Currently, manufacturers have high expectations surrounding the performance of their materials. A sealing ring must not become brittle, a PET bottle cannot deform, and medications need to react within the body at exactly the right time. Across the material science domain, Mettler-Toledo’s dynamic Differential Scanning Calorimeter (DSC) has become an indispensable tool for many. Thermal analysis makes a valuable contribution from quality control to research and development of materials and chemical compounds.
Why do we need DSC?
All materials can absorb or release energy in the form of heat. DSC is used to record physical changes or chemical reactions of materials in quantitative terms by measuring the heat flow of a sample as a function of temperature or time, and as such, it is one of the most important analytical methods within thermal analysis.
DSC is not only highly sensitive and precise, and works with simple sample preparation, but it also provides automation options and short measuring times. DSC is used in all areas in which thermal parameters are determined, thermal processes are investigated, and materials are characterized or compared. Thus, it provides answers to questions surrounding the stability, usage and processing conditions, error detection, failure analysis, material identification, stability, reactivity, chemical safety, and purity of materials. For example, polymers such as thermoplastics, thermosets, elastomers, composite materials, and adhesives, but also food products, pharmaceuticals, and chemicals can be analyzed.
The curves obtained as a result are heat capacity as a function of temperature. The temperatures at which the heat capacity changes are of interest here – these ranges are called “effects”.
Effects are physical or chemical transitions, i.e. phase transitions such as crystallization, melting, glass transitions, or chemical reactions. Important information about the material properties can be derived from the exact form and characteristics of these effects. The shape of the curve and the temperature range contain information that the user employs to interpret the effects.
The evaluation of measured curves is time-consuming and challenging, even for experts. The project idea developed by Mettler-Toledo was to support users with AI.