ANALYTICAL CHEMISTRY
·
2024
·
IF 6.7
Dailu Jia,Chao Lei,Wei Ren,Chenghui Liu
In this work, we report a new generation of single microbead bioassay that employs a single BaTiO3 microbead as an optical booster for target biomarker enrichment and optical enhancement toward protein and nucleic acid analysis. The single BaTiO3 microbead can not only concentrate the target molecules by nearly 104-fold but also act as an optical booster to prominently enhance the target-induced fluorescence signal by the whispering gallery mode for improving the excitation efficiency and the microlens effect for promoting the signal collecting efficiency, respectively. Compared with using a conventional single microbead, this optical booster exhibits nearly 2 orders of magnitude higher sensitivity without the assistance of any signal amplification techniques or costly instruments. Moreover, this single microbead optical booster is capable of detecting different kinds of protein and nucleic acid biomarkers in a simple mix-and-read manner, holding great potential for early clinical diagnosis.
CAS / MDL:
研究领域:
生物传感
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光学技术
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分子诊断
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产品关联
DOI
PubMed
高影响因子
DALTON TRANSACTIONS
·
2023
·Vol. 52
·期 44
·16421-16432
·
IF 4
Xiaoyi Liu,Haiming Cheng,Hu Wang,Zhu Wen,Guixia Liu,Shengda Liu,Dan Li,Jinxian Wang,Wensheng Yu,Xiangting Dong
Herein, a new organic cationic matrix [N(CH3)4]3MoO3F3 suitable for Mn4+ doping was constructed. Due to the large steric hindrance of N[CH3]4+ (TMA), charge compensation defects can be effectively prevented in the heterovalent Mn4+-doping process, and a high IQE (91.05%) was obtained. Through the cation co-doping strategy, Mg2+/Zn2+/Li+ cations were introduced into the Mo6+ cationic site, which improved the crystallinity of the matrix and reduced energy losses, so as to improve luminescence intensity, QE, thermal stability, water stability and other spectral properties. Meanwhile, [N(CH3)4]2TiF6:Mn4+ phosphors with the same TMA organic cation and equivalent Mn4+ doping were synthesized for comparison, and the effects of the Mg2+ cation co-doping strategy on the spectral properties of phosphors with different matrix types (fluoride/oxyfluoride) and substitution types (equivalent/non-equivalent) were analyzed. These findings provide the basis for the preparation of new luminescent materials. Furthermore, according to the optical properties exhibited by these phosphors, they are packaged into WLED devices with excellent photoelectric properties, which are suitable for indoor lighting and display fields.
研究领域:
材料科学
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光学
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光子学
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发光材料
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DOI
高影响因子
InfoMat
·
2023
·Vol. 5
·期 4
·e12399
·
IF 22.7
Dingchen Wang,Dingyao Liu,Yinan Lin,Anran Yuan,Woyu Zhang,Yaping Zhao,Shaocong Wang,Xi Chen,Hegan Chen,Yi Zhang,Yang Jiang,Shuhui Shi,Kam Chi Loong,Jia Chen,Songrui Wei,Qing Wang,Hongyu Yu,Renjing Xu,Dashan Shang,Han Zhang,Shiming Zhang,Zhongrui Wang
Autonomous one-shot on-the-fly learning copes with the high privacy, small dataset, and in-stream data at the edge. Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottlenecks. The optical neural networks featuring large parallelism, low latency, and high efficiency offer a promising solution. However, ex-situ training of conventional optical networks, where optical path configuration and deep learning model optimization are separated, incurs hardware, energy and time overheads, and defeats the advantages in edge learning. Here, we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model (HOWM), manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical reactions. We first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification, association and denoising. We then leveraged HOWM to function as a ternary content addressable memory (TCAM) of an optical memory augmented neural network (MANN) for one-shot learning the Omniglot dataset. The HOWM empowered one-shot on-the-fly edge learning leads to 1000× boost of energy efficiency and 10× boost of speed, which paves the way for the next-generation autonomous, efficient, and affordable smart edge systems.
研究领域:
光学
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机器学习
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神经网络
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材料科学
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边缘计算
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产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）