Afm Imaging Verification
Afm Imaging Verification - Related Videos
08:21
Quantitative Hardness Measurement by Instrumented AFM-indentation
0 Views •
Cited by 6 •
2016
This experimental protocol describes how atomic force microscopy can be used to measure hardness at the true nanometer scale and to detect single atomistic plasticity events.
09:47
AFM and Microrheology in the Zebrafish Embryo Yolk Cell
0 Views •
Cited by 3 •
2017
The lack of tools to measure material properties and tensional parameters in vivo prevents validating their roles during development. We employed atomic force microscopy (AFM) and nanoparticle-tracking to quantify mechanical features on the intact zebrafish embryo yolk cell during epiboly. These methods are reliable and widely applicable avoiding intrusive interventions.
03:00
Self-Evaluation: Self-Enhancement and Self-Verification
0 Views •
2020
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
01:17
Strategies of Self-Presentation II: Self-Verification
0 Views •
2025
Self-verification is a fundamental psychological drive wherein individuals seek affirmation of their self-concept from others, striving for consistency between their internal self-view and external perceptions. This drive operates even when the self-concept is negative, influencing interpersonal behavior and feedback preferences in complex and often counterintuitive ways. Unlike the self-enhancement motive, which seeks positive evaluations, self-verification prioritizes coherence and...
08:29
Au-Interaction of Slp1 Polymers and Monolayer from Lysinibacillus sphaericus JG-B53 - QCM-D, ICP-MS and AFM as Tools for Biomolecule-metal Studies
0 Views •
Cited by 12 •
2016
To obtain basic information on the sorption and recycling of gold from aqueous systems the interaction of Au(III) and Au(0) nanoparticles on S-layer proteins were investigated. The sorption of protein polymers was investigated by ICP-MS and that of proteinaceous monolayers by QCM-D. Subsequent AFM enables the imaging of the nanostructures.
FAQs
Probe calibration helps determine whether observed nanoscale features reflect the sample or the probe itself. A probe with unsuitable or altered characteristics can produce tip convolution, which broadens or reshapes features in the image. Checking the probe before analysis therefore supports more reliable interpretations of cell surfaces, membranes, biomaterials, and engineered nanostructures.
Scanner calibration establishes confidence that measured image dimensions correspond to the sample, while drift can shift features during acquisition. Noise may obscure or mimic surface structure, and feedback distortion can alter recorded topography when the system does not track changes appropriately. Evaluating these effects helps separate genuine morphology from limitations introduced by image collection.
Repeat scans test whether the same features appear consistently under controlled imaging conditions. Features that remain stable across scans are more credible than structures seen only once, especially when drift, noise, or feedback effects could influence the image. This comparison also exposes poor reproducibility and supports more defensible quantitative measurements of surface morphology and roughness.
A verification workflow begins with probe and scanner calibration, followed by imaging under controlled conditions. Researchers then inspect scans for tip convolution, drift, noise, and feedback distortion, and compare repeat images for consistency. Only after these checks should they interpret morphology, roughness, or mechanical-related surface changes, because acquisition artifacts can otherwise be mistaken for sample properties.
Verified images can support analysis of nanoscale morphology and surface roughness, as well as evaluation of mechanical-related surface changes. These measurements are relevant to cell surfaces, biomaterials, membranes, and engineered nanostructures. Verification does not remove the need for careful interpretation, but it improves confidence that measured patterns arise from the sample rather than image-processing or instrument limitations.
The practice is especially valuable when researchers compare biological or material surfaces across samples, conditions, or experiments. Reliable verification helps determine whether apparent changes are genuine rather than caused by probe behavior, scanner performance, drift, noise, or feedback distortion. As a result, it strengthens reproducibility and makes quantitative comparisons of nanoscale features more defensible.