A Simple Generalized Theory for the Analysis of Dynamic Thermal Measurement

  • Allan P. Gray


Virtually all of the theoretical discussions of differential thermal analysis which have appeared in the literature begin with two simple relationships, one arising from the conservation of energy and the other from the linear dependence of heat flow on temperature differential known as Newton’s Law. Usually these equations have been stated and further developed for the purpose of describing the performance of some particular apparatus or to provide a basis for the analysis of data obtained in the investigation of some particular class of problem. As a result, mathematical complexities which may arise in the analysis of a particular apparatus or problem are incorporated in the theoretical development so that the conclusions lose their generality and are often of little value to the thermal analyst employing other equipment or investigating different areas of application. It is the object of this paper to show that the important performance properties of scanning thermal analysis systems, including DTA, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), can be derived from the simplest possible forms of the energy conservation and heat transfer equations. The results can be interpreted as representing the performance of an ideal scanning thermal device and therefore provide useful criteria for evaluating instrumentation and data quality. Further, since several modern apparatus designs actually approach “ideal” behavior quite closely, the development is not entirely an academic exercise; the equations lead to graphical methods for correcting thermal analysis data for instrumental effects which are simple and quite useful in practice.


Differential Scanning Calorimetry Differential Thermal Analysis Thermal Resistance Differential Scanning Calorimetry Curve Sharp Transition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. (1).
    Vold, M. J., Anal. Chem., 21, 683 (1949).CrossRefGoogle Scholar
  2. (2).
    Boersma, S. L., J. Am. Ceramic Soc. 38, 281 (1955).CrossRefGoogle Scholar
  3. (3).
    Borschardt, H. J., and Daniels, F. J., J. Am. Chem. Soc. 79, 41 (1957).CrossRefGoogle Scholar
  4. (4).
    Reed, R. L., Weber, L., and Gottfried, B. S., Ind. & Eng. Chem., Fundamentals 4, 38 (1965).CrossRefGoogle Scholar
  5. (5).
    Watson, E.S., O’Neill, M. J., Justin, J., and Brenner, N., Anal. Chem. 36, 1233 (1964).CrossRefGoogle Scholar
  6. (6).
    O’Neill, M. J., Anal. Chem. 36, 1238 (1964).CrossRefGoogle Scholar

Copyright information

© Plenum Press 1968

Authors and Affiliations

  • Allan P. Gray
    • 1
  1. 1.Perkin-Elmer CorporationNorwalkUSA

Personalised recommendations