Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Measuring Lattice Strain in Three Dimensions through Electron Microscopy.

ABSTRACT: The three-dimensional (3D) atomic structure of nanomaterials, including strain, is crucial to understand their properties. Here, we investigate lattice strain in Au nanodecahedra using electron tomography. Although different electron tomography techniques enabled 3D characterizations of nanostructures at the atomic level, a reliable determination of lattice strain is not straightforward. We therefore propose a novel model-based approach from which atomic coordinates are measured. Our findings demonstrate the importance of investigating lattice strain in 3D.

SUBMITTER: Goris B

PROVIDER: S-EPMC4877113 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Measuring Lattice Strain in Three Dimensions through Electron Microscopy.

Goris Bart B De Beenhouwer Jan J De Backer Annick A Zanaga Daniele D Batenburg K Joost KJ Sánchez-Iglesias Ana A Liz-Marzán Luis M LM Van Aert Sandra S Bals Sara S Sijbers Jan J Van Tendeloo Gustaaf G

Nano letters 20150909 10

The three-dimensional (3D) atomic structure of nanomaterials, including strain, is crucial to understand their properties. Here, we investigate lattice strain in Au nanodecahedra using electron tomography. Although different electron tomography techniques enabled 3D characterizations of nanostructures at the atomic level, a reliable determination of lattice strain is not straightforward. We therefore propose a novel model-based approach from which atomic coordinates are measured. Our findings de ...[more]

PMID: 26340328

Similar Datasets

Spatial Transcriptomics correlated Electron Microscopy

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-06-21 | GSE202638 | GEO

Spatial Transcriptomics correlated Electron Microscopy [MERFISH]

Project description:Current spatial transcriptomics methods provide molecular and spatial information but no morphological readout. Here, we present STEM - a method that correlates multiplexed error-robust FISH with electron microscopy from neighboring tissue sections of the same sample. STEM links transcriptional and spatial organization of single cells with ultrastructural morphology of the tissue in vivo. Using STEM to characterize demyelinated white-matter lesions allowed us to link morphology of myelin-laden foamy microglia to transcriptional signature. Moreover, we revealed that interferon-response microglia have unique morphology and are enriched near CD8 T-cells.

2023-06-21 | GSE202623 | GEO

Spatial Transcriptomics correlated Electron Microscopy [scRNA-Seq]

Project description:Current spatial transcriptomics methods identify cell types and states in a spatial context but lack morphological information. Electron microscopy, in contrast, provides structural details at nanometer resolution without decoding the diverse cellular states and identity. STEM address this limitation by correlating multiplexed error-robust FISH with electron microscopy from adjacent tissue sections. Using STEM to characterize demyelinated lesions in mice, we were able to bridge spatially resolved transcriptional data with morphological information on cell identities. This approach allowed us to link the morphology of foamy microglia and interferon-response microglia with their transcriptional signatures.

2023-06-21 | GSE202636 | GEO

u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions.

Project description:We describe u-track3D, a software package that extends the versatile u-track framework established in 2D to address the specific challenges of 3D particle tracking. First, we present the performance of the new package in quantifying a variety of intracellular dynamics imaged by multiple 3D microcopy platforms and on the standard 3D test dataset of the particle tracking challenge. These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability that automatically defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track3D provides a complete framework for unbiased studies of molecular processes in complex volumetric sequences.

| S-EPMC10783629 | biostudies-literature

In vivo deconvolution acoustic-resolution photoacoustic microscopy in three dimensions.

Project description:Acoustic-resolution photoacoustic microscopy (ARPAM) provides a spatial resolution on the order of tens of micrometers, and is becoming an essential tool for imaging fine structures, such as the subcutaneous microvasculature. High lateral resolution of ARPAM is achieved using high numerical aperture (NA) of acoustic transducer; however, the depth of focus and working distance will be deteriorated correspondingly, thus sacrificing the imaging range and accessible depth. The axial resolution of ARPAM is limited by the transducer's bandwidth. In this work, we develop deconvolution ARPAM (D-ARPAM) in three dimensions that can improve the lateral resolution by 1.8 and 3.7 times and the axial resolution by 1.7 and 2.7 times, depending on the adopted criteria, using a 20-MHz focused transducer without physically increasing its NA and bandwidth. The resolution enhancement in three dimensions by D-ARPAM is also demonstrated by in vivo imaging of the microvasculature of a chick embryo. The proposed D-ARPAM has potential for biomedical imaging that simultaneously requires high spatial resolution, extended imaging range, and long accessible depth.

| S-EPMC4771455 | biostudies-other

Spatial Transcriptomics correlated Electron Microscopy

Project description:Spatial Transcriptomics correlated Electron Microscopy

| PRJNA836860 | ENA

Measuring the Autocorrelation Function of Nanoscale Three-Dimensional Density Distribution in Individual Cells Using Scanning Transmission Electron Microscopy, Atomic Force Microscopy, and a New Deconvolution Algorithm.

