Rational Construction of a Cu–Cu <sub>2</sub> O–CuHCF Heterojunction Interface for High-Performance Potassium-Ion Sensing

The deployment of potassium ion (K<sup>+</sup>) sensors in demanding environments, such as marine resource utilization, energy storage systems, and industrial processes, is severely limited by the thermal instability of state-of-the-art sensing materials. Traditional ionophores like valinomycin and crown ethers irreversibly lose activity above 50 °C, rendering them unsuitable for applications involving significant temperature fluctuations. Herein, we report a breakthrough all-solid-state K<sup>+</sup> sensor engineered to overcome this fundamental limitation. The sensor architecture integrates a Cu<sub>2</sub>O semiconductor as an ion-electron transduction layer and a potassium-copper hexacyanoferrate (CuHCF(K)) active layer, forming a robust Cu-Cu<sub>2</sub>O-CuHCF heterojunction. First-principles density functional theory (DFT) calculations guided the rational design, predicting superior K<sup>+</sup> selectivity over Na<sup>+</sup> based on adsorption energetics and d-band center analysis. A one-step electrochemical synthesis protocol was developed to fabricate this heterostructure with comprehensive characterization (EIS, XPS, XRD, SEM) confirming its successful formation and interfacial properties. The sensor demonstrates exceptional analytical performance, operating reliably within a wide temperature range of 25-100 °C and maintaining high selectivity for K<sup>+</sup> in high-salinity seawater matrices. Furthermore, to bridge the gap between laboratory data and field deployment, we developed and open-sourced a Potential-Temperature-Concentration (PTC) machine learning model based on a back-propagation neural network. This work provides a new paradigm for designing robust electrochemical sensors for harsh environments, combining theoretical material design with intelligent data processing for practical industrial applications.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-02-18
DOI
https://doi.org/10.1021/acsami.5c25528
Primary Topic
Copper-based nanomaterials and applications
Type
article
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article

Rational Construction of a Cu–Cu 2 O–CuHCF Heterojunction Interface for High-Performance Potassium-Ion Sensing

Lian-Kui Wu, Xian-Ze Meng, Hao-Jie Yan, Hong-Ji Wan et al.
ACS Applied Materials & Interfaces
Copper-based nanomaterials and applications
article

Rational Construction of a Cu–Cu 2 O–CuHCF Heterojunction Interface for High-Performance Potassium-Ion Sensing

Lian-Kui Wu, Xian-Ze Meng, Hao-Jie Yan, Hong-Ji Wan, Fa-He Cao, Xin-Ran Li, Yang Li
article en

Abstract

The deployment of potassium ion (K<sup>+</sup>) sensors in demanding environments, such as marine resource utilization, energy storage systems, and industrial processes, is severely limited by the thermal instability of state-of-the-art sensing materials. Traditional ionophores like valinomycin and crown ethers irreversibly lose activity above 50 °C, rendering them unsuitable for applications involving significant temperature fluctuations. Herein, we report a breakthrough all-solid-state K<sup>+</sup> sensor engineered to overcome this fundamental limitation. The sensor architecture integrates a Cu<sub>2</sub>O semiconductor as an ion-electron transduction layer and a potassium-copper hexacyanoferrate (CuHCF(K)) active layer, forming a robust Cu-Cu<sub>2</sub>O-CuHCF heterojunction. First-principles density functional theory (DFT) calculations guided the rational design, predicting superior K<sup>+</sup> selectivity over Na<sup>+</sup> based on adsorption energetics and d-band center analysis. A one-step electrochemical synthesis protocol was developed to fabricate this heterostructure with comprehensive characterization (EIS, XPS, XRD, SEM) confirming its successful formation and interfacial properties. The sensor demonstrates exceptional analytical performance, operating reliably within a wide temperature range of 25-100 °C and maintaining high selectivity for K<sup>+</sup> in high-salinity seawater matrices. Furthermore, to bridge the gap between laboratory data and field deployment, we developed and open-sourced a Potential-Temperature-Concentration (PTC) machine learning model based on a back-propagation neural network. This work provides a new paradigm for designing robust electrochemical sensors for harsh environments, combining theoretical material design with intelligent data processing for practical industrial applications.

ACS Applied Materials & InterfacesVol. 18(8)
University of Science and Technology of China (CN), Anhui University (CN), Sun Yat-sen University (CN), City University of Hong Kong (HK), Chinese Academy of Sciences (CN), Hefei Institutes of Physical Science (CN), Institute of Reactor Materials (Russia) (RU)
Life below water
Openalex Percentile: Top 52%
Copper-based nanomaterials and applications
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