A Flexible Humidity and Temperature Sensor Based on Calligraphy Itself for Calligraphy Conservation Application
Journal of Materials Science Materials in Electronics(2023)
Donghua University | The Ohio State University | Shanghai Jiao Tong University Affiliated Sixth People’s Hospital
Abstract
Calligraphy preservation requires strict humidity and temperature environment, however, there are feeling differences between calligraphy and currently used sensors. Herein, a novel strategy is proposed to employ calligraphy itself (composed of paper and carbonic ink) as a sensor instead of sensitive components based on metals or semiconductors to eliminate sensing deviations. Carbonic ink can be decorated on paper substrate through dip coating or hand writing and the as-prepared composite calligraphy (CIP) is served as multifunctional sensor for humidity and temperature monitoring. The CIP demonstrates an admirable sensitivity to water molecules, showing a high humidity sensitivity of 506.36%/RH in the range of 94–100%RH and liquid water sensitivity of 17,680%. A linear negative temperature coefficient dependence of CIP as a temperature sensor is observed, and it exhibits outstanding performances including admirable temperature sensitivity of −3.13%/°C and prominent stability. As a result, CIP with accurate humidity and temperature sensing capability based on carbonic ink and paper as sensing element and flexible substrate shows brilliant possibility in calligraphy conservation.
MoreTranslated text
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
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