90% Yield Production of Spiropyran Covalently Grafted MXene‐Based RRAM Devices for Optoelectronic Dual‐Response Switching
ADVANCED OPTICAL MATERIALS(2024)
East China Univ Sci & Technol
Abstract
Over the past decade, great efforts have been devoted to both intellectual property protection and information security protection. As a promising electroactive material, MXene is covalently or non‐covalently functionalized with organic species/polymers to produce a large amount of novel functional materials for biomedicine, optoelectronics, and energy storage. By using spiropyran covalently functionalized MXene (MXene‐SP) as an active material, an optoelectronic dual‐response resistive random access memory (RRAM) device is successfully fabricated. Upon UV illumination, the active layer is changed from MXene‐SP (SP: ring‐closed spiropyran form) observed under the illumination of blue light to MXene‐MC (MC: ring‐opened merocyanine form) due to photo‐induced “close‐to‐open” isomerization. The as‐fabricated ITO/MXene‐SP/ITO device exhibits typical nonvolatile optoelectronic dual‐response RRAM performance, with a production yield exceeding 90%. The achieved switch‐on/off voltages are −1.25/2.07 V under the illumination of blue light and −0.58/0.91 V under UV illumination. The switching bias window (Δ| V ON − V OFF |) and the ON/OFF current ratio of MXene‐MC are 44.9% and 14.7% of these of MXene‐SP, respectively. By utilizing the window difference between the RRAM performance achieved under the illumination of different lights, one can easily encrypt the quick response code.
MoreTranslated text
Key words
information encryption,memory devices,MXene,spiropyran
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
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
Summary is being generated by the instructions you defined