Relevant bibliographies by topics / Deep Raman Spectroscopy

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Deep Raman Spectroscopy'

Author: Grafiati
Published: 11 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Deep Raman Spectroscopy"

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Luo, Ruihao, Juergen Popp, and Thomas Bocklitz. "Deep Learning for Raman Spectroscopy: A Review." Analytica 3, no. 3 (2022): 287–301. http://dx.doi.org/10.3390/analytica3030020.

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Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman si
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Zhou, Qian, Zhiyong Zou, and Lin Han. "Deep Learning-Based Spectrum Reconstruction Method for Raman Spectroscopy." Coatings 12, no. 8 (2022): 1229. http://dx.doi.org/10.3390/coatings12081229.

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Raman spectroscopy, measured by a Raman spectrometer, is usually disturbed by the instrument response function and noise, which leads to certain measurement error and further affects the accuracy of substance identification. In this paper, we propose a spectral reconstruction method which combines the existing maximum a posteriori (MAP) method and deep learning (DL) to recover the degraded Raman spectrum. The proposed method first employs the MAP method to reconstruct the measured Raman spectra, so as to obtain preliminary estimated Raman spectra. Then, a convolutional neural network (CNN) is
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Mosca, Sara, Priyanka Dey, Marzieh Salimi, et al. "Spatially Offset Raman Spectroscopy—How Deep?" Analytical Chemistry 93, no. 17 (2021): 6755–62. http://dx.doi.org/10.1021/acs.analchem.1c00490.

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Arnold, Bradley R., Christopher E. Cooper, Michael R. Matrona, Darren K. Emge, and Jeffrey B. Oleske. "Stand-off deep-UV Raman spectroscopy." Canadian Journal of Chemistry 96, no. 7 (2018): 614–20. http://dx.doi.org/10.1139/cjc-2017-0678.

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UV Raman spectra were measured using a novel experimental configuration. This configuration allows many of the difficulties associated with UV excitation and high-power pulsed laser sources to be mitigated. Large sample areas are imaged into the detection system allowing high power excitation sources to be used while simultaneously avoiding sample degradation and multi-photon absorption effects. Such large detection areas allow large numbers of molecular scatters to be probed even with minimal penetration depth. Alignment issues between sample and collection optics are also simplified. Several
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Matousek, P. "Raman Signal Enhancement in Deep Spectroscopy of Turbid Media." Applied Spectroscopy 61, no. 8 (2007): 845–54. http://dx.doi.org/10.1366/000370207781540178.

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A new, passive method for enhancing spontaneous Raman signals for the spectroscopic investigation of turbid media is presented. The main areas to benefit are transmission Raman and spatially offset Raman spectroscopy approaches for deep probing of turbid media. The enhancement, which is typically several fold, is achieved using a multilayer dielectric optical element, such as a bandpass filter, placed within the laser beam over the sample. This element prevents loss of the photons that re-emerge from the medium at the critical point where the laser beam enters the sample, the point where major
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Macleod, Neil A., and Pavel Matousek. "Deep Noninvasive Raman Spectroscopy of Turbid Media." Applied Spectroscopy 62, no. 11 (2008): 291A—304A. http://dx.doi.org/10.1366/000370208786401527.

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Liu, Yuping, Junchi Wu, Yuqing Wang, and Sicen Dong. "Direct recognition of Raman spectra without baseline correction based on deep learning." AIP Advances 12, no. 8 (2022): 085212. http://dx.doi.org/10.1063/5.0100937.

