Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy.

ABSTRACT: Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated. In this study, we applied machine learning techniques to denoise high frame rate spectra, benchmarking with slower frame rate "ground truths". The results provide a foundation for reliable use of low SNR data acquired in rapid, in-situ spectroscopy experiments. Such a tool-set is a first step toward both automation in microscopy as well as use of these methods to interrogate otherwise poorly understood transformations.

SUBMITTER: Pate CM

PROVIDER: S-EPMC8484590 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy.

Project description:Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping.

| S-EPMC10444881 | biostudies-literature

Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach

Project description: Not available

| S-EPMC10528079 | biostudies-literature

Single-atom electron energy loss spectroscopy of light elements.

Project description:Light elements such as alkali metal (lithium, sodium) or halogen (fluorine, chlorine) are present in various substances and indeed play significant roles in our life. Although atomic behaviours of these elements are often a key to resolve chemical or biological activities, they are hardly visible in transmission electron microscope because of their smaller scattering power and higher knock-on probability. Here we propose a concept for detecting light atoms encaged in a nanospace by means of electron energy loss spectroscopy using inelastically scattered electrons. In this method, we demonstrate the single-atom detection of lithium, fluorine, sodium and chlorine with near-atomic precision, which is limited by the incident probe size, signal delocalization and atomic movement in nanospace. Moreover, chemical shifts of lithium K-edge have been successfully identified with various atomic configurations in one-dimensional lithium compounds.

| S-EPMC4532884 | biostudies-literature

Graphene nanowalls formation investigated by Electron Energy Loss Spectroscopy.

Project description:The properties of layered materials are significantly dependent on their lattice orientations. Thus, the growth of graphene nanowalls (GNWs) on Cu through PECVD has been increasingly studied, yet the underlying mechanisms remain unclear. In this study, we examined the GNWs/Cu interface and investigated the evolution of their microstructure using advanced Scanning transmission electron microscopy and Electron Energy Loss Spectroscopy (STEM-EELS). GNWs interface and initial root layers of comprise graphitic carbon with horizontal basal graphene (BG) planes that conform well to the catalyst surface. In the vertical section, the walls show a mix of graphitic and turbostratic carbon, while the latter becomes more noticeable close to the top edges of the GMWs film. Importantly, we identified growth process began with catalysis at Cu interface forming BG, followed by defect induction and bending at 'coalescence points' of neighboring BG, which act as nucleation sites for vertical growth. We reported that although classical thermal CVD mechanism initially dominates, growth of graphene later deviates a few nanometers from the interface to form GNWs. Nascent walls are no longer subjected to the catalytic action of Cu, and their development is dominated by the stitching of charged carbon species originating in the plasma with basal plane edges.

| S-EPMC10796779 | biostudies-literature

Chemical identification through two-dimensional electron energy-loss spectroscopy.

Project description:We explore a disruptive approach to nanoscale sensing by performing electron energy loss spectroscopy through the use of low-energy ballistic electrons that propagate on a two-dimensional semiconductor. In analogy to free-space electron microscopy, we show that the presence of analyte molecules in the vicinity of the semiconductor produces substantial energy losses in the electrons, which can be resolved by energy-selective electron injection and detection through actively controlled potential gates. The infrared excitation spectra of the molecules are thereby gathered in this electronic device, enabling the identification of chemical species with high sensitivity. Our realistic theoretical calculations demonstrate the superiority of this technique for molecular sensing, capable of performing spectral identification at the zeptomol level within a microscopic all-electrical device.

| S-EPMC7455491 | biostudies-literature

Chemical Fingerprinting of Polymers Using Electron Energy-Loss Spectroscopy.

