microDSC

Differential scanning calorimetry (DSC) is a thermoanalytical technique in which the difference in the amount of heat required to increase the temperature of a sample and reference is measured as a function of temperature. Both the sample and reference are maintained at nearly the same temperature throughout the experiment. Generally, the temperature program for a DSC analysis is designed such that the sample holder temperature increases linearly as a function of time. The reference sample should have a well-defined heat capacity over the range of temperatures to be scanned. 

The change in the sample amount or/and reference amount in the cell has a significant impact on heat capacity measurement. This research focused on developing a method that can estimate the possible deviation in measurement. Moreover, give information about the appropriate volume of the sample in the cell that can cause minimum deviation in measurement.

As an extension of this research work, an Artificial Neural Network (ANN) based model is developed to predict an error in heat capacity measurement experiment with the help of the sample and reference material amount. The prepared ANN model is open source and available at github/nirmalparmarphd.