Afm Data Processing
Afm Data Processing - Related Videos
08:21
Quantitative Hardness Measurement by Instrumented AFM-indentation
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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
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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.
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
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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.
08:15
Data Communication Based on MQTT in a Polymer Extrusion Process
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Cited by 1 •
2022
This work proposes a flexible method for data communication between a film extrusion system and monitoring devices based on a message protocol called Message Queuing Telemetry Transport (MQTT).
08:12
An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
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Cited by 11 •
2024
This protocol is a beginner's entryway into processing, fitting, and interpreting transient absorption spectra. The focus of this protocol is the preparation of datasets, and fitting using both single wavelength kinetics and global lifetime analysis. Challenges associated with transient absorption data and its fitting are discussed.
FAQs
Calibration establishes the relationship between measured probe deflection, scanner movement, and the quantities represented in the dataset. Without these corrections, apparent height or force values may not be quantitatively comparable. Accurate calibration therefore supports estimates of biomolecular dimensions, adhesion, and mechanical properties rather than relying only on visual patterns in an image.
Line-by-line flattening addresses background tilt that can make a surface appear sloped or uneven for computational reasons. The correction improves the baseline across scanned lines, allowing genuine topographic variation and surface roughness to remain distinguishable from background geometry. In biochemical samples, this separation is important when comparing molecular organization across regions or experimental conditions.
Artifact removal and filtering solve different problems. Artifact removal targets features introduced during measurement or scanning, whereas controlled filtering reduces unwanted variation in the signal. Filtering must remain limited because excessive smoothing can erase meaningful topographic or force information. Keeping these operations separate helps preserve structural details while improving readability and quantitative interpretation of processed AFM data.
A basic workflow starts with calibration of deflection and scanner movement, followed by line-by-line flattening to correct background tilt. The dataset can then undergo artifact removal and controlled filtering, with each adjustment checked against the raw signal. This sequence moves from measurement correction toward cleanup while reducing the risk that processing choices create misleading molecular or material features.
AFM Data Processing can support analysis of proteins, membranes, nucleic acids, and cells by preserving information needed to quantify biomolecular dimensions, surface roughness, adhesion, and mechanical properties. These measurements provide more than an image alone: they help characterize molecular organization and interactions and evaluate how those features change under different experimental conditions.
Consistent AFM Data Processing improves comparison between datasets by applying calibration, flattening, artifact removal, and filtering in a controlled manner. This consistency helps distinguish actual changes in molecular organization, surface roughness, adhesion, or mechanical properties from differences introduced by background tilt or processing choices. As a result, biochemistry experiments can interpret condition-dependent changes with greater confidence.