Project description:Electron energy-loss spectroscopy (EELS) is becoming an important tool in the characterization of polymeric materials. The sensitivity of EELS to changes in the chemical structure of polymeric materials dictates its applicability. In particular, it is important for compositional analysis to have reference spectra of pure components. Here, we report the spectra of the carbon K-edge of six polymers (polyethylene, polypropylene, polybutylene terephthalate, and polylactic acid) including copolymers (styrene acrylonitrile and acrylonitrile butadiene styrene), to be used as reference spectra for future EELS studies of polymers. We have successfully decomposed the carbon K-edge of each of the polymers and assigned the observed peaks to bonding transitions. The spectra have been acquired in standard experimental conditions, and electron beam damage has been taken into account during establishment of spectral-structural relationships. We found that the more commonly available low-energy resolution spectrometers are adequate to chemically fingerprint linear saturated hydrocarbons such as PE, PP, and PLA. We have thus moved a step closer toward creating an atlas of polymer EELS spectra, which can be subsequently used for chemical bond mapping of polymeric materials with nanoscale spatial resolution.

| S-EPMC8459415 | biostudies-literature

Electron energy-loss spectroscopy of branched gap plasmon resonators.

Project description:The miniaturization of integrated optical circuits below the diffraction limit for high-speed manipulation of information is one of the cornerstones in plasmonics research. By coupling to surface plasmons supported on nanostructured metallic surfaces, light can be confined to the nanoscale, enabling the potential interface to electronic circuits. In particular, gap surface plasmons propagating in an air gap sandwiched between metal layers have shown extraordinary mode confinement with significant propagation length. In this work, we unveil the optical properties of gap surface plasmons in silver nanoslot structures with widths of only 25 nm. We fabricate linear, branched and cross-shaped nanoslot waveguide components, which all support resonances due to interference of counter-propagating gap plasmons. By exploiting the superior spatial resolution of a scanning transmission electron microscope combined with electron energy-loss spectroscopy, we experimentally show the propagation, bending and splitting of slot gap plasmons.

| S-EPMC5171719 | biostudies-literature

EpiGe: A cytosine methyl-genotyping PCR-based machine-learning tool for rapid classification of medulloblastoma

Project description:Molecular classification of medulloblastoma is critical for the correct treatment of this malignant paediatric brain tumour. The analysis of genome-wide DNA methylation patterns has profoundly improved diagnostic precision and classification of brain tumours. However, the implementation of DNA methylation microarrays in daily clinical practice can be time-consuming, costly and inaccessible for many centres worldwide. We aimed to develop a machine-learning decision support system for rapid and cost-effective prediction of medulloblastoma methylation class directly from quantitative PCR data.

2023-08-23 | GSE210723 | GEO

Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning.

Project description:Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz-5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved.

| S-EPMC7070823 | biostudies-literature

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy.

Project description:Introduction:Assessment of cartilage integrity during arthroscopy is limited by the subjective visual nature of the technique. To address this shortcoming in diagnostic evaluation of articular cartilage, near infrared spectroscopy (NIRS) has been proposed. In this study, we evaluated the capacity of NIRS, combined with machine learning techniques, to classify cartilage integrity. Methods:Rabbit (n = 14) knee joints with artificial injury, induced via unilateral anterior cruciate ligament transection (ACLT), and the corresponding contra-lateral (CL) joints, including joints from separate non-operated control (CNTRL) animals (n = 8), were used. After sacrifice, NIR spectra (1000-2500 nm) were acquired from different anatomical locations of the joints (n TOTAL = 313: n CNTRL = 111, n CL = 97, n ACLT = 105). Machine and deep learning methods (support vector machines-SVM, logistic regression-LR, and deep neural networks-DNN) were then used to develop models for classifying the samples based solely on their NIR spectra. Results:The results show that the model based on SVM is optimal of distinguishing between ACLT and CNTRL samples (ROC_AUC = 0.93, kappa = 0.86), LR is capable of distinguishing between CL and CNTRL samples (ROC_AUC = 0.91, kappa = 0.81), while DNN is optimal for discriminating between the different classes (multi-class classification, kappa = 0.48). Conclusion:We show that NIR spectroscopy, when combined with machine learning techniques, is capable of holistic assessment of cartilage integrity, with potential for accurately distinguishing between healthy and diseased cartilage.

| S-EPMC7225230 | 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