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Research Highlights
Building a Trustworthy Nanoscience ChatBot
The Center for Functional Nanomaterials has developed a nanoscience chatbot that grounds its answers in a set of trusted documents, eliminating the tendency to fabricate facts, a behavior that renders existing chatbots unusable for science.
Machine-Learning Accelerates Interpretation of Carbon X-Ray Spectra
A team of scientists from Lawrence Livermore and Brookhaven Labs demonstrated a robust machine learning model that predicts and interprets the X-ray absorption spectra of amorphous carbon.
Watching Metals Oxidize at the Atomic Scale
CFN users and staff discovered unexpected reaction dynamics, where oxidation and reduction occur at the same time, due to the countering effect of the gaseous carbon monoxide (CO) oxidation product.
AI Deduces Material Structure From Complex X-Ray Spectra
CFN researchers developed and demonstrated a new, semi-supervised machine learning model for discovering structure-spectrum relationships in x-ray absorption near-edge structure (XANES) spectra.
Warming up Valley Polarization
CFN staff led a collaborative team that realized room-temperature, selective population of a specific energy valley (valley polarization) in a 2D quantum material (MoS2), by coupling it to a chiral perovskite material for spin-selective charge transfer.
Rare & Efficient Energy Transfer in 2D Heterostructures
CFN staff and users co-led a study that discovered an efficient and unusual energy transfer process from a lower to higher bandgap semiconductor material in stacked heterostructures of monolayer WSe2 & MoS2.
Understanding the Tantalizing Benefits of Tantalum for Qubits
CFN users collaborated with CFN staff on a study to correlate tantalum surface oxidation states with coherence losses in superconducting qubits. Developing a variable energy X-ray photoelectron spectroscopy methodology was key to revealing the depth profile of tantalum oxidation.
In Fuel Cell Catalysts, Three Elements are Better than One
CFN staff and users from Binghamton University co-led a study resulting in a new method to synthesize monodisperse, ternary (Pt-Fe-Ni) colloidal catalysts and measure their performance.
AI Discovers New Nanostructures Using X-rays
A CFN-led team directed a blend of two self-assembling polymers to form a large library of nanostructures.
High Throughput and Multimodal Materials Identification
CFN researchers and collaborators employed artificial intelligence (AI) and machine learning (ML) enhanced data analytics and theory, along with high-throughput synthesis and spatially resolved characterization, to identify the phase composition of complex metal-oxide thin films.
Controlling Oxide Catalysts with Peroxides
CFN Users at Binghamton University and CFN staff demonstrated they could control the activity and selectivity of an oxide catalyst (cupric oxide, CuO) using adsorbed oxygen.
Molecular Brushes Stabilize Nanoparticles in Extreme Solution Environments
CFN researchers and collaborators developed a series of molecular brushes that both functionalizes inorganic nanoparticles (gold, silver, platinum, and iron oxide) and stabilizes them in a diverse spectrum of solution environments.
