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

pH01:24

pH

The potential of hydrogen (pH) is a measure of the acidity or basicity of a water-based solution determined by the concentration of hydronium ions (H3O+). In one liter of pure water at neutral pH, there are 1×10−7 moles of hydronium ions. However, the extensive range of hydronium ion concentrations present in water-based solutions makes measuring pH in moles cumbersome. Therefore, a pH scale was developed to convert moles of hydronium ions into the negative logarithm of the hydronium ion...

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

Integration of label-free electrochemical sensing, differential privacy deep learning, and blockchain for secure cytotoxicity monitoring.

Toxicology in vitro : an international journal published in association with BIBRA·2026
Same author

Smartphone-based colorimetric analysis of pH strips using machine learning.

Analytical methods : advancing methods and applications·2026
Same author

Bifunctional peptide-conjugated vertically aligned tubular nanofibrous conduits for enhanced vascularization and nerve regeneration.

Biomaterials advances·2026
Same author

Clinical Predictors of Ultrasound-Guided Cervical Medial Branch Pulsed Radiofrequency Outcomes: A Cohort Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Time-resolved, label-free electrochemical monitoring of neurotoxicity via differential pulse voltammetry.

Bioelectrochemistry (Amsterdam, Netherlands)·2026
Same author

Custom-formulated MWCNT inks for scalable fabrication of flexible screen-printed electrodes.

Nanotechnology·2025

相关实验视频

Updated: Jun 29, 2026

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

9.8K

基于机器学习的高精度色度pH量化,使用定制的便携式条形像设备和智能手机应用程序.

Ece Minel Bursalı1, Mehmet Akif Özdemir1, Mustafa Şen2

  • 1Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey.

Mikrochimica acta
|November 25, 2025
PubMed
概括

一个新的机器学习增强的pH传感器平台提供准确的,实时的,离线的pH监测. 这种便携式设备克服了传统带和智能手机系统的局限性,非常适合资源有限的设置.

关键词:
测色仪检测检测的颜色测量检测嵌入式硬件 嵌入式硬件机器学习是机器学习.这是一个智能手机应用程序.检测pH值的感应器

更多相关视频

Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone
07:41

Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone

Published on: September 8, 2023

4.9K
Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

2.0K

相关实验视频

Last Updated: Jun 29, 2026

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

9.8K
Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone
07:41

Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone

Published on: September 8, 2023

4.9K
Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

2.0K

科学领域:

  • 分析化学 分析化学
  • 传感器技术 传感器技术
  • 机器学习 机器学习

背景情况:

  • 传统的pH条带受到主观解释的影响.
  • 智能手机的pH传感受到相机和照明不一致的影响.
  • 需要准确,便携和可靠的pH监测解决方案.

研究的目的:

  • 开发一个紧的,机器学习增强的pH传感平台.
  • 克服现有的pH测量技术的局限性.
  • 为了实现准确的,实时的,离线的pH预测.

主要方法:

  • 开发了一个定制的ESP32-S3设备,具有可控制的LED照明.
  • 收集了787个样本的数据集,其pH值范围为0-14.
  • 从多个颜色空间 (RGB,HSV,CIELAB) 提取了45个统计特征.
  • 使用一个AutoML管道来选择一个ExtraTreesRegressor模型.

主要成果:

  • 通过ExtraTreesRegressor模型实现了高的确定系数 ([公式:参见文本] = 0.967).
  • 显著优于传统的基于RGB的方法 ([公式:参见文本] = 0.618).
  • 集成的Android应用程序通过Wi-Fi提供实时,离线的pH预测.

结论:

  • 开发的平台为pH监测提供了强大,准确和便携式的解决方案.
  • 消除了对智能手机摄像机和可变照明条件的依赖.
  • 非常适合在资源有限的环境中应用,需要精确的pH测量.