Co-restoring Multimodal Microscopy Images

2015 ◽  
pp. 242-250
Author(s):  
Mingzhong Li ◽  
Zhaozheng Yin
Keyword(s):  
Microscopy Images

Specimen Collection Effect in Scanning Electron Microscopy Images

1972 ◽  
Vol 30 ◽  
pp. 448-449
Author(s):  
J. Temple Black ◽  
Jose Guerrero
Keyword(s):  
Surface Morphology ◽  
Secondary Electron Emission ◽  
Secondary Electrons ◽  
Charge Effects ◽  
Material Density ◽  
Specimen Collection ◽  
The Subject ◽  
Curved Paths ◽  
Subject Material ◽  
Microscopy Images

In the SEM, contrast in the image is the result of variations in the volume secondary electron emission and backscatter emission which reaches the detector and serves to intensity modulate the signal for the CRT's. This emission is a function of the accelerating potential, material density, chemistry, crystallography, local charge effects, surface morphology and especially the angle of the incident electron beam with the particular surface site. Aside from the influence of object inclination, the surface morphology is the most important feature In producing contrast. “Specimen collection“ is the name given the shielding of the collector by adjacent parts of the specimen, producing much image contrast. This type of contrast can occur for both secondary and backscatter electrons even though the secondary electrons take curved paths to the detector-collector.Figure 1 demonstrates, in a unique and striking fashion, the specimen collection effect. The subject material here is Armco Iron, 99.85% purity, which was spark machined.

Scanning ion microscopy images

1987 ◽  
Vol 45 ◽  
pp. 180-181
Author(s):  
R. Levi-Setti ◽  
J.M. Chabala ◽  
Y.L. Wang
Keyword(s):  
Metal Ion ◽  
Secondary Emission ◽  
Scanning Method ◽  
Ion Channeling ◽  
Secondary Ions ◽  
High Resolution Images ◽  
Ion Microscopy ◽  
Z Contrast ◽  
Focused Beams ◽  
Microscopy Images

Finely focused beams extracted from liquid metal ion sources (LMIS) provide a wealth of secondary signals which can be exploited to create high resolution images by the scanning method. The images of scanning ion microscopy (SIM) encompass a variety of contrast mechanisms which we classify into two broad categories: a) Emission contrast and b) Analytical contrast.Emission contrast refers to those mechanisms inherent to the emission of secondaries by solids under ion bombardment. The contrast-carrying signals consist of ion-induced secondary electrons (ISE) and secondary ions (ISI). Both signals exhibit i) topographic emission contrast due to the existence of differential geometric emission and collection effects, ii) crystallographic emission contrast, due to primary ion channeling phenomena and differential oxidation of crystalline surfaces, iii) chemical emission or Z-contrast, related to the dependence of the secondary emission yields on the Z and surface chemical state of the target.

A Transmission Electron Microscopical Investigation of Phase Transformations in TiNi

1980 ◽  
Vol 38 ◽  
pp. 340-341
Author(s):  
P. Moine ◽  
G. M. Michal ◽  
R. Sinclair
Keyword(s):  
Electron Microscopy ◽  
Phase Transformations ◽  
Applied Stress ◽  
Structural Changes ◽  
Microscopical Investigation ◽  
Temperature Range ◽  
Transmission Electron ◽  
Electron Microscopical ◽  
Diffraction Effects ◽  
Microscopy Images

Premartensitic effects in near equiatomic TiNi have been pointed out by several authors(1-5). These include anomalous contrast in electron microscopy images (mottling, striations, etc. ),diffraction effects(diffuse streaks, extra reflections, etc.), a resistivity peak above Ms (temperature at which a perceptible amount of martensite is formed without applied stress). However the structural changes occuring in this temperature range are not well understood. The purpose of this study is to clarify these phenomena.