Project description:Essentially all biological processes are highly dependent on the nanoscale architecture of the cellular components where these processes take place. Statistical measures, such as the autocorrelation function (ACF) of the three-dimensional (3D) mass-density distribution, are widely used to characterize cellular nanostructure. However, conventional methods of reconstruction of the deterministic 3D mass-density distribution, from which these statistical measures can be calculated, have been inadequate for thick biological structures, such as whole cells, due to the conflict between the need for nanoscale resolution and its inverse relationship with thickness after conventional tomographic reconstruction. To tackle the problem, we have developed a robust method to calculate the ACF of the 3D mass-density distribution without tomography. Assuming the biological mass distribution is isotropic, our method allows for accurate statistical characterization of the 3D mass-density distribution by ACF with two data sets: a single projection image by scanning transmission electron microscopy and a thickness map by atomic force microscopy. Here we present validation of the ACF reconstruction algorithm, as well as its application to calculate the statistics of the 3D distribution of mass-density in a region containing the nucleus of an entire mammalian cell. This method may provide important insights into architectural changes that accompany cellular processes.

| S-EPMC5790320 | biostudies-literature

Three-dimensional scanning transmission electron microscopy of biological specimens.

Project description:A three-dimensional (3D) reconstruction of the cytoskeleton and a clathrin-coated pit in mammalian cells has been achieved from a focal-series of images recorded in an aberration-corrected scanning transmission electron microscope (STEM). The specimen was a metallic replica of the biological structure comprising Pt nanoparticles 2-3 nm in diameter, with a high stability under electron beam radiation. The 3D dataset was processed by an automated deconvolution procedure. The lateral resolution was 1.1 nm, set by pixel size. Particles differing by only 10 nm in vertical position were identified as separate objects with greater than 20% dip in contrast between them. We refer to this value as the axial resolution of the deconvolution or reconstruction, the ability to recognize two objects, which were unresolved in the original dataset. The resolution of the reconstruction is comparable to that achieved by tilt-series transmission electron microscopy. However, the focal-series method does not require mechanical tilting and is therefore much faster. 3D STEM images were also recorded of the Golgi ribbon in conventional thin sections containing 3T3 cells with a comparable axial resolution in the deconvolved dataset.

| S-EPMC2917646 | biostudies-literature

Three-beam convergent-beam electron diffraction for measuring crystallographic phases.

Project description:Under almost all circumstances, electron diffraction patterns contain information about the phases of structure factors, a consequence of the short wavelength of an electron and its strong Coulombic interaction with matter. However, extracting this information remains a challenge and no generic method exists. In this work, a set of simple analytical expressions is derived for the intensity distribution in convergent-beam electron diffraction (CBED) patterns recorded under three-beam conditions. It is shown that these expressions can be used to identify features in three-beam CBED patterns from which three-phase invariants can be extracted directly, without any iterative refinement processes. The octant, in which the three-phase invariant lies, can be determined simply by inspection of the indexed CBED patterns (i.e. the uncertainty of the phase measurement is ±22.5°). This approach is demonstrated with the experimental measurement of three-phase invariants in two simple test cases: centrosymmetric Si and non-centrosymmetric GaAs. This method may complement existing structure determination methods by providing direct measurements of three-phase invariants to replace 'guessed' invariants in ab initio phasing methods and hence provide more stringent constraints to the structure solution.

| S-EPMC6211523 | biostudies-literature

Structural and chemical characterization of MoO<sub>2</sub>/MoS<sub>2</sub> triple-hybrid materials using electron microscopy in up to three dimensions.

Project description:This work presents the synthesis of MoO2/MoS2 core/shell nanoparticles within a carbon nanotube network and their detailed electron microscopy investigation in up to three dimensions. The triple-hybrid core/shell material was prepared by atomic layer deposition of molybdenum oxide onto carbon nanotube networks, followed by annealing in a sulfur-containing gas atmosphere. High-resolution transmission electron microscopy together with electron diffraction, supported by chemical analysis via energy dispersive X-ray and electron energy loss spectroscopy, gave proof of a MoO2 core covered by few layers of a MoS2 shell within an entangled network of carbon nanotubes. To gain further insights into this complex material, the analysis was completed with 3D electron tomography. By using Z-contrast imaging, distinct reconstruction of core and shell material was possible, enabling the analysis of the 3D structure of the material. These investigations showed imperfections in the nanoparticles which can impact material performance, i.e. for faradaic charge storage or electrocatalysis.

| S-EPMC9418330 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data