32 research outputs found
Lossy Compression of Electron Diffraction Patterns for Ptychography via Change of Basis
Ptychography is a computational imaging technique that has risen in
popularity in the x-ray and electron microscopy communities in the past half
decade. One of the reasons for this success is the development of new high
performance electron detectors with increased dynamic range and readout speed,
both of which are necessary for a successful application of this technique.
Despite the advances made in computing power, processing the recorded data
remains a challenging task, and the growth in data rate has made the size of
the resulting datasets a bottleneck for the whole process. Here we present an
investigation into lossy compression methods for electron diffraction patterns
that retain the necessary information for ptychographic reconstructions, yet
lead to a decrease in data set size by three or four orders of magnitude. We
apply several compression methods to both simulated and experimental data - all
with promising results
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
The overdetermination of the mathematical problem underlying ptychography is
reduced by a host of experimentally more desirable settings. Furthermore,
reconstruction of the sample-induced phase shift is typically limited by
uncertainty in the experimental parameters and finite sample thicknesses.
Presented is a conjugate gradient descent algorithm, regularized optimization
for ptychography (ROP), that recovers the partially known experimental
parameters along with the phase shift, improves resolution by incorporating the
multislice formalism to treat finite sample thicknesses, and includes
regularization in the optimization process, thus achieving reliable results
from noisy data with severely reduced and underdetermined information.Comment: 18 pages, 7 figures, 3 table
Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization
High-throughput grain mapping with sub-nanometer spatial resolution is
demonstrated using scanning nanobeam electron diffraction (also known as 4D
scanning transmission electron microscopy, or 4D-STEM) combined with high-speed
direct electron detection. An electron probe size down to 0.5 nm in diameter is
implemented and the sample investigated is a gold-palladium nanoparticle
catalyst. Computational analysis of the 4D-STEM data sets is performed using a
disk registration algorithm to identify the diffraction peaks followed by
feature learning to map the individual grains. Two unsupervised feature
learning techniques are compared: Principal component analysis (PCA) and
non-negative matrix factorization (NNMF). The characteristics of the PCA versus
NNMF output are compared and the potential of the 4D-STEM approach for
statistical analysis of grain orientations at high spatial resolution is
discussed
py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets
Scanning transmission electron microscopy (STEM) allows for imaging,
diffraction, and spectroscopy of materials on length scales ranging from
microns to atoms. By using a high-speed, direct electron detector, it is now
possible to record a full 2D image of the diffracted electron beam at each
probe position, typically a 2D grid of probe positions. These 4D-STEM datasets
are rich in information, including signatures of the local structure,
orientation, deformation, electromagnetic fields and other sample-dependent
properties. However, extracting this information requires complex analysis
pipelines, from data wrangling to calibration to analysis to visualization, all
while maintaining robustness against imaging distortions and artifacts. In this
paper, we present py4DSTEM, an analysis toolkit for measuring material
properties from 4D-STEM datasets, written in the Python language and released
with an open source license. We describe the algorithmic steps for dataset
calibration and various 4D-STEM property measurements in detail, and present
results from several experimental datasets. We have also implemented a simple
and universal file format appropriate for electron microscopy data in py4DSTEM,
which uses the open source HDF5 standard. We hope this tool will benefit the
research community, helps to move the developing standards for data and
computational methods in electron microscopy, and invite the community to
contribute to this ongoing, fully open-source project
Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.
We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies
The study of atmospheric ice-nucleating particles via microfluidically generated droplets
Ice-nucleating particles (INPs) play a significant role in the climate and hydrological cycle by triggering ice formation in supercooled clouds, thereby causing precipitation and affecting cloud lifetimes and their radiative properties. However, despite their importance, INP often comprise only 1 in 10³–10⁶ ambient particles, making it difficult to ascertain and predict their type, source, and concentration. The typical techniques for quantifying INP concentrations tend to be highly labour-intensive, suffer from poor time resolution, or are limited in sensitivity to low concentrations. Here, we present the application of microfluidic devices to the study of atmospheric INPs via the simple and rapid production of monodisperse droplets and their subsequent freezing on a cold stage. This device offers the potential for the testing of INP concentrations in aqueous samples with high sensitivity and high counting statistics. Various INPs were tested for validation of the platform, including mineral dust and biological species, with results compared to literature values. We also describe a methodology for sampling atmospheric aerosol in a manner that minimises sampling biases and which is compatible with the microfluidic device. We present results for INP concentrations in air sampled during two field campaigns: (1) from a rural location in the UK and (2) during the UK’s annual Bonfire Night festival. These initial results will provide a route for deployment of the microfluidic platform for the study and quantification of INPs in upcoming field campaigns around the globe, while providing a benchmark for future lab-on-a-chip-based INP studies
AusTraits, a curated plant trait database for the Australian flora
We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge
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Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping.
Scanning nanobeam electron diffraction strain mapping is a technique by which the positions of diffracted disks sampled at the nanoscale over a crystalline sample can be used to reconstruct a strain map over a large area. However, it is important that the disk positions are measured accurately, as their positions relative to a reference are directly used to calculate strain. In this study, we compare several correlation methods using both simulated and experimental data in order to directly probe susceptibility to measurement error due to non-uniform diffracted disk illumination structure. We found that prefiltering the diffraction patterns with a Sobel filter before performing cross correlation or performing a square-root magnitude weighted phase correlation returned the best results when inner disk structure was present. We have tested these methods both on simulated datasets, and experimental data from unstrained silicon as well as a twin grain boundary in 304 stainless steel