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Native and carbonized freeze‐cast bacterial cellulose‐alginate (BC‐ALG) foams possess an ice‐templated honeycomb‐like architecture with remarkable properties. Their unique pore morphology consists of two levels of porosity: 20–50 µm diameter pores between, and 0.01–10 µm diameter pores within the cell‐walls. The mechanical properties of the BC‐ALG foams, a Young's modulus of up to 646.2 ± 90.4 kPa and a compressive yield strength of up to 37.1 ± 7.9 kPa, are high for their density and scale as predicted by the Gibson–Ashby model for cellular materials. Carbonizing the BC‐ALG foams in an inert atmosphere at 1000–1200 °C in a second processing step, both pore morphology and mechanical properties of the BC‐ALG remain well preserved with specific mechanical properties that are higher than those reported in the literature for similar foams. Also the electrical conductivity of the BC‐ALG foams is high at 1.68 ± 0.04 S cm−1 at a density of only 0.055 g cm−3, and is found to increase with density as predicted, and as a function of the degree of carbonization determined by both carbonization temperature and atmosphere. The property profile makes freeze‐cast BC‐ALG foams and their carbonized foams attractive for energy applications and as a sorbent. [ABSTRACT FROM AUTHOR]

The design and development of novel methodologies and customized materials to fabricate patient‐specific 3D printed organ models with integrated sensing capabilities can yield advances in smart surgical aids for preoperative planning and rehearsal. Here, 3D printed prostate models are demonstrated with physical properties of tissue and integrated soft electronic sensors using custom‐formulated polymeric inks. The models show high quantitative fidelity in static and dynamic mechanical properties, optical characteristics, and anatomical geometries to patient tissues and organs. The models offer tissue‐mimicking tactile sensation and behavior and thus can be used for the prediction of organ physical behavior under deformation. The prediction results show good agreement with values obtained from simulations. The models also allow the application of surgical and diagnostic tools to their surface and inner channels. Finally, via the conformal integration of 3D printed soft electronic sensors, pressure applied to the models with surgical tools can be quantitatively measured. [ABSTRACT FROM AUTHOR]

The development of a general‐purpose machine learning algorithm capable of quickly identifying optimal 3D‐printing settings can save manufacturing time and cost, reduce labor intensity, and improve the quality of 3D‐printed objects. Existing methods have limitations which focus on overall performance or one specific aspect of 3D‐printing quality. Here, for addressing the limitations, a multi‐objective Bayesian Optimization (BO) approach which uses a general‐purpose algorithm to optimize the black‐box functions is demonstrated and identifies the optimal input parameters of direct ink writing for 3D‐printing different presurgical organ models with intricate geometry. The BO approach enhances the 3D‐printing efficiency to achieve the best possible printed object quality while simultaneously addressing the inherent trade‐offs from the process of pursuing ideal outcomes relevant to requirements from practitioners. The BO approach also enables us to effectively explore 3D‐printing inputs inclusive of layer height, nozzle travel speed, and dispensing pressure, as well as visualize the trade‐offs between each set of 3D‐printing inputs in terms of the output objectives which consist of time, porosity, and geometry precisions through the Pareto front. [ABSTRACT FROM AUTHOR]