Manometry Strategy Based on Excitation Spectral Shift Induced by Pressure-Controlled Intervalence Charge Transfer in Tb3+:LiTaO3
Ceramics International(2025)
Abstract
Luminescence manometer is of great practical importance when investigating the physical and chemical phenomena under the extreme condition of high pressure. In this work, we propose a manometry strategy based on pressure-induced spectral shift of an excitation band which offers great flexibility for switching the monitoring wavelengths, in contrast to the traditional strategies based on spectral shift of a specific emission band. A significant red-shift of the UV excitation band with maximal pressure sensitivity of 2.12 nm/GPa is observed in Tb3+:LiTaO3, which stems from the susceptibility of the metal-to-metal intervalence charge transfer to surrounding chemical environment. The abnormal intensity variation of emissions in the visual spectral range from 496 to 627 nm and the luminescence mechanism are investigated via studying the pressure dependent structural and spectroscopic properties. Given the widened pressure-monitoring spectral window, relatively intense luminescence signal and competitive pressure sensitivity, the Tb3+ activated LiTaO3 is demonstrated to be a promising candidate for luminescent manometer. The strategy based on varying excitations may provide an alternative path for developing high-performance optical manometers.
MoreTranslated text
Key words
luminescence manometry,excitation spectral shift,intervalence charge transfer,high-pressure
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