Broad Dynamic Range with High Temperature Stability in Ultrathin CrPS<sub>4</sub> Nano‐Electromechanical Resonators
Owing to the exceptional mechanical and optical properties of atomically thin materials, nano-electro-mechanical systems (NEMS) resonators hold great promise for various sensing applications requiring high resolution and broad dynamic range (DR). However, these applications are often challenged by variations in parameters, including temperature, making the preservation of a stable and large dynamic range across different temperature ranges a big challenge. Here, it is reported that the ultra-thin mechanical resonators made of Chromium thiophosphate (CrPS<sub>4</sub>) at the fundamental frequency of up to 84 MHz, exhibit large dynamic range and remarkable stability over a broad temperature range. The characterization encompasses intrinsic Brownian thermomechanical vibrations with noise levels as low as 9.98 fm/Hz<sup>-1/2</sup> and Duffing responses, showing a maximal dynamic range of up to 106 dB at liquid helium temperature. Notably, it is observed that the device can maintain this expansive dynamic range over a temperature range of 100 K. Based on these properties it is estimated that the resonator has a high mass resolution of 1.51 × 10<sup>-24</sup> g. These results suggest the potential for new applications of NEMS resonators including ultrasensitive sensing and signal processing under extreme conditions.
Authors
- Qiang Zhou (ORCID: https://orcid.org/0000-0001-7099-1995)
- Guang‐Can Guo (ORCID: https://orcid.org/0000-0003-3771-9185)
- Menglin Guo
- Jiawei Fang (ORCID: https://orcid.org/0000-0002-2448-8376)
- Bo-Lin Li (ORCID: https://orcid.org/0000-0003-0452-6950)
- Xiaobin Niu (ORCID: https://orcid.org/0000-0002-5348-3013)
- Guangwei Deng (ORCID: https://orcid.org/0000-0001-9761-3986)
- K. Yu. Arutyunov (ORCID: https://orcid.org/0000-0001-9373-5185)
- Jiang Wu (ORCID: https://orcid.org/0000-0003-0679-6196)
- Zhiming M. Wang (ORCID: https://orcid.org/0000-0001-7847-7704)
- Junfan Chen (ORCID: https://orcid.org/0009-0004-4199-6147)
- Haizhi Song (ORCID: https://orcid.org/0000-0002-5527-0775)
- You Wang
- Jun Zhou (ORCID: https://orcid.org/0000-0001-5394-9787)
Institutions
- National Research University Higher School of Economics (RU)
- University of Science and Technology of China (CN)
- University of Electronic Science and Technology of China (CN)
- P.L. Kapitza Institute for Physical Problems (RU)
- Southwestern Institute of Physics (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- Small
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1002/smll.202408291
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- National Natural Science Foundation of China
- National Research University Higher School of Economics
- National Key Research and Development Program of China