O. Burak Özdoğanlar

Freeform 3D ice printing is emerging as a promising additive manufacturing method with potential applications in engineering, medicine, science, and art. Printing ice at the micrometer-millimeter scale is a challenging, high-frequency additive manufacturing process. Freeform ice 3D printing though is an early stage manufacturing process where complex geometries require significant time to design manually through an empirical trial-and-error approach, due to the nonlinear nature of droplet deposition and solidification along with unavoidable uncertainties and noise. This process would be tremendously improved with better understanding and harnessing the ability to visualize, analyze and adjust the printing process in real time. For this automation, using approaches such as closed loop control that have allowed tremendous advances in other fields like self-driving cars, plane autopilot, etc. would be very useful. In order to use closed loop control for this process, ice position and shape must be determined and transformed into in situ actionable data, which is complicated further by the transparency of ice and speed required for the phase transition from water to ice. We implemented vision techniques to build a data set to train a machine learning algorithm though an ice segmentation approach using a convolutional filter. We also implemented a hybrid optical flow algorithm (Farneback-FAST) to create a segmented video frame data set for training a neural network, Icenet. This approach was faster than the Farneback-FAST, segmenting frames in 25 ms, which allows for single and low multi droplet control. Our approaches will enable future closed loop control of the printing process and will be useful in a variety of areas including additive manufacturing, organ on a chip systems, and biomanufacturing.

Ice is emerging as a promising sacrificial material in the rapidly expanding area of advanced manufacturing for creating precise 3D internal geometries. Freeform 3D printing of ice (3D-ICE) can produce microscale ice structures with smooth walls, hierarchical transitions, and curved and overhang features. However, controlling 3D-ICE is challenging due to an incomplete understanding of its complex physics involving heat transfer, fluid dynamics, and phase changes. This work aims to advance our understanding of 3D-ICE physics by combining numerical modeling and experimentation. We developed a 2D thermo-fluidic model to analyze the transition from layered to continuous printing and a 3D thermo-fluidic model for the oblique deposition, which enables curved and overhang geometries. Experiments are conducted and compared with model simulations. We found that high droplet deposition rates enable the continuous deposition mode with a sustained liquid cap on top of the ice, facilitating smooth geometries. The diameter of ice structures is controlled by the droplet deposition frequency. Oblique deposition causes unidirectional spillover of the liquid cap and asymmetric heat transfer at the freeze front, rotating the freeze front. These results provide valuable insights for reproducible 3D-ICE printing that could be applied across various fields, including tissue engineering, microfluidics, and soft robotics.

Purpose: The reconstruction of the missing ear poses one of the most complex challenges to the plastic surgeon, and existing options carry significant disadvantages. The current standard involves harvesting autologous rib cartilage and carving it into an ear—a time-consuming process that requires multiple stages and exceptional artistic skill. The alternative is to use a prefabricated alloplastic implant which, although convenient, carries a high lifetime risk of complications. Our goal is to combine the best features of both approaches by creating off-the-shelf, patient-specific cartilage implants in complex anatomic shapes (e.g., an ear), through the development of a customized 3D freeze-micromilling (3DFM) technology that shapes frozen cartilage tissue into any configuration. Methods: A prototype of our 3DFM machine was designed and developed by custom adaptation of a standard CNC (computer numerical control) micromilling machine using specialized components (an enclosing chamber, a cooling stage, a frozen nitrogen sprayer, thermal and moisture measuring parts, and a micro-camera). A preliminary design of a three-dimensional shape was made in specialized rendering software to test the machine. Subsequently, porcine rib cartilage was procured from fresh specimens and frozen at -20-40 degrees. Using the constructed machine prototype, the cartilage was sculpted into desired shapes set by a computer toolpath. Once ideal settings were determined, this process was repeated using commercial-grade human cadaveric costal cartilage allograft (MTF Profile). Results: The constructed 3DFM machine prototype was able to successfully carve porcine costal cartilage into predetermined 3D geometries down to a precision of 100 microns. The same process was successfully reproduced using human cadaveric costal cartilage allograft, producing a complex 3D shape while ensuring that temperature was kept appropriately low throughout the milling process. Conclusion: We have developed a patent-pending, functioning 3DFM machine prototype capable of carving frozen human cartilage into predetermined shapes, while ensuring sub-zero temperature stability. Next step involves implantation in an animal model to ensure preservation of contours of the cartilage implants over time. Although these are early results, this novel 3DFM process could be used for various reconstructive and cosmetic surgical applications requiring complex cartilage shapes, with tremendous potential impact for both surgical practice and patient outcomes.

Purpose: Although costal cartilage allograft (CCA) has been available for several decades, there has been a resurgence in its use for rhinoplasty and other facial surgery indications. Newer CCA products claim improved stability and retention over time stemming from less aggressive tissue processing strategies. However, there is little data investigating in-vivo behavior of these new forms of CCA. This study aims to assess the structural retention of new CCA using a hairless but immunocompetent mouse model. Methods: Clinical-grade CCA (MTF Profile) was used to create oval cartilage discs through transverse section of costal implants. The discs were subcutaneously implanted in the dorsum of 9 SKH-1 hairless mice. Cartilage disc size and volume were evaluated at 2, 6, and 12 weeks post-operatively using clinical assessment, digital caliper measurements, and CT scan volumetric assessment. Measures were compared over time between time-points. Results: There were no significant changes in cartilage disc height (chi-squared = 3.26, p=0.20), width (chi-squared =2.89, p=0.24), or length (chi-squared=1.65, p=0.45) across timepoints. The size consistency comparing first versus last measurements did not change when stratifying CCA by gamma-radiation treated vs. not treated. Intraclass correlation coefficient (ICC) across days was found to be 0.93 (95% CI: 0.78, 0.98), indicating substantial consistency of the caliper measurements. Similarly, CT scan measurements in all three dimensions did not change significantly over the 12-week period. The clinical appearance of the implanted disc was equivalent at weeks 2 and 12. Conclusion: This preliminary animal study shows that the new form of clinically available CCA (MTF Profile) retains its shape and volume at 12 weeks in a hairless mouse model. Later time-points and histological analyses are underway to further elucidate new CCA in-vivo behavior. Results from this study will continue to offer insights into the long-term retention and cellular reactivity of these costal allografts, informing future clinical decisions and the potential for expanded indications in plastic surgery.

Transition metal carbides (MXenes) are novel 2D nanomaterials with exceptional properties, promising significant impact in applications such as energy storage, catalysis, and energy conversion. A major barrier preventing the widespread use of MXenes is the lack of methods for assembling MXene in 3D space without significant restacking, which degrades their performance. Here, this challenge is successfully overcome by introducing a novel material system: a 3D network of MXene formed on a porous ceramic backbone. The backbone dictates the network's 3D architecture while providing mechanical strength, gas/liquid permeability, and other beneficial properties. Freeze casting is used to fabricate a silica backbone with open pores and controlled porosity. Next, capilary flow is used to infiltrate MXene into the backbone from a dispersion. The system is then dried to conformally coat the pore walls with MXene, creating an interconnected 3D‐MXene network. The fabrication approach is reproducible, and the MXene‐infiltrated porous silica (MX‐PS) system is highly conductive (e.g., 340 S m −1 ). The electrical conductivity of MX‐PS is controlled by the porosity distribution, MXene concentration, and the number of infiltration cycles. Sandwich‐type supercapacitors with MX‐PS electrodes are shown to produce excellent areal capacitance (7.24 F cm −2 ) and energy density (0.32 mWh cm −2 ) with only 6% added MXene mass. This approach of creating 3D architectures of 2D nanomaterials will significantly impact many engineering applications.