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Stephan Link

Stephan Link

· Charles W. and Genevieve M. Walton Endowed Professor of Chemistry, and Professor of Chemistry

University of Illinois Urbana-Champaign · Chemistry

Active 1994–2026

h-index76
Citations35.2k
Papers27370 last 5y
Funding$3.9M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Professor Stephan Link is the Charles W. and Genevieve M. Walton Endowed Professor of Chemistry and Professor of Electrical & Computer Engineering at the University of Illinois. His research focuses on coupled nanostructures, hybrid plasmonic interfaces, and the dynamics of plasmon, hot carrier, and exciton decay. He is involved in exploring the fundamental mechanisms and applications of these nanostructures in various scientific and technological contexts.

Research topics

  • Materials science
  • Nanotechnology
  • Optoelectronics
  • Physics
  • Optics
  • Molecular physics
  • Chemistry
  • Chemical physics
  • Atomic physics
  • Computer Science

Selected publications

  • Bottom-up carbon dots: purification, single-particle dynamics, and electronic structure

    Chemical Science · 2025-01-01 · 49 citations

    reviewOpen access

    -networked carbon and core-surface energy transfer, and heterogeneities, due to the unpredictable location of heteroatoms and often non-crystalline structure. Here we focus our review on three aspects of these systems: (1) coupling characterization with bottom-up synthesis to identify and remove confounding byproducts such as small molecules or hydrogen-rich polymers; (2) single-particle characterization to obtain unambiguous information on carbon dots and highlight the distribution of propertie…

  • Single-Particle Correlated Imaging Reveals Multiple Chromophores in Carbon Dot Fluorescence

    Journal of the American Chemical Society · 2025-05-16 · 12 citations

    articleSenior authorCorresponding

    Carbon dots are remarkable nanomaterials with many applications, but the sources of their emission are still uncertain. Carbon dots exhibit complex behaviors such as excitation-dependent emission due to their heterogeneous composition and structure. Most studies have been carried out on the ensemble level, where sample heterogeneity remains hidden. Understanding the complex emission of carbon dots requires single-particle measurements. Here, we determined that for red-emitting carbon dots made f…

  • Chemical Interface Damping Revealed by Single-Particle Absorption Spectroscopy

    ACS Nano · 2025-03-04 · 12 citations

    articleSenior authorCorresponding

    Plasmon-induced interfacial charge separation is a promising way to efficiently extract energetic carriers through direct plasmon decay. This mechanism of charge transfer has been investigated by single-particle scattering spectroscopy, which measures the homogeneous plasmon line width. The line width is broadened by charge transfer, generally known as chemical interface damping. However, conflicting reports exist regarding the effect of chemical interface damping on the corresponding single-par…

  • Plasmonic pathway to hybrid nanomaterials through energy transfer

    Science Advances · 2025-10-10 · 8 citations

    articleOpen accessCorresponding

    Plasmon-induced resonance energy transfer (PIRET) is a promising approach for plasmonic photocatalysis and energy conversion, but challenges include elucidating the mechanism and maximizing its efficiency, both of which are hampered by competing processes. Another challenge is demonstrating that PIRET can photoinitiate reactions that follow efficient pathways compared to bulk processes. We report a plasmon-induced route to plasmonic-polymer hybrid nanomaterials using in operando single-particle…

  • Machine Learning to Adaptively Predict Gold Nanorod Sizes on Different Substrates

    The Journal of Physical Chemistry C · 2025-03-18 · 4 citations

    articleSenior authorCorresponding

    Correlating a nanoparticle’s morphology with its optical properties is essential and is achieved by a combination of electron microscopy and optical spectroscopy. Machine learning has gained attention for enhancing in situ measurements and enabling inverse nanoparticle design. However, new training data for each specific condition are often required when testing data differ from training data. We propose a method to adapt existing training data for predicting the size of gold nanorods (AuNRs) on…

Recent grants

Frequent coauthors

Labs

  • Link Research GroupPI

Awards & honors

  • Fellow, American Association for the Advancement of Science…

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