Designing Donor–Acceptor Interfaces with Built-In Electric Potential Using Hierarchically Grown NiTe–CeO<sub>2</sub> Heterostructure for Efficient Water Oxidation

Designing a non-noble metal based heterojunction electrocatalyst with outstanding catalytic activity and long-term stability is essential for efficient oxygen evolution reaction (OER). Herein, we have strategically designed a NiTe/CeO<sub>2</sub> heterojunction based on a donor-acceptor approach, wherein CeO<sub>2</sub> functions as the electron donor and NiTe as the electron acceptor. Kelvin probe force microscopy (KPFM) measurements revealed that the work functions of NiTe and CeO<sub>2</sub> facilitate the charge transfers upon interfacial coupling between NiTe and CeO<sub>2</sub>. This interfacial synergy induces the formation of a built-in electric field at the heterojunction, which effectively modulates the surface electronic structure of NiTe through strong interfacial interactions with CeO<sub>2</sub>, achieving an overpotential of 263 mV at 10 mA cm<sup>-2</sup> and a Tafel slope of 81 mV dec<sup>-1</sup> for the OER. The formation of the heterojunction led to an increased electrochemical active surface area (a <i>C</i><sub>dl</sub> value of 10.5 μF cm<sup>-2</sup>) and a reduced charge-transfer resistance (1 Ω), which results in improved turnover frequency (3 s<sup>-1</sup>) as compared to the bare counterpart. Through the distribution of relaxation time (DRT) analysis, the extracted time constants corresponding to different relaxation processes reveal an enhanced charge-transfer kinetics across the interfaces, enhancing OER kinetics.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-08-14
DOI
https://doi.org/10.1021/acsami.5c12080
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Designing Donor–Acceptor Interfaces with Built-In Electric Potential Using Hierarchically Grown NiTe–CeO2 Heterostructure for Efficient Water Oxidation

Priyanka Ray, Ankit Sharma, Mohammad Qureshi, Nageswara Rao Peela et al.
4 links
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Designing Donor–Acceptor Interfaces with Built-In Electric Potential Using Hierarchically Grown NiTe–CeO2 Heterostructure for Efficient Water Oxidation

Priyanka Ray, Ankit Sharma, Mohammad Qureshi, Nageswara Rao Peela, Peeyush Pandey, Ameer Suhail
article en

Abstract

Designing a non-noble metal based heterojunction electrocatalyst with outstanding catalytic activity and long-term stability is essential for efficient oxygen evolution reaction (OER). Herein, we have strategically designed a NiTe/CeO<sub>2</sub> heterojunction based on a donor-acceptor approach, wherein CeO<sub>2</sub> functions as the electron donor and NiTe as the electron acceptor. Kelvin probe force microscopy (KPFM) measurements revealed that the work functions of NiTe and CeO<sub>2</sub> facilitate the charge transfers upon interfacial coupling between NiTe and CeO<sub>2</sub>. This interfacial synergy induces the formation of a built-in electric field at the heterojunction, which effectively modulates the surface electronic structure of NiTe through strong interfacial interactions with CeO<sub>2</sub>, achieving an overpotential of 263 mV at 10 mA cm<sup>-2</sup> and a Tafel slope of 81 mV dec<sup>-1</sup> for the OER. The formation of the heterojunction led to an increased electrochemical active surface area (a <i>C</i><sub>dl</sub> value of 10.5 μF cm<sup>-2</sup>) and a reduced charge-transfer resistance (1 Ω), which results in improved turnover frequency (3 s<sup>-1</sup>) as compared to the bare counterpart. Through the distribution of relaxation time (DRT) analysis, the extracted time constants corresponding to different relaxation processes reveal an enhanced charge-transfer kinetics across the interfaces, enhancing OER kinetics.

ACS Applied Materials & InterfacesVol. 17(34)
Indian Institute of Technology Guwahati (IN), London Centre for Nanotechnology (GB), Applied Nanotech (United States) (US), Centre de Nanosciences et de Nanotechnologies (FR), CeNTI (Portugal) (PT)
Openalex Percentile: Top 82%
Advanced Memory and Neural Computing
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