Implementation of an Optimally Windowed Chirp method for Industrial Rheological Measurements

The Optimally Windowed Chirp (OW-Chirp) methodology,¹ which traces its foundation to echolocation, is an efficient alternative to discrete data collection approaches traditionally used for rheology frequency sweep experiments.  Successful application of the methodology results in a significant reduction in data collection time, from several minutes to as little as a few seconds. For a complete rheology master curve, the approach can save hours of instrument time, without sacrificing data density. In this talk we will discuss the successful implementation of this methodology into an industrial analytical R&D framework, using commercially available rheometers.

Central to our approach is an innovative in-house Python data analysis package (cralds) which offers a robust conceptual foundation for handling, visualizing, processing, and documenting complex analytical data. Thanks to the inherent flexibility of Python, our software can seamlessly integrate with modules like mastercurves,² thus enabling efficient and automated Time-Temperature Superposition (TTS) master curve generation.

Our findings not only elucidate the industrial relevance of the Optimally Windowed Chirp technique but also underscore the significance of cutting-edge, data-driven strategies in the realm of contemporary rheology research.

1. Geri Keshavarz et al; Phys. Rev. X" 8 041042 (2018)

2. Lennon, K. R.et al;  Appl. Soft Matter Sci. Data-Cent. Eng 2023, 4, e13.