Literature DB >> 1236025

Aperture contrast in thick amorphous specimens using scanning transmission electron microscopy.

D J Smith, J M Cowley.   

Abstract

The contrast observed in thick amorphous specimens using a scanning transmission electron microscope (STEM) can be considerably improved by the use of an optimum collector aperture angle. The size of this angle can be calculated by considering the variation of electron current transmitted through the specimen as a function both of the specimen thickness and of the angle of collection subtended at the specimen. Typically these calculations predict optimum angles to be several times the half-width of the elastic scattering distribution, often 10(-1) rad or more. Observations of biological sections of up to 2 micron in thickness using scanning attachments of commercial transmission microscopes have verifie these results at beam voltages of 50, 100 and 200 kV. Wide angle convergent beam diffraction patterns were used to give accurate values of the effective angles represented by the various collector apertures. Once the linearity of the detector-amplifier system had been established, operation in a line modulation mode enabled quantitative measurements to be made of the image contrast. Such measurements also offer a quick effective method of comparing electron beam penetrations.

Mesh:

Year:  1975        PMID: 1236025     DOI: 10.1016/s0304-3991(75)80015-5

Source DB:  PubMed          Journal:  Ultramicroscopy        ISSN: 0304-3991            Impact factor:   2.689


  3 in total

Review 1.  Development and application of STEM for the biological sciences.

Authors:  Alioscka A Sousa; Richard D Leapman
Journal:  Ultramicroscopy       Date:  2012-05-18       Impact factor: 2.689

2.  Monte Carlo electron-trajectory simulations in bright-field and dark-field STEM: implications for tomography of thick biological sections.

Authors:  A A Sousa; M F Hohmann-Marriott; G Zhang; R D Leapman
Journal:  Ultramicroscopy       Date:  2008-10-25       Impact factor: 2.689

3.  Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.

Authors:  Shiro Ihara; Hikaru Saito; Mizumo Yoshinaga; Lavakumar Avala; Mitsuhiro Murayama
Journal:  Sci Rep       Date:  2022-08-05       Impact factor: 4.996

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.