Material Science & Physics Research
Tianyu Lu
Physics researcher specializing in materials characterization and machine learning for material science. Master degree in Physics at Brown, Bachelor's degree in Physics at Penn State.
Thin Film Deposition, Python, Machine Learning, Magnetron Sputtering, Atomic Force Microscopy (AFM), Scanning Tunneling Microscopy (STM), Wearable Devices, Experimental Design
Materials Physics Portfolio
Project Highlights
Triboelectric Nanogenerator (TENG) and Wearable Devices
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Experimental design using different materials as underlayment to optimize the voltage/current combination to achieve excellent output performance. Improved small current/voltage generator used in wearable devices by testing various combinations of voltage conducting materials (silver, aluminum, gold, etc.)
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Learned, upkeep and operate magnetron sputtering device to prepare TENG samples for experiments. Designed and fabricated thin-film-based energy devices using layered material architectures.
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Conducted comprehensive data analysis, documented experimental processes, and applied the scientific method and hypothesis testing, culminating in the preparation, revision, and finalization of two publications.
Cryogenic Systems - Dilution Refrigerator (Bluefors)
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Trained in the Standard Operating Procedures (SOP), and safe handling protocols of a Bluefors dilution refrigerator system at Brown University, studied cooldown and warmup procedures, pulse tube pre-cooling, mixing chamber thermometry, and the thermodynamic cycle underlying millikelvin temperature operation.
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Developed working knowledge of the step-by-step operational procedures for dilution refrigerator preparation, cooldown sequencing, and controlled warmup, familiar with associated instrumentation including temperature sensors, pressure gauges, and vacuum systems used to monitor and maintain system performance.
Predicting Nematic Vector Fields in Liquid Crystals Using Machine Learning
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Investigated the use of AI and Machine Learning techniques for regression-based predictions of the nematic vector field within liquid crystals and developed models combining FCNN and RNN, capable of accurately predicting the local orientation field given a spatial configuration.
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Communicated model and experimental results in a paper, ensuring documentation and reproducibility, culminating in GitHub and ensured reproducibility through structured codebase and documentation.
Publications
1. “High current output DC triboelectric nanogenerator based on organic semiconductor heterojunction,” *Nano Energy*, 2022.
2. “High-performance triboelectric nanogenerators based on organic semiconductor Copper Phthalocyanine,” *Nanoscale*, 2021.
I'm a physicist and materials researcher working at the intersection of experimental physics and machine learning. My work spans thin-film fabrication, surface characterization, and predictive modeling — from engineering triboelectric nanogenerators for wearable devices to building neural network models that predict material behavior. I care about rigorous experimental design paired with computational methods that turn raw data into real insight.
Material Science
Machine Learning
Thin-Film Fabrication
Experimental Design
Let's Connect
I'm always open to new opportunities, collaborations, or just a chat about creative projects. Reach out and let's build something amazing together.
tianyulu2001@gmail.com
Phone
+1 4017498182