Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains-Reference-Cited by-同舟云学术

Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Published:2023 Issue:3 Volume:19 Page:451-467
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

McCraw Marshall R.¹^ORCID,Uluutku Berkin¹^ORCID,Solomon Halen D.¹^ORCID,Anderson Megan S.¹^ORCID,Sarkar Kausik¹^ORCID,Solares Santiago D.¹^ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering, The George Washington University School of Engineering and Applied Science, Washington, District of Columbia, USA

Abstract

The range of observable time-dependent mechanical behavior in AFM FD experiments is limited by sampling frequency 1/Δt and duration of the experiment tf. One should choose parameters that encompass both the minimum and maximum material relaxation times.

Funder

National Science Foundation

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SM/D2SM01331B

Reference105 articles.

1. Actomyosin Cortical Mechanical Properties in Nonadherent Cells Determined by Atomic Force Microscopy

2. Nanomechanical Property Maps of Breast Cancer Cells As Determined by Multiharmonic Atomic Force Microscopy Reveal Syk-Dependent Changes in Microtubule Stability Mediated by MAP1B

3. Drug-Induced Changes of Cytoskeletal Structure and Mechanics in Fibroblasts: An Atomic Force Microscopy Study

4. Direct measurement of the lamellipodial protrusive force in a migrating cell

5. Cellular nanoscale stiffness patterns governed by intracellular forces

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning assisted multifrequency AFM: Force model prediction;Applied Physics Letters;2023-12-04