Poster L27 - pyQCM-BraTaDio: A tool for visualization, data mining, and modeling of Quartz crystal microbalance with dissipation data

Introduction: Quartz Crystal Microbalance with Dissipation (QCM-D) is a versatile analytical technique widely employed for real-time measurements of surface interactions in various scientific fields, such as the adsorption of biomacromolecules to substrates of various chemistries. Despite its extensive applicability, the complexity of QCM-D data analysis often restricts its broader adoption. The need for simplified yet powerful computational tools for interpreting complex data has led to the development of this the software presented in this work, designed to enhance and expedite QCM-D data analysis.

Materials and

Methods: This paper introduces pyQCM-BraTaDio, a Python-based, open-source software crafted to optimize the analysis of multi-harmonic QCM-D systems. The software supports various data formats and integrates powerful computational libraries such as Pandas, Numpy, and SciPy to facilitate data visualization, mining, and model application. pyQCM-BraTaDio features a Tkinter GUI and interactive matplotlib widgets that allows for robust user interaction and model application, handling data adjustments and calibrations with ease, thanks to customizable input methods for processing files.

Results, Conclusions, and Discussions: pyQCM-BraTaDio significantly streamlines the QCM-D data analysis process, enabling rapid visualization and model application in lieu of labor-intensive spreadsheet manipulations. Validation across commercial QCM-D systems (e.g., QSense, AWSensors, QCM-I, openQCM-Next) demonstrated that pyQCM-BraTaDio not only reduces the time required for data analysis, but also maintains functionality across various devices. The software's ability to consistently and efficiently mine, visualize, and apply models to data, such as the Sauerbrey equation for rigid films and shear-dependent compliance for viscoelastic films in various environments, underscores its versatility and utility in QCM-D research. PyQCM-BraTaDio is demonstrated as a beneficial tool for researchers handling QCM-D data analysis accurately and expeditiously.

Acknowledgements (Optional): We are grateful for support from the U.S. National Science Foundation, HRD-1547848 and CAREER CMMI 2239665 to RCAE.