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Raman spectroscopy, widely used for material analysis, has formed an extensive spectral library. In practical applications, it is usually necessary to preprocess Raman spectroscopy of the target material and then identify the material through spectral-library comparisons. Baseline correction is an important step during pre-processing and it usually requires a special algorithm. However, it demands time and high-level professional skill, confining Raman spectroscopy to laboratories rather than large-scale applications. Therefore, to improve its efficiency and take advantage of the big data in t
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Domes, Robert, Christian Domes, Christian R. Albert, Gerhard Bringmann, Jürgen Popp, and Torsten Frosch. "Vibrational spectroscopic characterization of arylisoquinolines by means of Raman spectroscopy and density functional theory calculations." Physical Chemistry Chemical Physics 19, no. 44 (2017): 29918–26. http://dx.doi.org/10.1039/c7cp05415g.

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Cao, Zheng, Xiang Pan, Hongyun Yu, et al. "A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra." BME Frontiers 2022 (May 2, 2022): 1–10. http://dx.doi.org/10.34133/2022/9872028.

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Objective and Impact Statement. Distinguishing tumors from normal tissues is vital in the intraoperative diagnosis and pathological examination. In this work, we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues. Introduction. Raman spectra can reflect the substance components of the target tissues. However, the feature peak is slight and hard to detect due to environmental noise. Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches. Methods. First, we
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Zhang, Yilong, Tianke Wang, Kang Du, Peng Chen, Haixia Wang, and Haohao Sun. "General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning." Applied Sciences 14, no. 22 (2024): 10245. http://dx.doi.org/10.3390/app142210245.

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Raman spectroscopy is a powerful tool for identifying substances, yet accurately analyzing mixtures remains challenging due to overlapping spectra. This study aimed to develop a deep learning-based framework to improve the identification of components in mixtures using Raman spectroscopy. We propose a three-branch feature fusion network that leverages spectral pairwise comparison and a multi-head self-attention mechanism to capture both local and global spectral features. To address limited data availability, traditional data augmentation techniques were combined with deep convolutional genera
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Dissertations / Theses on the topic "Deep Raman Spectroscopy"

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Patil, Raj. "Deep UV Raman Spectroscopy." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613378.

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This thesis examines the performance of a custom built deep UV laser (257.5nm) for Raman spectroscopy and the advantages of Raman spectroscopy with a laser in the deep UV over a laser in the visible range (532 nm). It describes the theory of resonance Raman scattering, the experimental setup for Raman spectroscopy and a few Raman spectroscopy measurements. The measurements were performed on biological samples oak tree leaf and lactobacillus acidophilus and bifidobacteria from probotioc medicinal capsules. Fluorescence free Raman spectra were acquired for the two samples with 257.5 nm laser. Th
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Sundarajoo, Shankaran. "Deep Raman Spectroscopy in the analytical forensic investigation of concealed substances." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/61022/1/Shankaran_Sundarajoo_Thesis.pdf.

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Deep Raman Spectroscopy is a domain within Raman spectroscopy consisting of techniques that facilitate the depth profiling of diffusely scattering media. Such variants include Time-Resolved Raman Spectroscopy (TRRS) and Spatially-Offset Raman Spectroscopy (SORS). A recent study has also demonstrated the integration of TRRS and SORS in the development of Time-Resolved Spatially-Offset Raman Spectroscopy (TR-SORS). This research demonstrates the application of specific deep Raman spectroscopic techniques to concealed samples commonly encountered in forensic and homeland security at various wo
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Kerssens, Marleen Maartje. "Study of calcification formation and disease diagnostics utilising advanced vibrational spectroscopy." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7924.

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The accurate and safe diagnosis of breast cancer is a significant societal issue, with annual disease incidence of 48,000 women and around 370 men in the UK. Early diagnosis of the disease allows more conservative treatments and better patient outcomes. Microcalcifications in breast tissue are an important indicator for breast cancers, and often the only sign of their presence. Several studies have suggested that the type of calcification formed may act as a marker for malignancy and its presence may be of biological significance. In this work, breast calcifications are studied with FTIR, sync
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Vardaki, Martha. "Advanced Raman techniques for real time cancer diagnostics." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/24215.

