Electrochemical upcycling of spent ITO via charge-induced deconstruction

R Rongcen Zhao X Xiaotao Lv Z Zepeng Lv S Shaolong Li Y Yong Fan (Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania) J Jilin He (Laboratory for Synthetic Chemistry and Chemical Biology Limited) J Jianxun Song

Abstract

Abstract Spent indium tin oxide (s-ITO, the mass ratio of In 2 O 3 to SnO 2 is approximately 9:1) represents the main secondary resource for the rare metal indium, possessing exceptionally potential for reutilization. However, traditional hydrometallurgical or pyrometallurgical recycling methods grapple with substantial challenges, including low product purity, lengthy processes, and environmental pollution. This paper presents an electrochemical upcycling strategy for spent ITO based on charge-induced deconstruction, effectively addressing the aforementioned issues. In this approach, spent ITO serves as the anode in an aqueous electrolysis system. The number of oxygen vacancies on the ITO surface increases drastically under charge excitation, leading to weakened metal-oxygen bonds and the subsequent release of indium and tin ions into the electrolyte. These ions then migrate to the cathode, where high-purity indium tin alloy is obtained by leveraging the differences in electrochemical properties between impurity ions and the target metal ions. Notably, the electrolyte employed in this process can be reused, which significantly reduces wastewater generation. Through systematic optimization of electrolysis parameters, a cathode current efficiency of 71.12% and an energy consumption of 4.87 kWh kg āˆ’1 are achieved in a hundred-ampere scale trial, fully demonstrating the industrial applicability of this method.

Article Details

Volume / Issue Vol. 17, Issue 1
Published July 28, 2026
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (7)

R

Rongcen Zhao

X

Xiaotao Lv

Z

Zepeng Lv

S

Shaolong Li

Y

Yong Fan

Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania

J

Jilin He

Laboratory for Synthetic Chemistry and Chemical Biology Limited

J

Jianxun Song