Applications of thin layer activation technique for the measurement of surface loss of materials: an Indian perspective
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D. P. Chowdhury
, J. Datta and A. V. R. Reddy
Abstract
Thin layer activation (TLA) technique is a powerful nuclear technique based on the production of a radioisotope in the surface of a material by a charged particle induced nuclear reaction, followed by an estimation of the loss of the activity which is a measure of the surface loss of materials in the range of nanometer to hundreds of micrometer in the fields of wear, corrosion or erosion. The technique has been found to be a very useful tool in the basic scientific research and industrial applications. The present review describes the basic principle of TLA technique, its characteristic features, methodology, scope, applications and various recent developments regarding its growing use. Some of our earlier TLA work carried out with α and heavy ion (16O) in various applied areas is also included here. Heavy ions were used because no other light ions (particularly protons) were available at our cyclotron.
© by Oldenbourg Wissenschaftsverlag, Kolkata - 700064, Germany
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Articles in the same Issue
- Preface
- Superheavy elements at GSI: a broad research program with element 114 in the focus of physics and chemistry
- Nuclear structure and reaction studies near doubly magic 270Hs
- Recent developments in nuclear data measurements and chemical separation methods in accelerator production of astatine and technetium radionuclides
- Radiotracers for SPECT imaging: current scenario and future prospects
- Recoil and conversion electron considerations of the 166Dy/166Ho in vivo generator
- Emergence and present status of Lu-177 in targeted radiotherapy: the Indian scenario
- Nuclear radioactive techniques applied to materials research
- Applications of thin layer activation technique for the measurement of surface loss of materials: an Indian perspective
- Surface and volume characterization of TiO2 nanomaterials by 44Ti time differential perturbed angular correlation