Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Atomic Force Microscopy01:08

Atomic Force Microscopy

3.4K
Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
3.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Bio-conjugated ultrabright fluorescent nanoparticles for targeted cancer-cell imaging: independent size control and brightness.

Nanoscale·2026
Same author

Long-Term Cell-Membrane-Coated Ultrabright Nanospheres for Targeted Cancer Cell Imaging and Hydrophobic Drug Delivery.

Chemistry of materials : a publication of the American Chemical Society·2026
Same author

Single ultrabright fluorescent silica nanoparticles can be used as individual fast real-time nanothermometers.

Materials horizons·2025
Same author

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.

Cells·2025
Same author

Ultrabright fluorescent particles <i>via</i> physical encapsulation of fluorescent dyes in mesoporous silica: a mini-review.

Nanoscale·2024
Same author

Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps.

Cells·2023

相关实验视频

Updated: Jul 1, 2025

Atomic Force Microscopy of Red-Light Photoreceptors Using PeakForce Quantitative Nanomechanical Property Mapping
14:13

Atomic Force Microscopy of Red-Light Photoreceptors Using PeakForce Quantitative Nanomechanical Property Mapping

Published on: October 24, 2014

11.7K

在机器学习分析原子力显微镜图像以进行图像分类,样本表面识别等方面.

I Sokolov1,2,3

  • 1Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA. Igor.Sokolov@Tufts.edu.

Physical chemistry chemical physics : PCCP
|March 13, 2024
PubMed
概括

机器学习 (ML) 对原子力显微镜 (AFM) 数据的分析提供了强大的见解,特别是在小数据集. 本研究探讨了超越深度学习的ML方法,用于对AFM图像进行分类,包括生物细胞和材料表面.

更多相关视频

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
05:04

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Published on: June 13, 2023

1.5K
Contact Mode Atomic Force Microscopy as a Rapid Technique for Morphological Observation and Bacterial Cell Damage Analysis
05:34

Contact Mode Atomic Force Microscopy as a Rapid Technique for Morphological Observation and Bacterial Cell Damage Analysis

Published on: June 30, 2023

1.4K

相关实验视频

Last Updated: Jul 1, 2025

Atomic Force Microscopy of Red-Light Photoreceptors Using PeakForce Quantitative Nanomechanical Property Mapping
14:13

Atomic Force Microscopy of Red-Light Photoreceptors Using PeakForce Quantitative Nanomechanical Property Mapping

Published on: October 24, 2014

11.7K
Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
05:04

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Published on: June 13, 2023

1.5K
Contact Mode Atomic Force Microscopy as a Rapid Technique for Morphological Observation and Bacterial Cell Damage Analysis
05:34

Contact Mode Atomic Force Microscopy as a Rapid Technique for Morphological Observation and Bacterial Cell Damage Analysis

Published on: June 30, 2023

1.4K

科学领域:

  • 表面科学和纳米技术
  • 生物物理和计算生物学
  • 材料科学与工程 材料科学与工程

背景情况:

  • 原子力显微镜 (AFM) 产生了表面物理化学性质的多维数据集.
  • 由于其复杂性和高维度,对AFM数据的传统分析具有挑战性.
  • 机器学习 (ML) 为分析AFM数据提供了一个有前途的途径,但深度学习方法通常需要大型数据集.

研究的目的:

  • 探索和介绍适合分析原子力显微镜 (AFM) 数据的机器学习 (ML) 方法,特别是在处理小数据库时.
  • 通过使用AFM成像来证明这些ML方法用于分类和识别任务的应用.
  • 为机场机组数据量身定制的ML分析提供一个一般框架,强调统计学意义.

主要方法:

  • 利用机器学习 (ML) 算法,专注于超越深度学习神经网络的方法,用于分析原子力显微镜 (AFM) 图像数据.
  • 开发并应用了一种针对飞行机组数据的ML分析的一般模板.
  • 包括对评估获得结果的统计学意义的重点,并为此目的描述了一种简单的方法.

主要成果:

  • 成功应用了ML方法来分析和分类生物细胞的表面,在有限的AFM图像数据中证明了有效性.
  • 验证了 ML 驱动的 AFM 分析在各种领域的潜力,包括医学成像,材料处理,法医科学和艺术认证.
  • 提供了使用拟议的ML方法识别细胞表型的实际例子.

结论:

  • 机器学习 (ML) 为分析来自原子力显微镜 (AFM) 的复杂多维数据提供了一种强大而易于使用的工具,即使使用小数据集.
  • 提出的ML方法在需要表面特征和分类的各种科学和技术领域中具有广泛的适用性.
  • 在ML分析中强调AFM数据的统计意义对于可靠和可解释的结果至关重要.