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Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing technique, which has emerged as a high-resolution method for the fabrication of a broad spectrum of electronic devices. Despite the advantages and critical applications of AJP in the printed-electronics industry, AJP process is intrinsically unstable, complex, and prone to unexpected gradual drifts, which adversely affect the morphology and consequently the functional performance of a printed electronic device. Therefore, in situ process monitoring and control in AJP is an inevitable need. In this respect, in addition to experimental characterization of the AJP process, physical models would be required to explain the underlying aerodynamic phenomena in AJP. The goal of this research work is to establish a physics-based computational platform for prediction of aerosol flow regimes and ultimately, physics-driven control of the AJP process. In pursuit of this goal, the objective is to forward a three-dimensional (3D) compressible, turbulent, multiphase computational fluid dynamics (CFD) model to investigate the aerodynamics behind: (i) aerosol generation, (ii) aerosol transport, and (iii) aerosol deposition on a moving free surface in the AJP process. The complex geometries of the deposition head as well as the pneumatic atomizer were modeled in the ansys-fluent environment, based on patented designs in addition to accurate measurements, obtained from 3D X-ray micro-computed tomography (μ-CT) imaging. The entire volume of the constructed geometries was subsequently meshed using a mixture of smooth and soft quadrilateral elements, with consideration of layers of inflation to obtain an accurate solution near the walls. A combined approach, based on the density-based and pressure-based Navier–Stokes formation, was adopted to obtain steady-state solutions and to bring the conservation imbalances below a specified linearization tolerance (i.e., 10−6). Turbulence was modeled using the realizable k-ε viscous model with scalable wall functions. A coupled two-phase flow model was, in addition, set up to track a large number of injected particles. The boundary conditions of the CFD model were defined based on experimental sensor data, recorded from the AJP control system. The accuracy of the model was validated using a factorial experiment, composed of AJ-deposition of a silver nanoparticle ink on a polyimide substrate. The outcomes of this study pave the way for the implementation of physics-driven in situ monitoring and control of AJP. 

Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing (AM) method, emerging as the process of choice for the fabrication of a broad spectrum of electronics, such as sensors, transistors, and optoelectronic devices. However, AJP is a highly complex process, prone to intrinsic gradual drifts. Consequently, real-time process monitoring and control in AJP is a bourgeoning need. The goal of this work is to establish an integrated, smart platform for in situ and real-time monitoring of the functional properties of AJ-printed electronics. In pursuit of this goal, the objective is to forward a multiple-input, single-output (MISO) intelligent learning model—based on sparse representation classification (SRC)—to estimate the functional properties (e.g., resistance) in situ as well as in real-time. The aim is to classify the resistance of printed electronic traces (lines) as a function of AJP process parameters and the trace morphology characteristics (e.g., line width, thickness, and cross-sectional area (CSA)). To realize this objective, line morphology is captured using a series of images, acquired: (i) in situ via an integrated high-resolution imaging system and (ii) in real-time via the AJP standard process monitor camera. Utilizing image processing algorithms developed in-house, a wide range of 2D and 3D morphology features are extracted, constituting the primary source of data for the training, validation, and testing of the SRC model. The four-point probe method (also known as Kelvin sensing) is used to measure the resistance of the deposited traces and as a result, to define a priori class labels. The results of this study exhibited that using the presented approach, the resistance (and potentially, other functional properties) of printed electronics can be estimated both in situ and in real-time with an accuracy of ≥ 90%. 

Aerosol jet printing (AJP) is a complex process for additive electronics that is often unstable. To overcome this instability, observation while printing and control of the printing process using image-based monitoring is demonstrated. This monitoring is validated against images taken after the print and shown highly correlated and useful for the determination of printed linewidth. These images and the observed linewidth are used as input for closed-loop control of the printing process, with print speed changed in response to changes in the observed linewidth. Regression is used to relate these quantities and forms the basis of proportional and proportional integral control. Electrical test structures were printed with controlled and uncontrolled printing, and it was found that the control influenced their linewidth and electrical properties, giving improved uniformity in both size and electrical performance. 

There has been an increasing need of technologies to manufacturing chemical and biological sensors for various applications ranging from environmental monitoring to human health monitoring. Currently, manufacturing of most chemical and biological sensors relies on a variety of standard microfabrication techniques, such as physical vapor deposition and photolithography, and materials such as metals and semiconductors. Though functional, they are hampered by high cost materials, rigid substrates, and limited surface area. Paper based sensors offer an intriguing alternative that is low cost, mechanically flexible, has the inherent ability to filter and separate analytes, and offers a high surface area, permeable framework advantageous to liquid and vapor sensing. However, a major drawback is that standard microfabrication techniques cannot be used in paper sensor fabrication. To fabricate sensors on paper, low temperature additive techniques must be used, which will require new manufacturing processes and advanced functional materials. In this work, we focus on using aerosol jet printing as a highresolution additive process for the deposition of ink materials to be used in paper-based sensors. This technique can use a wide variety of materials with different viscosities, including materials with high porosity and particles inherent to paper. One area of our efforts involves creating interdigitated microelectrodes on paper in a one-step process using commercially available silver nanoparticle and carbon black based conductive inks. Another area involves use of specialized filter papers as substrates, such as multi-layered fibrous membrane paper consisting of a poly(acrylonitrile) nanofibrous layer and a nonwoven poly(ethylene terephthalate) layer. The poly(acrylonitrile) nanofibrous layer are dense and smooth enough to allow for high resolution aerosol jet printing. With additively fabricated electrodes on the paper, molecularly-functionalized metal nanoparticles are deposited by molecularly-mediated assembling, drop casting, and printing (sensing and electrode materials), allowing full functionalization of the paper, and producing sensor devices with high surface area. These sensors, depending on the electrode configuration, are used for detection of chemical and biological species in vapor phase, such as water vapor and volatile organic compounds, making them applicable to human performance monitoring. These paper based sensors are shown to display an enhancement in sensitivity, as compared to control devices fabricated on non-porous polyimide substrates. These results have demonstrated the feasibility of paper-based printed devices towards manufacturing of a fully wearable, highly-sensitive, and wireless human performance monitor coupled to flexible electronics with the capability to communicate wirelessly to a smartphone or other electronics for data logging and analysis.