Bridging the Art and Science: A Symposium Honoring Prof. Luis M. Liz-Marzán for the Dong Qin ACS Award in Nanochemistry

This Nano Focus article summarizes the symposium honoring the inaugural Dong Qin ACS Award in Nanochemistry at the ACS Spring 2026 meeting. The event celebrated the first recipient, Prof. Luis M. Liz-Marzán, and honored the legacy of Prof. Dong Qin. Key themes included data-driven and machine-learning-assisted synthesis, the engineering of chiral and asymmetric nanostructures for biomedical sensing, therapeutic delivery at the nano-bio interface, and high-throughput single-particle characterization techniques. Ultimately, the symposium showcased a future in which nanochemistry is increasingly automated, predictive, and biologically integrated.

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Publication Details

Journal
ACS Nano
Published
2026-07-27
DOI
https://doi.org/10.1021/acsnano.6c12052
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Bridging the Art and Science: A Symposium Honoring Prof. Luis M. Liz-Marzán for the Dong Qin ACS Award in Nanochemistry

Yadong Yin, Yugang Sun, Jorge Pérez‐Juste
ACS Nano
Machine Learning in Materials Science
article

Bridging the Art and Science: A Symposium Honoring Prof. Luis M. Liz-Marzán for the Dong Qin ACS Award in Nanochemistry

Yadong Yin, Yugang Sun, Jorge Pérez‐Juste
article en

Abstract

This Nano Focus article summarizes the symposium honoring the inaugural Dong Qin ACS Award in Nanochemistry at the ACS Spring 2026 meeting. The event celebrated the first recipient, Prof. Luis M. Liz-Marzán, and honored the legacy of Prof. Dong Qin. Key themes included data-driven and machine-learning-assisted synthesis, the engineering of chiral and asymmetric nanostructures for biomedical sensing, therapeutic delivery at the nano-bio interface, and high-throughput single-particle characterization techniques. Ultimately, the symposium showcased a future in which nanochemistry is increasingly automated, predictive, and biologically integrated.

ACS Nano
Temple College (US), University of California System (US), Rede de Química e Tecnologia (PT), Universidade de Vigo (ES), Temple University (US)
Openalex Percentile: Top 20%
Machine Learning in Materials Science
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