Design and Commercialization Strategy of a Gantry-Based Automation Platform for High-Throughput Raman Spectroscopy
Name
romero-cromero1-ms-meche-2025-thesis.pdf
Description
Thesis PDF
Size
13.31 MB
Format
Adobe PDF
Checksum (MD5)
efc10e074b06b84d80d2fa4f7097a232
Author(s)
Romero, Catalina
Advisor(s)
Tadesse, Loza
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Raman spectroscopy is a powerful optical technique that enables rapid, label-free molecular analysis. This offers significant potential to be used across pharmaceutical development, microbiome research, and food diagnostics. However, the utility of Raman spectroscopy in high-throughput applications has been limited by the lack of cost-effective, modular automation platforms capable of handling large volumes of samples with precision and repeatability. Conventional Raman workflows are constrained by manual sample handling, slow throughput, and high user variability, limiting their applicability in high-volume testing environments. To address these challenges, this thesis presents the development and initial validation of a custom two-axis (XY) gantry and a robotic well plate stacker automation platform designed to streamline the sample handling workflow in Raman spectroscopy systems, facilitating high-throughput, precise, and reproducible positioning of microplate samples under a Raman microscope. This thesis also provides a commercialization framework for the system as a standalone automation product, targeting pharmaceutical high-throughput screening, microbiome analysis, and food safety testing. The platform serves the unmet needs in these industries, where labor-intensive and inconsistent sample positioning limits scalability. The commercialization analysis includes an evaluation of market sizing, competitive benchmarking, pricing models, and go-to-market strategies. The modular platform has the potential to enable broader adoption of Raman-based analysis tools by reducing labor intensity and improving repeatability in sample positioning workflows. This work lays the foundation for the future integration of optical feedback and automated analysis, with the goal of transforming how Raman-based diagnostics are conducted at scale.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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