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Abstract Purpose of ReviewThe purpose of this review is to share insights from recognized experts in 3D biopriniting on the recent advances in these technologies discussed during a recent workshop held in conjunction with the 2024 ISS National Laboratory Research and Development Conference (ISSRDC). We seek to answer how microgravity can be used as a disruptor to make further advances not possible through conventional means. Recent FindingsThis review will cover current efforts underway to use microgravity for 3D bioprinting. For instance multi-levitation biofabrication technology funded under the EU PULSE project is currently being used to create cardiovascular 3D in vitro models to better mimic cardiac and vascular physiology compared to organoids. These types of models could be expanded to other organ systems and disease models to use the environment of microgravity to unlock new signaling pathways to cure disease. SummaryThe major takeaway from this review is that microgravity will unlock new opportunities for 3D bioprinting that were simply not possible using conventional means. We provide forward looking answers to what microgravity will inspire from advanced biomaterials to new disease models to even creating a knowledge hub for 3D bioprinting to launch new platforms at record speeds. 

Designing and manufacturing devices at the micro- and nanoscales offers significant advantages, including high precision, quick response times, high energy density ratios, and low production costs. These benefits have driven extensive research in micro-electromechanical systems (MEMS) and nano-electromechanical systems (NEMS), resulting in various classifications of materials and manufacturing techniques, which are ultimately used to produce different classifications of MEMS devices. The current work aims to systematically organize the literature on MEMS in biomedical devices, encompassing past achievements, present developments, and future prospects. This paper reviews the current research trends, highlighting significant material advancements and emerging technologies in biomedical MEMS in order to meet the current challenges facing the field, such as ensuring biocompatibility, achieving miniaturization, and maintaining precise control in biological environments. It also explores projected applications, including use in advanced diagnostic tools, targeted drug delivery systems, and innovative therapeutic devices. By mapping out these trends and prospects, this review will help identify current research gaps in the biomedical MEMS field. By pinpointing these gaps, researchers can focus on addressing unmet needs and advancing state-of-the-art biomedical MEMS technology. Ultimately, this can lead to the development of more effective and innovative biomedical devices, improving patient care and outcomes. 

Abstract Medical devices play a crucial role in modern healthcare, providing innovative solutions for diagnosing, preventing, monitoring, and treating ailments. Artificial Intelligence is transforming the field of medical devices, offering unprecedented opportunities through diagnostic accuracy, personalized treatment plans, and enhancing patient outcomes. This review outlines the applications of artificial intelligence-based medical devices in healthcare specialties, especially in dentistry, medical imaging, ophthalmology, mental health, autism spectrum disorder diagnosis, oncology, and general medicine. Specifically, the review highlights advancements such as improved diagnostic accuracy, tailored treatment planning, and enhanced clinical outcomes in the above-mentioned applications. Regulatory approval remains a key issue, where medical devices must be approved or cleared by the United States Food and Drug Administration to establish their safety and efficacy. The regulatory guidance pathway for artificial intelligence-based medical devices is presented and moreover the critical technical, ethical, and implementation challenges that must be addressed for large-scale adoption are discussed. The review concludes that the intersection of artificial intelligence with the medical device domain and internet-enabled or enhanced technology, such as biotechnology, nanotechnology, and personalized therapeutics, enables an enormous opportunity to accelerate customized and patient-centered care. By evaluating these advancements and challenges, the study aims to present insights into the future trajectory of smart medical technologies and their role in advancing personalized, patient-centered care. 

The ever-increasing global energy demand necessitates the development of efficient, sustainable, and high-performance energy storage systems. Nanotechnology, through the manipulation of materials at the nanoscale, offers significant potential for enhancing the performance of energy storage devices due to unique properties such as increased surface area and improved conductivity. This review paper investigates the crucial role of nanotechnology in advancing energy storage technologies, with a specific focus on capacitors and batteries, including lithium-ion, sodium–sulfur, and redox flow. We explore the diverse applications of nanomaterials in batteries, encompassing electrode materials (e.g., carbon nanotubes, metal oxides), electrolytes, and separators. To address challenges like interfacial side reactions, advanced nanostructured materials are being developed. We also delve into various manufacturing methods for nanomaterials, including top–down (e.g., ball milling), bottom–up (e.g., chemical vapor deposition), and hybrid approaches, highlighting their scalability considerations. While challenges such as cost-effectiveness and environmental concerns persist, the outlook for nanotechnology in energy storage remains promising, with emerging trends including solid-state batteries and the integration of nanomaterials with artificial intelligence for optimized energy storage. 

Microcapsules provide a microenvironment by improving the protection and delivery of cells and drugs to specific tissue areas, promoting cell integration and tissue regeneration. Effective microcapsules must not only be permeable for micronutrient diffusion but mechanically stable. Alginate hydrogel is one of the commonly used biomaterials for fabricating microcapsules due to its gel-forming ability and low toxicity. However, its mechanical instability, inertness, and excessive porosity have impeded its use. Embedding nanofibrils in the alginate hydrogel microcapsules improves their biological and mechanical properties. In this research, electrospun composite nanofibers of PCL–gelatin (PG) were first fabricated, characterized, and cryoground. The filtered and cryoground powder solution was mixed with the alginate solution and through electrospray, fabricated into microcapsules. Parameters such as flow rate, voltage, and hydrogel composition, which are critical in the electrostatic encapsulation process, were optimized. The microcapsules were further immersed in different solvent environments (DI water, complete media, and PBS), which were observed and compared for their morphology, size distribution, and mechanical stability properties. The average diameters of the PG nanofibers ranged between 0.2 and 2 μm, with an average porosity between 58 and 73%. The average size of the microcapsules varied between 300 and 900 μm, depending on the solvent environment. Overall, results showed an improved alginate 3D hydrogel network suitable for biomedical applications. 

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes. 

In this research, a direct-write 3D-printing method was utilized for the fabrication of inter-digitized solid oxide fuel cells (SOFCs) using ceramic materials. The cathode electrode was fabricated using the LSCF (La0.6Sr0.2Fe0.8Co0.2O3-δ) slurry loading and the Polyvinyl butyral (PVB) binder. The rheological parameters of slurries with varying LSCF slurry loading and PVB binder concentration were evaluated to determine their effect on the cathode trace performance in terms of microstructure, size, and resistance. Additionally, the dimensional shrinkage of LSCF lines after sintering was investigated to realize their influence on cathode line width and height. Moreover, the effect of the direct-write process parameters such as pressure, distance between the nozzle and substrate, and speed on the cathode line dimensions and resistance was evaluated. LSCF slurry with 50% solid loading, 12% binder, and 0.2% dispersant concentration was determined to be the optimal value for the fabrication of SOFCs using the direct-write method. The direct-write process parameters, in addition to the binder and LSCF slurry concentration ratios, had a considerable impact on the microstructure of cathode lines. Based on ANOVA findings, pressure and distance had significant effects on the cathode electrode resistance. An increase in the distance between the nozzle and substrate, speed, or extrusion pressure of the direct writing process increased the resistance of the cathode lines. These findings add to the ongoing effort to refine SOFC fabrication techniques, opening the avenues for advanced performance and efficiency of SOFCs in energy applications. 

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.