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Cancer is one of the greatest causes of death in modern societies, affecting over 350,000 new cases every year in the UK. Although there are currently more than 100 different cancer types, breast and prostate cancer remain the most common types for women and men respectively. A number of different cancer types follow, with bladder cancer being the ninth most significant type, accounting for 3% of the total new cases. The currently employed techniques aim to diagnose the cancer at an early stage, where the symptoms are easier to be treated and the disease more likely to be cured. A further issu
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Klonowska, Iwona. "Deep subduction of the Seve Nappe Complex in the Scandinavian Caledonides." Doctoral thesis, Uppsala universitet, Institutionen för geovetenskaper, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-332525.

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This thesis seeks to improve our understanding of the processes involved in continental collision zones, with a particular focus on subduction-exhumation. The main objective of this work has been to define the tectonometamorphic evolution of the deeply subducted Seve Nappe Complex (SNC) in the Scandinavian Caledonides. I utilize mineralogy, petrology and geochronology to constrain the P-T-t paths of the SNC rocks in Sweden. The research has focused on the high grade rocks of the SNC and resulted in the discovery of metamorphic diamonds within the gneisses in west-central Jämtland and southern
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Anzolini, Chiara. "Depth of formation of super-deep diamonds." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424577.

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Diamonds, and the mineral inclusions they trap during growth, are pristine samples from the mantle that reveal processes in the deep Earth, provided the depth of formation of an inclusion-diamond pair being known. The majority of diamonds are lithospheric, while the depth of origin of super-deep diamonds (SDDs), which represent only 6% of the total, is uncertain. SDDs are considered to be sub-lithospheric, with formation from 300 to 800 km depth, on the basis of the inclusions trapped within them, which are believed to be the products of retrograde transformation from lower-mantle or transitio
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Holmberg, Johanna. "Pressure-Temperature-time Constraints on the Deep Subduction of the Seve Nappe Complex in Jämtland and southern Västerbotten, Scandinavian Caledonides." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-334822.

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The Scandinavian Caledonides are defined by long transported thrust sheets emplaced in a nappe stratigraphic succession onto the Paleozoic Baltica platform, as a result of the collision between the paleo-continents Baltica and Laurentia. This Palaeozoic collisional orogen is nowadays exposed at mid-crustal levels, thus provides an excellent ground for in situ studies of mountain building processes. The complex nappe stack is subdivided into the Lower, Middle, Upper and Uppermost allochthons. The tectonostratigraphic highest unit in the Middle Allochthon is the Seve Nappe Complex (SNC), itself
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DHANABALAN, BALAJI. "Developing Metal-Halide Layered Perovskite Nanomaterials for Optoelectronics." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1041905.

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The abundant chemical tunability along with outstanding optoelectronic properties of the 2D metal-halide layered perovskite materials, as well as the potential prospect on understanding the structure-property relationships at the molecular level, provide enormous opportunity for the scientific community on designing new and efficient 2D metal-halide layered perovskites for a specific optoelectronic application. These materials still require attention on understanding their fundamental electronic properties and on controlling their synthesis parameters to produce high quality materials. Moreove
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Vishnu, Kumar R. "Raman Spectroscopy Instrumentation and Its Application in Deep Tissue Imaging." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6035.

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Raman spectroscopy is based on inelastic scattering, which gives molecular information. Due to its weak nature, for a long-time Raman spectroscopy was limited to only study of molecular interactions, but with the advancement in technology such as highly compact and stable lasers, high quantum efficiency and low noise detectors and so on, Raman spectroscopy today finds itself in wide applications ranging from study on biological cells to space applications. In the first work we aim to develop a 3D Raman imaging system that can give both chemical and morphological information of the concealed
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Chadwick, Christopher T. "Resonance Raman spectroscopy utilizing tunable deep ultraviolet excitation for materials characterization." 2009. http://www.lib.ncsu.edu/theses/available/etd-01062009-141349/unrestricted/etd.pdf.

