Advancements in mechanical Raman spectroscopy for applications in energetic materials

Dhiman, Abhijeet; Lewis, Nolan S.; Dillard, Tyler; Sudarshan, Meghana; Tomar, Vikas

doi:10.1016/j.enmf.2021.09.001

Cited by 8 publications

(2 citation statements)

References 70 publications

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“…Concerning the industrial area, RS can be of great assistance for the analysis of product packaging, with the example of Yun Peng et al [ 16 ] who perform a real time and in situ analysis of plastic packaging. Another domain of use of this technique is in the materials science, where it can be used to comprehend the behavior of complex energetic microstructures [ 17 ], as in graphene electrical conductivity in [ 18 ] or in an in situ investigation on carbon materials in batteries [ 19 ]. Moreover, a very important field to consider is environmental science, wherein the RS technique can be used to monitor pollution as performed by T. Somekawa et al in [ 20 ] for a quantitative analysis of subsea oil leaks from pipelines.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Conforti

D’Acunto

Russo

2022

Sensors

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The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Conforti

D’Acunto

Russo

2022

Sensors

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“…In this work, the high-velocity impact is produced using a laser-based projectile launch setup. The laser-based projectile launch system used in this work is a modified version of the system used in previous work 2 , 33 , 46 . The details of modifications are included in the supplementary datasheet.…”

Section: Experiments Methodsmentioning

confidence: 99%

Thermo-mechanical behavior measurement of polymer-bonded sugar under shock compression using in-situ time-resolved Raman spectroscopy

Dhiman

Lewis

Olokun

et al. 2022

Sci Rep

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Quantitative information regarding the local behavior of interfaces in an inhomogeneous material during shock loading is limited due to challenges associated with time and spatial resolution. This paper reports the development of a novel method for in-situ measurement of the thermo-mechanical response of polymer bonded sugar composite where measurements are performed during propagagtion of shock wave in sucrose crystal through polydimethylsiloxane binder. The time-resolved measurements were performed with 5 ns resolution providing an estimation on local pressure, temperature, strain rate, and local shock viscosity. The experiments were performed at two different impact velocities to induce shock pressure of 4.26 GPa and 2.22 GPa and strain rate greater than 106/s. The results show the solid to the liquid phase transition of sucrose under shock compression. The results are discussed with the help of fractography analyses of sucrose crystal in order to obtain insights into the underlying heat generation mechanism.

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Visualizing Shock Induced Thermo-Mechanical Change at Bi-Crystal Interface Using Laser Array Based Nano-Second Raman Spectral Imaging

Dhiman

Dillard²,

Ehler³

et al. 2023

J. dynamic behavior mater.

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Advancements in mechanical Raman spectroscopy for applications in energetic materials

Cited by 8 publications

References 70 publications

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

Thermo-mechanical behavior measurement of polymer-bonded sugar under shock compression using in-situ time-resolved Raman spectroscopy

Visualizing Shock Induced Thermo-Mechanical Change at Bi-Crystal Interface Using Laser Array Based Nano-Second Raman Spectral Imaging

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