In this study, general theoretical framework is proposed to analyze nanoparticle deposition on a substrate. The deposition of particles onto surfaces is a widely observed phenomenon in both natural and industrial processes, such as inhalation, filtration, ash fouling on heat exchangers, drug delivery, and micro contamination control in the semiconductor industry. One extremely important biological application of particle deposition is vascular treatment. The endothelial glycocalyx layer (EGL), is a very thin (submicron) layer lining the inner wall of blood vessels and is paramount to vascular homeostasis. It regulates vascular permeability, circulating cells’ adhesion, vascular dilation via mechanotransduction, and microvascular thrombosis, among several other functions. Many cardiovascular diseases have been found to be associated with the perturbations of the EGL. Fixing a damaged EGL via targeted particle deposition, has therefore become a potential way to treat cardiovascular diseases. While instructive, the theoretical studies (among many that are mostly experimental) found in the literature describe particle deposition on a case-by-case basis without a unifying framework. In any circumstance, whether a nanoparticle will deposit on a substrate solely depends on the substrate-nanoparticle interfacial energy (be it due to electric potential or another form), and the particle’s incident velocity vector upon impact. Since the number of particles is usually too big to consider every single one of them, and the flow field in the carrying medium is rarely enough to infer the particles’ velocity distribution; much of this phenomenon is stochastic by nature. In the framework proposed herein, a model that quantitatively predicts the substrate’s coverage evolution, is developed based on statistical and physical considerations. A new metric that characterizes the deposition performance for any nanoparticle-substrate pairing, was introduced. The model is then validated under the specific circumstances of a sono-driven dip coating application. Experimental data were obtained from a dip coating setup in which a crank-slider mechanism drives the periodical vertical immersion of a Polydimethylsiloxane (PDMS) substrate in a sonicated graphene nano-solution. Optical images of the substrate were processed for the local covered fraction. Despite several uncertainties inherent to the experimental setup and image processing, theoretical predictions compared reasonably well with experimental observations. An extension of the model is subsequently presented, to encompass the growth rate of the deposition thickness, and representative results were discussed. This study is expected to spur future endeavors to characterize film coating, and other processes involving particle deposition in nanotechnology, in a more systematic fashion, enabling novel reliable design methods that minimize the need for trial and error to optimize the process for the desired outcomes.


A Physics-Based Statistical Model for Nanoparticle Deposition

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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