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Book chapters on the topic "Deep Raman Spectroscopy"

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Taguchi, Atsushi. "Deep-Ultraviolet Surface-Enhanced Raman Scattering." In Far- and Deep-Ultraviolet Spectroscopy. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55549-0_8.

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Anjikar, Ajinkya, Nidhi Prahlad Rao, Rajapandian Paneerselvam, et al. "Deep Learning in Biomedical Applications of Raman Spectroscopy." In Biological and Medical Physics, Biomedical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5345-1_9.

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Handen, Joseph D., and Igor K. Lednev. "Deep UV Resonance Raman Spectroscopy for Characterizing Amyloid Aggregation." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2978-8_6.

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Macleod, N. A., M. D. Morris, and P. Matousek. "Characterisation of Deep Layers of Tissue and Powders: Spatially Offset Raman and Transmission Raman Spectroscopy." In Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02649-2_3.

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Nakashima, Shinichi, and Takeshi Mitani. "Characterization of SiC Crystals by Using Deep UV Excitation Raman Spectroscopy." In Silicon Carbide and Related Materials 2005. Trans Tech Publications Ltd., 2006. http://dx.doi.org/10.4028/0-87849-425-1.333.

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Wang, Xiaoli, Guangzheng Zhou, and Xue Zhong Wang. "Integration of Raman Spectroscopy, On-Line Microscopic Imaging and Deep Learning-Based Image Analysis for Real-Time Monitoring of Cell Culture Process." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71253-1_18.

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Kleppe, Annette K., and Andrew P. Jephcoat. "Raman Spectroscopic Studies of Hydrous and Nominally Anhydrous Deep Mantle Phases." In Earth's Deep Water Cycle. American Geophysical Union, 2013. http://dx.doi.org/10.1029/168gm07.

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Topilina, Natalya I., Vitali Sikirzhytski, Seiichiro Higashiya, Vladimir V. Ermolenkov, John T. Welch, and Igor K. Lednev. "Genetically Engineered Polypeptides as a Model of Intrinsically Disordered Fibrillogenic Proteins: Deep UV Resonance Raman Spectroscopic Study." In Instrumental Analysis of Intrinsically Disordered Proteins. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470602614.ch9.

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Wathsala N. Jinadasa, M. H., Amila C. Kahawalage, Maths Halstensen, Nils-Olav Skeie, and Klaus-Joachim Jens. "Deep Learning Approach for Raman Spectroscopy." In Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99770.

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Raman spectroscopy is a widely used technique for organic and inorganic chemical material identification. Throughout the last century, major improvements in lasers, spectrometers, detectors, and holographic optical components have uplifted Raman spectroscopy as an effective device for a variety of different applications including fundamental chemical and material research, medical diagnostics, bio-science, in-situ process monitoring and planetary investigations. Undoubtedly, mathematical data analysis has been playing a vital role to speed up the migration of Raman spectroscopy to explore diff
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Taguchi, Atsushi. "Deep-Ultraviolet Surface- and Tip-Enhanced Raman Spectroscopy." In Recent Developments in Plasmon-Supported Raman Spectroscopy. WORLD SCIENTIFIC (EUROPE), 2017. http://dx.doi.org/10.1142/9781786344243_0005.

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Conference papers on the topic "Deep Raman Spectroscopy"

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Qiao, Wei, Cong Li, Xingen Gao, Huali Jiang, Hongyi Zhang, and Juqiang Lin. "Raman spectroscopy enhancement based on deep learning." In Seventeenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2024), edited by Valery V. Tuchin, Qingming Luo, and Lihong V. Wang. SPIE, 2025. https://doi.org/10.1117/12.3056643.

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Nicolson, Fay, Bohdan Andreiuk, Eunah Lee, et al. "Optimization of Surface-Enhanced Spatially Offset Raman spectroscopy for Applications in Pre-Clinical Cancer Imaging." In Optical Molecular Probes, Imaging and Drug Delivery. Optica Publishing Group, 2025. https://doi.org/10.1364/omp.2025.om3e.3.

