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相关实验视频

Updated: Jul 1, 2025

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

包装WGM MBR传感器用于使用基于CNN的多模条形码图像进行高性能温度测量.

Haiju Li, Yang Lu, Shengao Zhou

|March 5, 2024

概括

这项研究引入了一种使用卷积神经网络 (CNN) 与低声画廊模式 (WGM) 光学微共振传感器的新型温度测量方法. 该方法可以实现高精度和实时监控,用于精确的温度传感应用.

科学领域:

有光学传感器的感应器.
纳米技术纳米技术
机器学习是机器学习.

背景情况:

低语画廊模式 (WGM) 光学微振解器为温度测量提供高灵敏度和分辨率.
现有的WGM传感器在稳定性,单共振模式跟踪和实时监控方面面临挑战.

研究的目的:

使用WGM微共振传感器开发一种强大的实时温度测量方法.
利用卷积神经网络 (CNN) 来分析WGM传感器数据.

主要方法:

使用了一种低语画廊模式微瓶共振器 (WMBR) 传感器,配备了一种包装式微共振器-合结构 (包装式-MTCS).
采用卷积神经网络 (CNN) 来识别来自WGM传感器的多模条码图像.
开发了一个强大的包装,用于微共振器-渐变合结构.

主要成果:

在96°C的动态范围内,实现了 -14.28 pm/°C的高灵敏度和3.5 × 10−4 °C的分辨率.
证明了实时温度测量,平均检测精度为96.85%,每个图像的速度为0.68s.
展示了包装MTCS的稳定性和可靠性.

结论:

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Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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基于CNN的方法显著提高了WGM MBR传感器的性能,用于精确的温度测量.

这种方法克服了稳定性和实时监控方面的局限性,为先进的光学传感铺平了道路.

这些发现支持了通过热调节实现稳定的单离子微激发的可能性.