4.8 Article

Differential-Concentration Scanning Ion Conductance Microscopy

期刊

ANALYTICAL CHEMISTRY
卷 89, 期 22, 页码 12458-12465

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.7b03543

关键词

-

资金

  1. Leverhulme Trust
  2. EPSRC through the MOAC DTC [EP/F500378/1]
  3. Warwick-China Scholarship Council
  4. Royal Society Wolfson Research Merit Award
  5. Engineering and Physical Sciences Research Council [1229084, 1358136] Funding Source: researchfish

向作者/读者索取更多资源

Scanning ion conductance microscopy (SICM) is a nanopipette-based scanning probe microscopy technique that utilizes the ionic current flowing between an electrode inserted inside a nanopipette probe containing electrolyte solution, and a second electrode placed in a bulk electrolyte bath, to inform on a substrate of interest. For most applications to date, the composition and concentration of the electrolyte inside and outside the nanopipette is identical, but it is shown herein that it can be very beneficial to lift this restriction. In particular, an ionic concentration gradient at the end of the nanopipette, generates an ionic current with a greatly reduced electric field strength, with particular benefits for live cell imaging. This differential concentration mode of SICM (Delta C-SICM) also enhances surface charge measurements and provides a new way to carry out reaction mapping measurements at surfaces using the tip for simultaneous delivery and sensing of the reaction rate. Comprehensive finite element method (FEM) modeling has been undertaken to enhance understanding of SICM as an electrochemical cell, and to enable the interpretation and optimization of experiments. It is shown that electroosmotic flow (EOF) has much more influence on the nanopipette response in the Delta C-SICM configuration compared to standard SICM modes. The general model presented advances previous treatments, and provides a framework for quantitative SICM studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据