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Achieving high selectivity and specificity in deep tumor imaging remains a critical challenge in optical imaging. While Raman spectroscopy has been widely used for imaging various tissues, its penetration depth in vivo is limited. Here, we introduce a novel approach that integrates Spatially Offset Raman Spectroscopy (SORS) with Surface Enhanced Raman Scattering (SERS) nanoparticles, known as Surface Enhanced Spatially Offset Raman Spectroscopy (SESORS), for in vivo imaging of deep-seated tumors. We detail the optimization of SORS instrumentation to improve signal-to-noise ratio (SNR), spectra
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Harrington, Joseph T., Vsevolod Cheburkanov, Mykyta Kizilov, Ilya Kulagin, Georgi Petrov, and Vladislav Yakovlev. "Deep ultraviolet resonant Raman (DUVRR) spectroscopy for spectroscopic evaluation and disinfection of food and agricultural samples." In Photonic Technologies in Plant and Agricultural Science II, edited by Dag Heinemann and Gerrit Polder. SPIE, 2025. https://doi.org/10.1117/12.3042310.

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Lionts, Marilyn, Ezekiel Haugen, Anita Mahadevan-Jansen, and Yuankai Huo. "Deep-learning-based acquisitional denoising for Raman spectroscopy using CNN and transformer." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027465.

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Stothard, David J. M., Roman Spesyvtsev, John Leck, et al. "Molecule-specific, stand-off airborne substance detection with Deep-UV excited, range-resolved, single-photon "Quantum" Raman spectroscopy: Towards an optical "tricorder" with molecular LIDAR." In Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXVI, edited by Jason A. Guicheteau, Christopher R. Howle, and Tanya L. Myers. SPIE, 2025. https://doi.org/10.1117/12.3054999.

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Tuschel, David D., Aleksandr V. Mikhonin, Brian E. Lemoff, Sanford A. Asher, P. M. Champion, and L. D. Ziegler. "Deep Ultraviolet Resonance Raman Spectroscopy of Explosives." In XXII INTERNATIONAL CONFERENCE ON RAMAN SPECTROSCOPY. AIP, 2010. http://dx.doi.org/10.1063/1.3482860.

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Arnold, Bradley R., Eric Bowman, and Leslie Scheurer. "Deep-UV standoff Raman spectroscopy." In Next-Generation Spectroscopic Technologies XII, edited by Richard A. Crocombe, Luisa T. Profeta, and Abul K. Azad. SPIE, 2019. http://dx.doi.org/10.1117/12.2519033.

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Gallo, Emanuela, and Frank Duschek. "Deep-UV Remote Raman Detection of Chlorine." In Applied Industrial Spectroscopy. OSA, 2021. http://dx.doi.org/10.1364/ais.2021.am2d.5.

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Wang, Luling, David Tuschel, Alexander Tikhonov, Sanford A. Asher, P. M. Champion, and L. D. Ziegler. "Crystalline Colloidal Array Deep UV Narrow Band Radiation Filter." In XXII INTERNATIONAL CONFERENCE ON RAMAN SPECTROSCOPY. AIP, 2010. http://dx.doi.org/10.1063/1.3482807.

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Arzhantsev, Sergey, Connie M. Ruzicka, John F. Kauffman, P. M. Champion, and L. D. Ziegler. "Comparative Studies of Therapeutic Protein Secondary Structure Using Deep UV Resonance Raman Spectroscopy." In XXII INTERNATIONAL CONFERENCE ON RAMAN SPECTROSCOPY. AIP, 2010. http://dx.doi.org/10.1063/1.3482450.

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Reports on the topic "Deep Raman Spectroscopy"

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Nelson, W. H., and J. F. Sperry. The Rapid Detection of Single Bacterial Cells by Deep UV Micro Raman Spectroscopy. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada249811.

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