About Tim Nunney
Dr. Tim Nunney is a product and applications specialist at Thermo Fisher Scientific with a background that spans surface analysis and microanalysis. He has extensive experience with X ray photoelectron spectroscopy and with other analytical methods used to study elemental composition, chemical bonding, and material structure. His expertise supports researchers who rely on high resolution spectroscopic and microanalytical tools to understand how chemical changes at surfaces and interfaces affect the performance of materials and devices.
Expertise and focus
Tim has worked across a broad range of applications in materials science and industrial research. His technical foundation includes surface chemical characterization using X ray photoelectron spectroscopy and complementary microanalytical techniques used to identify and quantify elements, evaluate chemical environments, and assess variations in composition. He has authored academic papers, educational material, application notes, and technical guidance that help scientists interpret data generated from these techniques and apply them effectively to real scientific questions.
Scientific perspective
Tim’s perspective reflects years of engagement with researchers who use spectroscopy and microanalysis to answer questions about material performance, contamination, corrosion, adhesion, catalysis, and thin film chemistry. Prior to joining Thermo Fisher Scientific, he worked as a postdoctoral research fellow at the University of Southampton, where he investigated the dynamics of molecular dissociation on metal surfaces. He holds a PhD in Surface Science and an MSc in Surface Science and Catalysis from the University of Liverpool, along with a BSc with honors in Chemistry from the University of Nottingham.
He brings a clear understanding of how analytical methods contribute to a detailed view of material behavior and how different techniques provide complementary insight. His work helps scientists select appropriate characterization strategies and extract meaningful conclusions from their data.
