Author ORCID Identifier
https://orcid.org/0000-0002-0962-6777
Date of Award
Spring 5-2025
Document Type
Thesis (Ph.D.)
Department or Program
Engineering Sciences
First Advisor
John X.J. Zhang
Abstract
The reliable detection of molecules in liquid environments is a continuing challenge in chemical sensing and biological diagnostics. While surface-enhanced Raman spectroscopy (SERS) is well-recognized for its high sensitivity and molecular specificity, conventional platforms – based on colloidal nanoparticles or patterned substrates – have shortcomings. These conventional configurations struggle with signal reproducibility, non-uniform hotspot distribution, and, critically, restricted accessibility for large targets such as proteins and exosomes.
This thesis develops a computation-guided approach to developing next-generation SERS sensors capable of overcoming these persistent obstacles. At the heart of the study is the design and fabrication of metal-insulator-metal (MIM) nanoparticles, where a metallic core and surface metal nanoparticles are separated by a dielectric spacer. Finite element analysis (FEA) is used to simulate and optimize the electromagnetic field enhancements achieved by different MIM structures, with a focus on maximizing both signal intensity and molecular accessibility. These simulations provide clear guidelines for the synthesis of nanoparticles with controlled core size, dielectric spacer thickness, and surface nanoparticle arrangement.
The synthesis process involves both experiments and theoretical models, including the application of Derjaguin-Landau-Verwey-Overbeek (DLVO) theory to guide controlled MIM nanoparticle assembly and minimize nanoparticle aggregation. The resulting MIM nanoparticles demonstrate robust SERS performance for both small molecules and large biomolecular analytes in liquid samples. Protocols for surface modification and liquid-phase detection are developed, ensuring that large targets can interact effectively with the sensor’s enhanced fields.
In parallel, this work establishes robust data analysis strategies that combine spectral preprocessing with statistic digitalization and machine learning techniques. These methods help ensure consistent quantification and accurate identification of complex and heterogeneous SERS signals.
In summary, the research presented here offers a solution to current limitations in SERS-based sensing. By integrating computation, synthesis, and data analytics, this thesis aims to advance SERS technology in molecular diagnostics and other real-world applications.
Original Citation
Zhou, Junhu, Ziqian Wu, Congran Jin, and John XJ Zhang. "Machine learning assisted dual-functional nanophotonic sensor for organic pollutant detection and degradation in water." npj Clean Water 7, no. 1 (2024): 3.
Jin, Congran, Ziqian Wu, John H. Molinski, Junhu Zhou, Yundong Ren, and John XJ Zhang. "Plasmonic nanosensors for point-of-care biomarker detection." Materials Today Bio 14 (2022): 100263.
Tymm, Carly, Junhu Zhou, Amogha Tadimety, Alison Burklund, and John XJ Zhang. "Scalable COVID-19 detection enabled by lab-on-chip biosensors." Cellular and molecular bioengineering 13 (2020): 313-329.
Zhou, Junhu, Yuan Nie, Congran Jin, and John XJ Zhang. "Engineering biomimetic extracellular matrix with silica nanofibers: from 1D material to 3D network." ACS Biomaterials Science & Engineering 8, no. 6 (2022): 2258-2280.
Zhou, Junhu, Yundong Ren, Yuan Nie, Congran Jin, Jiyoon Park, and John XJ Zhang. "Dual fluorescent hollow silica nanofibers for in situ pH monitoring using an optical fiber." Nanoscale Advances 5, no. 8 (2023): 2180-2189.
Recommended Citation
Zhou, Junhu, "Computation-Driven Design and Synthesis of Molecular Sensors for Surface-Enhanced Raman Spectroscopy in Liquids" (2025). Dartmouth College Ph.D Dissertations. 398.
https://digitalcommons.dartmouth.edu/dissertations/398
