frontiera. "Far-Field Super-Resolution Vibrational Spectroscopy." Analytical Chemistry 91, no. 14 (2019): 8723–31. http://dx.doi.org/10.1021/acs.analchem.9b01731.

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6

Lim, Alane. "Machine learning method puts the “super” in super-resolution spectroscopy." Scilight 2021, no. 49 (2021): 491108. http://dx.doi.org/10.1063/10.0009031.

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7

DeLisle, Charles F., H. Bhagya Mendis, and Justin L. Lorieau. "Super resolution NOESY spectra of proteins." Journal of Biomolecular NMR 73, no. 3-4 (2019): 105–16. http://dx.doi.org/10.1007/s10858-019-00231-x.

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8

Matsuo, T., and H. Matsuda. "A super-high-resolution tandem mass spectrometer." International Journal of Mass Spectrometry and Ion Processes 91, no. 1 (1989): 27–40. http://dx.doi.org/10.1016/0168-1176(89)80107-7.

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9

Winterauer, Dominik J., Daniel Funes-Hernando, Jean-Luc Duvail, Saïd Moussaoui, Tim Batten, and Bernard Humbert. "Sub-Micron Spatial Resolution in Far-Field Raman Imaging Using Positivity-Constrained Super-Resolution." Applied Spectroscopy 73, no. 8 (2019): 902–9. http://dx.doi.org/10.1177/0003702819832355.

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Raman microscopy is a valuable tool for detecting physical and chemical properties of a sample material. When probing nanomaterials or nanocomposites the spatial resolution of Raman microscopy is not always adequate as it is limited by the optical diffraction limit. Numerical post-processing with super-resolution algorithms provides a means to enhance resolution and can be straightforwardly applied. The aim of this work is to present interior point least squares (IPLS) as a powerful tool for super-resolution in Raman imaging through constrained optimization. IPLS’s potential for super-resolution is illustrated on numerically generated test images. Its resolving power is demonstrated on Raman spectroscopic data of a polymer nanowire sample. Comparison to atomic force microscopy data of the same sample substantiates that the presented method is a promising technique for analyzing nanomaterial samples.

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10

Jeong, Dokyung, and Doory Kim. "Super‐resolution fluorescence microscopy‐based single‐molecule spectroscopy." Bulletin of the Korean Chemical Society 43, no. 3 (2022): 316–27. http://dx.doi.org/10.1002/bkcs.12471.

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