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Abstract IntroductionMechanical forces provide critical biological signals to cells. Within the distal lung, tensile forces act across the basement membrane and epithelial cells atop. Stretching devices have supported studies of mechanical forces in distal lung epithelium to gain mechanistic insights into pulmonary diseases. However, the integration of curvature into devices applying mechanical forces onto lung epithelial cell monolayers has remained challenging. To address this, we developed a hammock-shaped platform that offers desired curvature and mechanical forces to lung epithelial monolayers. MethodsWe developed hammocks using polyethylene terephthalate (PET)-based membranes and magnetic-particle modified silicone elastomer films within a 48-well plate that mimic the alveolar curvature and tensile forces during breathing. These hammocks were engineered and characterized for mechanical and cell-adhesive properties to facilitate cell culture. Using human small airway epithelial cells (SAECs), we measured monolayer formation and mechanosensing using F-Actin staining and immunofluorescence for cytokeratin to visualize intermediate filaments. ResultsWe demonstrate a multi-functional design that facilitates a range of curvatures along with the incorporation of magnetic elements for dynamic actuation to induce mechanical forces. Using this system, we then showed that SAECs remain viable, proliferate, and form an epithelial cell monolayer across the entire hammock. By further applying mechanical stimulation via magnetic actuation, we observed an increase in proliferation and strengthening of the cytoskeleton, suggesting an increase in mechanosensing. ConclusionThis hammock strategy provides an easily accessible and tunable cell culture platform for mimicking distal lung mechanical forces in vitro. We anticipate the promise of this culture platform for mechanistic studies, multi-modal stimulation, and drug or small molecule testing, extendable to other cell types and organ systems. 

Abstract This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives. 

Abstract This paper studies the concept of manufacturing systems that autonomously learn how to build parts to a user-specified performance. To perform such a function, these manufacturing systems need to be adaptable to continually change their process or design parameters based on new data, have inline performance sensing to generate data, and have a cognition element to learn the correct process or design parameters to achieve the specified performance. Here, we study the cognition element, investigating a panel of supervised and reinforcement learning machine learning algorithms on a computational emulation of a manufacturing process, focusing on machine learning algorithms that perform well under a limited manufacturing, thus data generation, budget. The case manufacturing study is for the manufacture of an acoustic metamaterial and performance is defined by a metric of conformity with a desired acoustic transmission spectra. We find that offline supervised learning algorithms, which dominate the machine learning community, require an infeasible number of manufacturing observations to suitably optimize the manufacturing process. Online algorithms, which continually modify the parameter search space to focus in on favorable parameter sets, show the potential to optimize a manufacturing process under a considerably smaller manufacturing budget. 

Abstract Circular supply chains require more accurate product labeling and traceability. In the apparel industry, product life cycle management is hampered in part by inaccurate, poorly readable, and detachable standard care labels. Instead, this article seeks to enable a labeling system capable of being integrated into the fabric itself, intrinsically recyclable, low‐cost, encodes information, and allows rapid readout after years of normal use. In this work, all‐polymer photonic crystals are designed and then fabricated by thermal drawing with >100 layers having sub‐micrometer individual thickness and low refractive index contrast (Δn = 0.1). The fibers exhibit reflectance features in the 1–5.5 µm wavelength range, characterized using insitu Fourier transform infrared spectroscopy. Drawn photonic fibers are then woven into fabrics, characterized by near‐infrared spectroscopy and short‐wave infrared imaging, techniques commonly used in industrial facilities for sorting materials. The fibers’ optical design also enables the use of overtone peaks to avoid overlap with parasitic molecular absorption, substantially improving the signal‐to‐noise ratio (and therefore ease and speed) of readout. The ability to produce kilometers of fiber that are compatible with existing textile manufacturing processes, coupled with low input material cost, make these a potential market‐viable improvement over the standard care label. 

Abstract Electrohydrodynamic jet (e‐jet) printing is a high‐resolution additive manufacturing technique that holds promise for the fabrication of customized micro‐devices. In this companion paper set, e‐jet printing is investigated for its capability in depositing multilayer thin‐films with microscale spatial resolution and nanoscale thickness resolution to create arrays of 1D photonic crystals (1DPC). In this paper, an empirical model for the deposition process is developed, relating process and material parameters to the thickness and uniformity of the patterns. Standard macroscale measurements of solid surface energy and liquid surface tension are used in conjunction with microscale contact angle measurements to understand the length scale dependence of material properties and their impact on droplet merger into uniform microscale thin‐films. The model is validated with several photopolymer inks, a subset of which is used to create pixelated, multilayer arrays of 1DPCs with uniformity and resolution approaching standards in the optics manufacturing industry. It is found that the printed film topography at the microscale can be predicted based on the surface energetics at the microscale. Due to the flexibility in design provided by the e‐jet process, these findings can be generalized for fabricating additional multimaterial, multilayer micro‐ and nanostructures with applications beyond the field of optics. 

Abstract Additive manufacturing systems that can arbitrarily deposit multiple materials into precise, 3D spaces spanning the micro‐ to nanoscale are enabling novel structures with useful thermal, electrical, and optical properties. In this companion paper set, electrohydrodynamic jet (e‐jet) printing is investigated for its ability in depositing multimaterial, multilayer films with microscale spatial resolution and nanoscale thickness control, with a demonstration of this capability in creating 1D photonic crystals (1DPCs) with response near the visible regime. Transfer matrix simulations are used to evaluate different material classes for use in a printed 1DPC, and commercially available photopolymers with varying refractive indices (n= 1.35 to 1.70) are selected based on their relative high index contrast and fast curing times. E‐jet printing is then used to experimentally demonstrate pixelated 1DPCs with individual layer thicknesses between 80 and 200 nm, square pixels smaller than 40 µm across, with surface roughness less than 20 nm. The reflectance characteristics of the printed 1DPCs are measured using spatially selective microspectroscopy and correlated to the transfer matrix simulations. These results are an important step toward enabling cost‐effective, custom‐fabrication of advanced imaging devices or photonic crystal sensing platforms.