Electrospray deposition of physical unclonable functions for drug anti-counterfeiting.

In recent years, pharmaceutical counterfeiting has become an increasingly dangerous situation. A patient who unknowingly consumes a counterfeit drug is at a serious health risk. To address this problem, a low-cost and robust approach for authentication that can be administered at the point-of-care is required. Our proposed solution uses Optical Physical Unclonable Functions (PUFs); patterns formed by a stochastic process that can be used for authentication. We create edible PUFs (ePUFs) using electrospray deposition, which utilizes strong electric fields to atomize a liquid suspension into a plume of micro-scale droplets that are delivered to the target. The ePUFs are electrospray-deposited from an edible ink directly onto the surface of the drug tablets. The process parameters (flow rate, translation speed, and suspension concentration) govern the characteristics of the ePUF to provide highly stochastic patterns. To evaluate our approach, 200 ePUFs were deposited onto tablets at various conditions, followed by imaging and storage of the patterns in a database. For ePUF authentication, a machine vision approach was created using the open source SIFT pattern matching algorithm. Using optimized pattern-matching constraints, our algorithm was shown to be 100% successful in authenticating the cellphone images of the ePUFs to the database. Additionally, the algorithm was found to be robust against changes in illumination and orientation of the cellphone images.

Flow in temporally and spatially varying porous media: a model for transport of interstitial fluid in the brain.

Flow in a porous medium can be driven by the deformations of the boundaries of the porous domain. Such boundary deformations locally change the volume fraction accessible by the fluid, creating non-uniform porosity and permeability throughout the medium. In this work, we construct a deformation-driven porous medium transport model with spatially and temporally varying porosity and permeability that are dependent on the boundary deformations imposed on the medium. We use this model to study the transport of interstitial fluid along the basement membranes in the arterial walls of the brain. The basement membrane is modeled as a deforming annular porous channel with the compressible pore space filled with an incompressible, Newtonian fluid. The role of a forward propagating peristaltic heart pulse wave and a reverse smooth muscle contraction wave on the flow within the basement membranes is investigated. Our results identify combinations of wave amplitudes that can induce either forward or reverse transport along these transport pathways in the brain. The magnitude and direction of fluid transport predicted by our model can help in understanding the clearance of fluids and solutes along the Intramural Periarterial Drainage route and the pathology of cerebral amyloid angiopathy.