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.

The goal of this research is online monitoring of functional electrical properties, e.g., resistance, of electronic devices made using aerosol jet printing (AJP) additive manufacturing (AM) process. In pursuit of this goal, the objective is to recover the cross-sectional profile of AJP-deposited electronic traces (called lines) through shape-from-shading (SfS) analysis of their online images. The aim is to use the SfS-derived cross-sectional profiles to predict the electrical resistance of the lines. An accurate characterization of the cross section is essential for monitoring the device resistance and other functional properties. For instance, as per Ohm’s law, the electrical resistance of a conductor is inversely proportional to its cross-sectional area (CSA). The central hypothesis is that the electrical resistance of an AJP-deposited line estimated online and in situ from its SfS-derived cross-sectional area is within 20% of its offline measurement. To test this hypothesis, silver nanoparticle lines were deposited using an Optomec AJ-300 printer at varying sheath gas flow rate (ShGFR) conditions. The four-point probes method, known as Kelvin sensing, was used to measure the resistance of the printed structures offline. Images of the lines were acquired online using a charge-coupled device (CCD) camera mounted coaxial to the deposition nozzle of the printer. To recover the cross-sectional profiles from the online images, three different SfS techniques were tested: Horn’s method, Pentland’s method, and Shah’s method. Optical profilometry was used to validate the SfS cross section estimates. Shah’s method was found to have the highest fidelity among the three SfS approaches tested. Line resistance was predicted as a function of ShGFR based on the SfS-estimates of line cross section using Shah’s method. The online SfS-derived line resistance was found to be within 20% of offline resistance measurements done using the Kelvin sensing technique.

The patient-specific treatment of bone fractures using porous osteoconductive scaffolds has faced significant clinical challenges due to insufficient mechanical strength and bioactivity. These properties are essential for osteogenesis, bone bridging, and bone regeneration. Therefore, it is crucial to develop and characterize biocompatible, biodegradable, and mechanically robust scaffolds for effective bone regeneration. The objective of this study is to systematically investigate the mechanical performance of SimuBone, a medical-grade biocompatible and biodegradable material, using 10 distinct triply periodic minimal surface (TPMS) designs with various internal structures. To assess the material’s tensile properties, tensile structures based on ASTM D638-14 (Design IV) were fabricated, while standard torsion structures were designed and fabricated to evaluate torsional properties. Additionally, this work examined the compressive properties of the 10 TPMS scaffold designs, parametrically designed in the Rhinoceros 3D environment and subsequently fabricated using fused deposition modeling (FDM) additive manufacturing. The FDM fabrication process utilized a microcapillary nozzle (heated to 240 °C) with a diameter of 400 µm and a print speed of 10 mm/s, depositing material on a heated surface maintained at 60 °C. It was observed that SimuBone had a shear modulus of 714.79 ± 11.97 MPa as well as an average yield strength of 44 ± 1.31 MPa. Scaffolds fabricated with horizontal material deposition exhibited the highest tensile modulus (5404.20 ± 192.30 MPa), making them ideal for load-bearing applications. Also, scaffolds with large voids required thicker walls to prevent collapse. The P.W. Hybrid scaffold design demonstrated high vertical stiffness but moderate horizontal stiffness, indicating anisotropic mechanical behavior. The Neovius scaffold design balanced mechanical stiffness and porosity, making it a promising candidate for bone tissue engineering. Overall, the outcomes of this study pave the way for the design and fabrication of scaffolds with optimal properties for the treatment of bone fractures.

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.

The repair of orthopedic and maxillofacial defects in modern medicine currently relies heavily on the use of autograft, allograft, void fillers, or other structural material composites. This study examines the in vitro osteo regenerative potential of polycaprolactone (PCL) tissue scaffolding, fabricated via a three-dimensional (3D) additive manufacturing technology, i.e., a pneumatic micro extrusion (PME) process. The objectives of this study were: (i) To examine the innate osteoinductive and osteoconductive potential of 3D-printed PCL tissue scaffolding and (ii) To perform a direct in vitro comparison of 3D-printed PCL scaffolding with allograft Allowash^® cancellous bone cubes with regards to cell-scaffold interactions and biocompatibility with three primary human bone marrow (hBM) stem cell lines. This study specifically examined cell survival, cell integration, intra-scaffold cell proliferation, and differentiation of progenitor cells to investigate the potential of 3D-printed PCL scaffolds as an alternative to allograft bone material for the repair of orthopedic injuries. We found that mechanically robust PCL bone scaffolds can be fabricated via the PME process and the resulting material did not elicit detectable cytotoxicity. When the widely used osteogenic model SAOS-2 was cultured in PCL extract medium, no detectable effect was observed on cell viability or proliferation with multiple test groups showing viability ranges of 92.2% to 100% relative to a control group with a standard deviation of ±10%. In addition, we found that the honeycomb infill pattern of the 3D-printed PCL scaffold allowed for superior mesenchymal stem-cell integration, proliferation, and biomass increase. When healthy and active primary hBM cell lines, having documented in vitro growth rates with doubling times of 23.9, 24.67, and 30.94 h, were cultured directly into 3D-printed PCL scaffolds, impressive biomass increase values were observed. It was found that the PCL scaffolding material allowed for biomass increase values of 17.17%, 17.14%, and 18.18%, compared to values of 4.29% for allograph material cultured under identical parameters. It was also found that the honeycomb scaffold infill pattern was superior to the cubic and rectangular matrix structures, and provided a superior microenvironment for osteogenic and hematopoietic progenitor cell activity and auto-differentiation of primary hBM stem cells. Histological and immunohistochemical studies performed in this work confirmed the regenerative potential of PCL matrices in the orthopedic setting by displaying the integration, self-organization, and auto-differentiation of hBM progenitor cells within the matrix. Differentiation products including mineralization, self-organizing “proto-osteon” structures, and in vitro erythropoiesis were observed in conjunction with the documented expression of expected bone marrow differentiative markers including CD-99 (>70%), CD-71 (>60%), and CD-61 (>5%). All of the studies were conducted without the addition of any exogenous chemical or hormonal stimulation and exclusively utilized the abiotic and inert material polycaprolactone; setting this work apart from the vast majority of contemporary investigations into synthetic bone scaffold fabrication In summary, this study demonstrates the unique clinical potential of 3D-printed PCL scaffolds for stem cell expansion and incorporation into advanced microstructures created via PME manufacturing to generate a physiologically inert temporary bony defect graft with significant autograft features for enhanced end-stage healing.

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%.