Polarization‐Sensitive and Self‐Driven Pyro‐Phototronic Photodetectors Based on MoS2‐Water Heterojunctions
Advanced Optical Materials(2024)SCI 2区
Abstract
Polarization-sensitive and self-driven pyroelectric-based photodetectors have recently gained interest due to their potential application in artificial electronic eyes, biomedical imaging, and optical switches. Here, a photodetector based on light modulation-induced polarization and depolarization of water molecules on the surface of a 2D MoS2 crystal is reported. The MoS2-water heterostructure photodetector serves as a self-driven pyro-phototronic device that converts light-induced thermal energy to electrical signals, leading to a transient photoresponsivity as high as 24.6 mA W-1 and a specific detectivity of 2.85 x 10(8) Jones under 470 nm wavelength at zero bias. Due to the formation of a built-in electric field at the MoS2-water interface, this structure also has a high steady-state responsivity of 3.62 A W-1 and detectivity of 9.18 x 10(8) Jones at 3 V bias, along with a fast response time of approximate to 0.74 ms. Moreover, due to the rearrangement of the hydrogen bond network in the liquid water upon visible light illumination, the MoS2-water photodetector is light polarization-sensitive. The simple fabrication process, low cost, polarization sensitivity, and high performance of the MoS2-water structure make it an excellent candidate for liquid-compatible photodetectors.
MoreTranslated text
Key words
interfacial pyroelectricity,liquid-semiconductor optoelectronic,MoS2-water photodetector,polarization-sensitive photodetector,pyro-phototronic photodetector
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
JOURNAL OF ALLOYS AND COMPOUNDS 2024
被引用0
ACS APPLIED MATERIALS & INTERFACES 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper