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115 results for “AFM”
Dataset for strain-localisation in helium implanted tungsten: CPFE, Laue diffraction, AFM
<p>The folder includes dataset for helium-implanted tungsten and pure tungsten. The data includes measurement of the following for both materials as observed experimentally, and predicted using crystal-plasticity simulations:</p> <p>1. Surface morphology of nano-indents,</p> <p>2. Lattice-distortions around and under nano-indents</p> <p>3. Computed geometrically necessary dislocations field around and under nano-indents in both materials</p> <p>4. Load-displacement curves</p> <p>Guidelines for using dataset:</p> <p>1. Extracting the folder will generate five individual folders</p> <p>2. In the AFM plots folder, use the matlab code and dataset in the the folder to generate the surface morphology of nano-indents.</p> <p>3. In the "CPFE implanted sample data" folder --> use "CPFE implanted matlab code" --> load "variables2" --> run the code (raw data is also provided in the folder)</p> <p>4. In the "CPFE unimplanted sample data" folder --> use "CPFE unimp matlab code" --> load "variables3" --> run the code (raw data is also provided in the folder)</p> <p>5. In the "Laue unimplanted data" folder --> use matlab code with relevant raw data provided in folder</p> <p>6. In the "Laue implanted data" folder --> use matlab code with relevant raw data provided in folder</p> <p>7. Excel sheet provides nano-indentation and CPFE predictions of load-displacement curves</p> <p>8. The folder "HR-EBSD code and data" includes related raw data and codes.</p>
RAW data - Boric acid modified polydopamine and nanocolumnar hydrogenated TiO2 nanocomposite with improved photocatalytic performance. Appl. Surf. Sci. 2025, 686, 162118. DOI: 10.1016/j.apsusc.2024.162118 - AFM - ICONBruker - CNBM UAM - BAPDA/H:TiO2
<p>This repository contains RAW data for the experiment described in the publication: Jakub Szewczyk, Tim Tjardts, Fabian Symalla, Igor Iatsunskyi, Franz Faupel, Cenk Aktas, Emerson Coy, Salih Veziroglu, Boric acid modified polydopamine and nanocolumnar hydrogenated TiO2 nanocomposite with improved photocatalytic performance. Applied Surface Science 2025, 686, 162118.</p> <p>The remaining data supporting this study's findings are available from the corresponding author upon reasonable request. The raw AFM data is posted here in the open-access model.</p> <p> </p> <p> </p>
Bending and Looping of long DNA by Polycomb repressive complex 2 revealed by AFM imaging in liquid
<p><span>Polycomb repressive complex 2 (PRC2) is a histone methyltransferase that methylates histone H3 at Lysine 27. PRC2 is critical for epigenetic gene silencing, cellular differentiation, and the formation of facultative heterochromatin. It can also promote or inhibit oncogenesis. Despite this importance, the molecular mechanisms by which PRC2 compacts chromatin are relatively understudied. Here, we visualized the binding of PRC2 to naked DNA in liquid at the single-molecule level using atomic force microscopy. Analysis of the resulting images showed PRC2, consisting of 5 subunits (EZH2, EED, SUZ12, AEBP2, and RBBP4), bound to a 2.5-kbp DNA with an apparent dissociation constant (<em>K</em><sub>d</sub><sup>App</sup>) of 150 ± 12 nM. PRC2 did not show sequence-specific binding to a region of high GC content (76%) derived from a CpG island embedded in such a long DNA substrate. At higher concentrations, PRC2 compacted DNA by forming DNA loops typically anchored by two or more PRC2 molecules. Additionally, PRC2 binding led to a 3-fold increase in the local bending of DNA's helical backbone and no evidence of DNA wrapping around the protein. We suggest that the bending and looping of DNA by PRC2, independent of PRC2's methylation activity, may contribute to heterochromatin formation and therefore epigenetic gene silencing. </span></p>
AFM Analysis of CEIT Samples
<p>AFM Analysis of CEIT Samples</p>
AFM Dataset on HeLa cells
<p>Dataset acquired using a NanoWizard III (Bruker, Santa Barbara, CA, USA) on 40 HeLa cells using a SAA-SPH-5UM AFM probe (Bruker, Santa Barbara, CA, USA).</p> <p>The dataset includes force maps (20 cells) and microrheological measurements (20 cells) acquired on HeLa cells.</p> <p>Additionally this dataset includes:</p> <p>- Force settings used to acquired the data</p> <p>- Data used to characterize the piezo lag</p> <p>- Data used to characterize the viscous drag</p> <p>- Results obtained from PyFMLab, MATLAB routines and JPKDP</p>
Bending and Looping of long DNA by Polycomb repressive complex 2 revealed by AFM imaging in liquid
Open the record for dataset details and reuse information.
Modulation of a protein-folding landscape revealed by AFM-based force spectroscopy notwithstanding instrumental limitations
Open the record for dataset details and reuse information.
AFM calibration - AFMBioMed Summer school 2020
<p>A 10 min video tutorial to perform proper AFM calibration.</p> <p>The video presents:</p> <ul> <li>Calibration for force measurements</li> <li>Explain both contact and non-contact calibration</li> <li>Provide a spring constant calibration process (Sader method)</li> <li>InvOLS calibration in liquid</li> </ul>
Mica sample substrate preparation for AFM - AFMBioMed Summer school 2020
<p>A 7 min video tutorial to prepare mica substrates for AFM.</p> <p>The video presents:</p> <ul> <li>How to cut mica sheets</li> <li>How to glue mica to teflon coated metallic support</li> </ul>
AFM-ice-Cu(111)
<p>Machine learning dataset of probe particle model CO-tip atomic force microscopy (AFM) simulation images of ice clusters on the Cu(111) surface and the corresponding atomic structure files.</p><p>The dataset consists of a total of 1837 different structures which are divided into training, validation, and test sets as 1469/110/258, and for each structure the simulations are performed 10 times with different randomized simulation parameters to yield a total of 18370 simulations. Each simulation consist of 15 images at different tip-sample distances at 0.1Å step.</p><p>The dataset is saved in a compressed .tar.gz archive. The decompressed archive has samples stored in the webdataset shard format. Each shard is a tar file with a number of samples. The tar files are named in the format `Ice-K-{param}_{set}_{shard}.tar`, where {param} number stands for the different sets of randomized simulation parameters, {set} stands for either train, validation, or test set, and {shard} is the shard number.</p><p>Each sample consists of a number of AFM simulation images and an xyz structure file. The image files are named in the format `{x}.{y}.png`, where {x} is the sample number, {y} is the index for the different height slices in the simulations. The height slices are numbered from 0 to 14, such that 0 is the farthest distance and 14 is the closest distance. Similarly, the corresponding xyz structure files are named `{x}.xyz`. The comment line (second line) in the xyz files has information about the parameters used for in the simulation for the sample.</p><p>The ice structures were obtained from a neural-network potential optimization, and subsequently the Hartree potentials for the structures were obtained through a density functional theory calculation using the optB86b-vdW density functional in Vienna Ab-initio Simulation Package. The AFM simulations utilize the Lennard-Jones force field for the Pauli repulsion and van der Waals interactions and tip convolution with the Hartree potential for the electrostatic interaction.</p>
AFM-ice-Au(111)-bilayer
<p>Machine learning dataset of probe particle model CO-tip atomic force microscopy (AFM) simulation images of bilayer ice clusters on the Au(111) surface and the corresponding atomic structure files.</p><p>The dataset consists of a total of 1198 different structures which are divided into training, validation, and test sets as 958/72/168, and for each structure the simulations are performed 10 times with different randomized simulation parameters to yield a total of 11980 simulations. Each simulation consist of 15 images at different tip-sample distances at 0.1Å step.</p><p>The dataset is saved in a compressed .tar.gz archive. The decompressed archive has samples stored in the webdataset shard format. Each shard is a tar file with a number of samples. The tar files are named in the format `Ice-K-{param}_{set}_{shard}.tar`, where {param} number stands for the different sets of randomized simulation parameters, {set} stands for either train, validation, or test set, and {shard} is the shard number.</p><p>Each sample consists of a number of AFM simulation images and an xyz structure file. The image files are named in the format `{x}.{y}.png`, where {x} is the sample number, {y} is the index for the different height slices in the simulations. The height slices are numbered from 0 to 14, such that 0 is the farthest distance and 14 is the closest distance. Similarly, the corresponding xyz structure files are named `{x}.xyz`. The comment line (second line) in the xyz files has information about the parameters used for in the simulation for the sample.</p><p>The ice structures were obtained from a neural-network potential optimization, and subsequently the Hartree potentials for the structures were obtained through a density functional theory calculation using the optB86b-vdW density functional in Vienna Ab-initio Simulation Package. The AFM simulations utilize the Lennard-Jones force field for the Pauli repulsion and van der Waals interactions and tip convolution with the Hartree potential for the electrostatic interaction.</p>
AFM-ice-Au(111)-monolayer
<p>Machine learning dataset of probe particle model CO-tip atomic force microscopy (AFM) simulation images of monolayer ice clusters on the Au(111) surface and the corresponding atomic structure files.</p><p>The dataset consists of a total of 2050 different structures which are divided into training, validation, and test sets as 1640/122/288, and for each structure the simulations are performed 10 times with different randomized simulation parameters to yield a total of 20500 simulations. Each simulation consist of 15 images at different tip-sample distances at 0.1Å step.</p><p>The dataset is saved in a compressed .tar.gz archive. The decompressed archive has samples stored in the webdataset shard format. Each shard is a tar file with a number of samples. The tar files are named in the format `Ice-K-{param}_{set}_{shard}.tar`, where {param} number stands for the different sets of randomized simulation parameters, {set} stands for either train, validation, or test set, and {shard} is the shard number.</p><p>Each sample consists of a number of AFM simulation images and an xyz structure file. The image files are named in the format `{x}.{y}.png`, where {x} is the sample number, {y} is the index for the different height slices in the simulations. The height slices are numbered from 0 to 14, such that 0 is the farthest distance and 14 is the closest distance. Similarly, the corresponding xyz structure files are named `{x}.xyz`. The comment line (second line) in the xyz files has information about the parameters used for in the simulation for the sample.</p><p>The ice structures were obtained from a neural-network potential optimization, and subsequently the Hartree potentials for the structures were obtained through a density functional theory calculation using the optB86b-vdW density functional in Vienna Ab-initio Simulation Package. The AFM simulations utilize the Lennard-Jones force field for the Pauli repulsion and van der Waals interactions and tip convolution with the Hartree potential for the electrostatic interaction.</p>
AFM data of ice clusters on Cu(111) and Au(111) in paper "Structure discovery in Atomic Force Microscopy imaging of ice"
<p>Frequency shift CO-tip atomic force microscopy data of small ice clusters on Cu(111) and Au(111) surfaces as they appear in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".</p><p>The data are saved in a compressed .tar.gz archive. The unpacked archive contains each experiment as a Numpy .npz file. Each file contains the measurement data as a 3D array in the key 'data' and the physical extent of the scan region in the x and y directions in Ånströms in the keys 'lengthX' and 'lengthY'.</p>
Figure 1 : Présentation des individus partiels d'AFM pour la comparaison des enquêtes (extraction des scores factoriels des 5 premiers axes)
<p>Figure 1 : Présentation des individus partiels d’AFM pour la comparaison des enquêtes (extraction des scores factoriels des 5 premiers axes).</p> <p>Il s'agit de l'image en haute définition de la figure 1 publiée p. 357 de l'article de Jardin A., Noble J., 2024, Enquêter sur l’insécurité et la victimation : une comparaison systématique des grands instruments français, <em>Déviance et Société</em>, 48, 3, 331‑364 | doi : 10.3917/ds.483.0331</p> <p> </p>
Gold nanoparticles interacting with synthetic lipid rafts: an AFM investigation
<p>In this work, Atomic Force Microscopy (AFM) is employed for obtaining the first direct proof that citrated gold nanoparticles (AuNPs) adsorb preferentially along the boundaries of lipid rafts. Multicomponent Supported Lipid Bilayers (SLBs) were used as synthetic model membranes to mimic the nanometric rafts that are known to characterize the plasma membrane. </p>
AFM-Based High-Throughput Nanomechanical Screening of Single Extracellular Vesicles
<p>I the present study, a novel AFM-based high throughput mechanical characterization for extracellular vesicles is presented. The method relies on the analysis of vesicles morphometry following their adsorption on surfaces. The deformation extent of the vesicle, which is representative of the vesicle stiffness, is evaluated by the calculation of the so-called vesicle contact angle. The method was applied to different etracellular vesicles (EVs) samples from different natural sources and was employed for spotting the presence of non-vesicular contaminants.</p>
Supplementary material 3 from: Vinasco AFM, Carrejo NSG (2016) Morphology and development rate of the immature stages of Glyphidops (Oncopsia) flavifrons (Bigot, 1886) (Diptera, Neriidae) under natural conditions. ZooKeys 603: 141-159. https://doi.org/10.3897/zookeys.603.7355
Egg hatching of Glyphidops (Oncopsia) flavifrons : Explanation note: Moments before the hatching, the larva is observed moving the body and the head, rubbing its mandibles against the inner wall of the egg and finally thrusting the corium. The larva emerges from the anterior part of the egg by using one of the two longitudinally lateral hatching lines.
Supplementary material 2 from: Vinasco AFM, Carrejo NSG (2016) Morphology and development rate of the immature stages of Glyphidops (Oncopsia) flavifrons (Bigot, 1886) (Diptera, Neriidae) under natural conditions. ZooKeys 603: 141-159. https://doi.org/10.3897/zookeys.603.7355
Mating and oviposition process of Glyphidops (Oncopsia) flavifrons : Explanation note: The body of each egg is buried in the substrate and the filament is spread over the surface. As the female began laying eggs (reaching packages up to 20 eggs, one egg at a time), it was observed that numerous filaments were emerging and radiating from the same point. Along this process, the male remains close to the female and the mating happen continuously between laid eggs.
Supplementary material 1 from: Vinasco AFM, Carrejo NSG (2016) Morphology and development rate of the immature stages of Glyphidops (Oncopsia) flavifrons (Bigot, 1886) (Diptera, Neriidae) under natural conditions. ZooKeys 603: 141-159. https://doi.org/10.3897/zookeys.603.7355
Cluster analysis of mandibular area and hypopharyngeal sclerite : Explanation note: Cluster analysis of mandibular area and hypopharyngeal sclerite length across time supports the concept of three larval instar (similarity indexes between 95% and 100%). (a) body length of the larva, (b) mandibular area and (c) length of hypopharyngeal sclerite. Each color represents a larval instar (Ln).
Liposome Mechanics Probed by AFM - HA and PBS filled liposomes with nonlinear force characteristics
<p>JPK files from AFM microscopy</p> <p> </p>
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