Kunlun Wu

Kunlun Wu

MS & BS in Materials Science and Engineering at Columbia University
BA in Physics & Japanese at Colgate University

Posts by Collection

portfolio

Illustrations

A collection of my illustrations, including both analog and digital work.

Music

Some Recordings and Demos saved as Playlists on Youtube

publications

research

Low Temperature Conductivity and Structural Analysis of Cu-CeO₂ Nanoparticles

Aug 2023 – May 2025, Paper under review

Under guidance of Professor Siu-Wai Chan, Columbia University, NY

This project, conducted in collaboration with Xinyi Ma, Zhaowen Lin, and Jianing Zhou, investigates small-polaron conduction in copper-doped nanocrystalline ceria (CeO₂). Through impedance spectroscopy, X-ray diffraction, and structural analysis, it examines how Cu incorporation modifies charge-transport behavior and may interact with local defect chemistry, revealing a measurable enhancement in conductivity compared with undoped ceria, suggesting a possible correlation with oxygen-vacancy dynamics previously reported in high-temperature catalytic studies.

Refractive Index Enhancement of Polymer Thin Film using CeO₂ Nanoparticles

Aug 2023 - May 2024, Carried over to new project with modification using ions

Senior Design Project, Columbia University, NY

Working with Cheng-Chia Tsai, this project explores the incorporation of nanocrystalline ceria (CeO₂) particles into polymer matrices to enhance the refractive index for photonic and optical-coating applications. Using ellipsometry and optical modeling, it investigates how nanoparticle loading influences optical constants in flexible, transparent nanocomposite thin films, revealing a nontrivial increase in refractive index while posing challenges for maintaining film uniformity.

ML Modeling of Reactivity Descriptors of Metal Clusters toward N₂ Activation

May 2025

Final project in Special Topics in Materials Science by Professor Simon Billinge, Columbia University, NY

This project, conducted in collaboration with Nicolas Raffaele, draws inspiration from the work of Li-Hui Mou et al. (ACS Catalysis 2015). As a practice exercise in applying machine learning methods to materials data, we reproduced and extended parts of the study using various models: Support Vector Regression (SVR) with RBF and polynomial kernels, Random Forest Regression (RFR) with Recursive Feature Elimination (RFE) and SelectKBest feature selection, and Multilayer Perceptron (MLP) models—both from scikit-learn and a self-compiled architecture. Our objective was to evaluate how well these models capture the underlying relationships in the dataset and to compare their predictive performance with the original authors’ reported results.

Refractive Index Enhancement of Polymer Thin Film using Ce, Cu, and Ti Ions

May 2024 - Present, Manuscript in preparation

Under guidance of Professor Nanfang Yu, Columbia University, NY

Working with Cheng-Chia Tsai, this project investigates metal-ion doping as a solution-based approach to tune the optical response of SU-8 polymer films. Through controlled curing and thermal treatment, it produces uniform, flexible thin films with increased refractive index and subtle color shifts. Ellipsometric analysis and Cauchy/Lorentzian optical modeling are used to extract optical constants, and a preliminary machine-learning framework is being developed to correlate refractive-index trends as additional data become available.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.