Plastics, Rubbers and Polymers are used for many products: automotive, packaging, households, electrical accessories, children games, general accessories, etc…
Currently, they are composed by a complex blend of elements (including additives, fillers, compatibilizers, mixtures of multiple polymers with various softness/hardness) and sometimes increasingly complicated to fragment. The systematic selections and mixing of these constituents into appropriate mixtures generate materials with optimized properties.
Imagine you are proud to show off your brand-new car, which unfortunately can’t stand for hours in the sun for a poor-quality paint which peels off and loses its brilliant colour.
Or the keyboard of your PC where keys are too soft and flaking off after a few uses.
As a customer you can claim these defects, but what can you do as a company?
A daily quality control of incoming products is an essential requirement to maintain high-quality products and low price in the production chain.
Subsequently, it is necessary to check the correct composition of the final plastic products to guarantee the appropriate characteristics.
Characterization of polymers and plastics helps a lot to improve the performance of materials by:
- Checking chemical composition,
- Determining molecular structure,
- Identifying properties, types and performances.
For these reasons, the use of different instruments such as: FTIR, NIR, Raman, GC and MS, TGA and/or DSC are indispensable for a quality control laboratory. Even the smallest companies have some of these devices.
Combining some of these instruments through hyphenation and the analysis of the evolved gases, we found also how to optimize time and to increase the quality of the analysis itself by acquiring much more information.
It is possible not only to check changes in the structural parameters of materials, analyzing melting transitions and thermal stability of additives, but it is also possible to verify the material identity by comparing a spectra with a reference one and stabilizing a correlation, just with one sample.