Machine Learning Approach to Characterize the Adhesive and Mechanical Properties of Soft Polymers Using PeakForce Tapping AFM-Reference-Cited by-同舟云学术

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Machine Learning Approach to Characterize the Adhesive and Mechanical Properties of Soft Polymers Using PeakForce Tapping AFM

Published:2022-09-21 Issue:19 Volume:55 Page:8731-8740
ISSN:0024-9297
Container-title:Macromolecules
language:en
Short-container-title:Macromolecules

Author:

Rajabifar Bahram¹²^ORCID,Meyers Gregory F.³,Wagner Ryan¹²^ORCID,Raman Arvind¹²^ORCID

Affiliation:

1. School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, Indiana 47907, United States

2. Birck Nanotechnology Center, 1205 W. State Street, West Lafayette, Indiana 47907, United States

3. Analytical Sciences, The Dow Chemical Company, 1897 Building, Midland, Michigan 48667, United States

Funder

National Science Foundation

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Inorganic Chemistry,Polymers and Plastics,Organic Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.macromol.2c00147

Reference32 articles.

1. Nanomechanical mapping of soft materials with the atomic force microscope: methods, theory and applications

2. Force measurements with the atomic force microscope: Technique, interpretation and applications

3. Measurement of Viscoelastic Loss Tangent with Contact Resonance Modes of Atomic Force Microscopy

4. Quantitative Viscoelastic Mapping of Polyolefin Blends with Contact Resonance Atomic Force Microscopy

5. Pulsed contact resonance for atomic force microscopy nanomechanical measurements

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