Formation of Luminescent Si Nanocrystals by High-Temperature Annealing of Ion-Beam-Sputtered Si/SiO2 Multilayers

2003 ◽  
Vol 775 ◽  
Author(s):  
Suk-Ho Choi ◽  
Jun Sung Bae ◽  
Kyung Jung Kim ◽  
Dae Won Moon
Keyword(s):  
Quantum Confinement ◽  
Radiative Recombination ◽  
Ion Beam ◽  
Quantum Confinement Effect ◽  
Visible Range ◽  
Ion Beam Sputtering ◽  
Transmission Electron ◽  
Si Nanocrystals ◽  
Beam Sputtering ◽  
Microscopy Images

AbstractSi/SiO2 multilayers (MLs) have been prepared under different deposition temperatures (TS) by ion beam sputtering. The annealing at 1200°C leads to the formation of Si nanocrystals in the Si layer of MLs. The high resolution transmission electron microscopy images clearly demonstrate the existence of Si nanocrystals, which exhibit photoluminescence (PL) in the visible range when TS is ≥ 300°C. This is attributed to well-separation of nanocrystals in the higher-TS samples, which is thought to be a major cause for reducing non-radiative recombination in the interface between Si nanocrystal and surface oxide. The visible PL spectra are enhanced in its intensity and are shifted to higher energy by increasing TS. These PL behaviours are consistent with the quantum confinement effect of Si nanocrystals.

A Novel Integrated Reliability Test System for BEOL TDDB Study

2012 ◽  
Author(s):  
Jifeng Chen ◽  
Peilin Song ◽  
Thomas M. Shaw ◽  
Franco Stellari ◽  
Lynne Gignac ◽  
...  
Keyword(s):  
Preliminary Analysis ◽  
Dielectric Breakdown ◽  
Test System ◽  
Accurate Information ◽  
Reliability Test ◽  
Electrical Measurements ◽  
Time Dependent Dielectric Breakdown ◽  
Microscopy Images ◽  
Insight Into

Abstract In this paper, we propose a new methodology and test system to enable the early detection and precise localization of Time-Dependent-Dielectric-Breakdown (TDDB) occurrence in Back-End-of-Line (BEOL) interconnection. The methodology is implemented as a novel Integrated Reliability Test System (IRTS). In particular, through our methodology and test system, we can easily synchronize electrical measurements and emission microscopy images to gather more accurate information and thereby gain insight into the nature of the defects and their relationship to chip manufacturing steps and materials, so that we can ultimately better engineer these steps for higher reliable systems. The details of our IRTS will be presented along with a case study and preliminary analysis results.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

Electrogeneration of Conducting Poly(α-tetrathiophene). Effect of Solution Stirring and Detection of Linear Oligomers

2003 ◽  
Vol 68 (7) ◽  
pp. 1326-1344 ◽  
Author(s):  
Francesc Estrany ◽  
Ramon Oliver ◽  
Esther García ◽  
Esther Gualba ◽  
Pere-Lluís Cabot ◽  
...  
Keyword(s):  
Ion Pairs ◽  
Polymer Chains ◽  
Polymerization Process ◽  
Anodic Process ◽  
Cathodic Peak ◽  
Linear Chains ◽  
Monomer Solution ◽  
Ir Analysis ◽  
Microscopy Images ◽  
Tof Ms

The anodic oxidation of α-tetrathiophene on Pt was studied in a 1.0 mM monomer solution in 0.1 M LiClO4 in 45:35:20 acetonitrile/ethanol/DMF. Three consecutive oxidation peaks were detected by cyclic voltammetry, along with a cathodic peak related to the reduction of electroactive polarons formed during the first anodic process. Uniform, adherent, insoluble and black polymer films were obtained by chronoamperometry at 1.000 V vs Ag|AgCl corresponding to the first oxidation-polymerization process. Stirring of monomer solution promotes the production of polymer, favoring the oxidation of polymer chains with the incorporation of more doping ClO4- ions and ion pairs of Li+ClO4- in their monomeric units. The conductivity of the polymer obtained under stirring was three orders of magnitude higher than that synthesized from a quiescent solution. The scanning electron microscopy images also showed much more uniform films under stirring. This behavior points to the existence of less crosslinking in the polymer and the production of longer linear chains when the solution is stirred. IR analysis of these materials confirmed the formation of crosslinked chains with predominance of β-β linkages. Short linear oligomers such as the dimer, trimer and tetramer were detected in all polymers by MALDI-TOF-MS, thus showing a radical polycondensation as initial electropolymerization mechanism. A larger proportion of linear oligomers is formed under solution stirring.

scholarly journals Correction to: Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Deep Nucleus ◽  
Microscopy Images

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