'Mic drop': on estimating the size of sub-mm droplets using a simple condenser microphone

Abstract

The size distribution of aerosol droplets is a key parameter in a myriad of processes, and it is typically measured with optical aids (e.g., lasers or cameras) that require sophisticated calibration, thus making the measurement cost intensive. We developed a new method to indirectly measure the size of small droplets using off-the-shelf <\$1 electret microphones. In this method we exploit the natural oscillations that small droplets undergo after impacting a flat surface: by allowing droplets to land directly on a microphone diaphragm, we record the impact force they exert onto it and calculate the complex resonant frequencies of oscillations, from which their size can be inferred. To test this method, we recorded the impact signals of droplets of varying sizes generated by a pipette and extracted the resonant frequencies that characterize each signal. Various sources of uncertainty in the experiments led to a range of frequencies that can characterize each droplet size, and hence a data-driven approach was taken to estimate the size from each set of measured frequencies. We employed a simple setting of neural network and trained it on the frequencies we measured from impact of droplets of prescribed radius. The network was then able to predict the droplet radius in the test group with an average error of 2.7\% and a maximum of 8.6\% relative to the pipette nominal values. These results, achieved with a data set of only 320 measurements, demonstrate the potential for reliable size-distribution measurements via a simple and inexpensive method.