[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120751-en":3,"doc-seo-120751-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120751,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy - doctoral thesis","Raman spectroscopy analysis demands substantial time and effort, making effective tools essential for extracting meaningful results. This PhD thesis advances improved Raman data analysis methods, primarily using Python-based machine learning. Micrometric pillars patterned via an electrohydrodynamic process, followed by gold coating, enable surface-enhanced Raman scattering. Convolutional neural networks are used to evaluate pattern surface morphology from microscope and atomic force microscopy images, achieving high prediction accuracy. A systematic review of Raman machine learning literature supports a reusable pipeline, combining PCA and self-organizing maps through a graphical user interface, and enabling precise multi-Lorentzian peak analysis.","Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy  \nBy  \nPAULO ALEXANDRE DE CARVALHO GOMES  \nA thesis submitted to the University of Birmingham for the degree of  \nDOCTOR OF PHILOSOPHY  \nAdvanced Nano-Materials Structures and Applications Group School of Chemical Engineering  \nCollege of Engineering and Physical Sciences University of Birmingham  \nJuly 2022  \nUniversity of Birmingham Research Archive  \ne-theses repository  \nThis unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation.  \nAny use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.  \nAbstract  \nIn Raman spectroscopy, data analysis occupies a large amount of time and effort; thus, it is paramount to have the proper tools to extract the most meaning from the Raman analysis. This thesis explores improved ways to analyse Raman data mostly by using machine learning techniques available in Python. The substrate used throughout this thesis has been patterned through an electrohydrodynamic process that patterns micrometric pillars onto the substrate, which, after being gold coated, can generate surface-enhanced Raman scattering. An initial theoretical background was laid for the electrohydrodynamic process and additional observations regarding the fluid mechanics. Furthermore, when the structures are fabricated, and Raman measurements are taken, we show that it is possible to create an effective convolutional neural networks that systematically evaluate these patterns’ surface morphology and extracts features responsible for the surface-enhanced Raman scattering phenomenon. Being able to predict 90% of the time from optical microscope images and 99% of the time with atomic force microscopy images  \nAdditionally, a thorough machine learning analysis of the Raman literature was done. The best machine learning algorithms were put together into a script combined with a graphical user Interface that can run multiple commands such as principal component analysis and self-organizing maps, all in a centralised way. This way, we managed to consistently extract information from Raman and surfaceenhanced Raman scattering spectra to open possibilities for precise peak analysis methods using a multi-Lorentzian fit algorithm.  \nAcknowledgements  \nThe work presented in this thesis was carried out under the supervision of Professor Pola Goldberg Oppenheimer , head of the Advanced Nano-Materials Structures and Applications (ANMSA) group in the school of Chemical Engineering at the University of Birmingham.  \nI would like to express my gratitude to all of those who have been part of this journey with me.  \nFirst, I am very grateful to Professor Pola Oppenheimer for letting me be part of this group and project, which made me grow as an individual and a professional. Thank you for all the meetings we had, which were always productive and always gave me the energy to continue and most of all, your crucial feedback. It has always been much appreciated.  \nSecond, I would also want to thank all new and old members of the ANMSA group that have been working around me since my arrival and until my farewell. Specially Dr. JJ Rickard , Dr. Carl Banbury , Dr. Mike Hardy , Mike MacGregor , Dr. Paolo Passaretti , Dr. Emma McCarthy , Dr. Michael Clancy , Dr. Liam Kelleher , Dr. Martin Chu , Georgia Harris and Clarissa Stickland. You have always been there to talk about science and random life questions. We had good times, and I will always cherish them when looking back.  \nI would also want to give a heart-warming thanks to the very special Portuguese com","cbCaiqF0jDJtTZvT","https://ap.wps.com/l/cbCaiqF0jDJtTZvT","pdf",14909300,1,271,"English","en",105,"# Abstract\n# Acknowledgements\n# Table of Contents\n# List of Figures\n# List of Tables\n# List of Equations","[{\"question\":\"How does the thesis improve Raman spectroscopy data analysis?\",\"answer\":\"It uses machine learning approaches implemented in Python to extract meaningful information from Raman and surface-enhanced Raman scattering spectra, reducing analysis time and improving interpretability.\"},{\"question\":\"What experimental substrate and surface-enhanced Raman scattering mechanism are used?\",\"answer\":\"Micrometric pillars are patterned on a substrate through an electrohydrodynamic process and then gold coated, producing surface-enhanced Raman scattering.\"},{\"question\":\"What machine learning methods are used to evaluate the patterned structures?\",\"answer\":\"Convolutional neural networks analyze surface morphology from optical microscope images and atomic force microscopy images to identify features related to surface-enhanced Raman scattering.\"}]","Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy - 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