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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. 

The food industry is one of the most regulated businesses in the world and follows strict internal and regulated requirements to ensure product reliability and safety. In particular, the industry must ensure that biological, chemical, and physical hazards are controlled from the production and distribution of raw materials to the consumption of the finished product. In the United States, the FDA regulates the efficacy and safety of food ingredients and packaging. Traditional packaging materials such as paper, aluminum, plastic, and biodegradable compostable materials have gradually evolved. Coatings made with nanotechnology promise to radically improve the performance of food packaging materials, as their excellent properties improve the appearance, taste, texture, and shelf life of food. This review article highlights the role of nanomaterials in designing and manufacturing anti-fouling and antimicrobial coatings for the food packaging industry. The use of nanotechnology coatings as protective films and sensors to indicate food quality levels is discussed. In addition, their assessment of regulatory and environmental sustainability is developed. This review provides a comprehensive perspective on nanotechnology coatings that can ensure high-quality nutrition at all stages of the food chain, including food packaging systems for humanitarian purposes. 

Microneedle (MN) technology is an optimal choice for the delivery of drugs via the transdermal route, with a minimally invasive procedure. MN applications are varied from drug delivery, cosmetics, tissue engineering, vaccine delivery, and disease diagnostics. The MN is a biomedical device that offers many advantages including but not limited to a painless experience, being time-effective, and real-time sensing. This research implements additive manufacturing (AM) technology to fabricate MN arrays for advanced therapeutic applications. Stereolithography (SLA) was used to fabricate six MN designs with three aspect ratios. The MN array included conical-shaped 100 needles (10 × 10 needle) in each array. The microneedles were characterized using optical and scanning electron microscopy to evaluate the dimensional accuracy. Further, mechanical and insertion tests were performed to analyze the mechanical strength and skin penetration capabilities of the polymeric MN. MNs with higher aspect ratios had higher deformation characteristics suitable for penetration to deeper levels beyond the stratum corneum. MNs with both 0.3 mm and 0.4 mm base diameters displayed consistent force–displacement behavior during a skin-equivalent penetration test. This research establishes guidelines for fabricating polymeric MN for high-accuracy and low-cost 3D printing. 

Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain. 

Three-dimensional (3D) printing was utilized for the fabrication of a composite scaffold of poly(ε-caprolactone) (PCL) and calcium magnesium phosphate (CMP) bioceramics for bone tissue engineering application. Four groups of scaffolds, that is, PMC-0, PMC-5, PMC-10, and PMC-15, were fabricated using a custom 3D printer. Rheology analysis, surface morphology, and wettability of the scaffolds were characterized. The PMC-0 scaffolds displayed a smoother surface texture and an increase in the ceramic content of the composite scaffolds exhibited a rougher structure. The hydrophilicity of the composite scaffold was significantly enhanced compared to the control PMC-0. The effect of ceramic content on the bioactivity of fibroblast NIH/3T3 cells in the composite scaffold was investigated. Cell viability and toxicity studies were evaluated by comparing results from lactate dehydrogenase (LDH) and Alamar Blue (AB) colorimetric assays, respectively. The live-dead cell assay illustrated the biocompatibility of the tested samples with more than 100% of live cells on day 3 compared to the control one. The LDH release indicated that the composite scaffolds improved cell attachment and proliferation. In this research, the fabrication of a customized composite 3D scaffold not only mimics the rough textured architecture, porosity, and chemical composition of natural bone tissue matrices but also serves as a source for soluble ions of calcium and magnesium that are favorable for bone cells to grow. These 3D-printed scaffolds thus provide a desirable microenvironment to facilitate biomineralization and could be a new effective approach for preparing constructs suitable for bone tissue engineering. 

Articular cartilage is the avascular and aneural tissue which is the primary connective tissue covering the surface of articulat- ing bone. Traumatic damage or degenerative diseases can cause articular cartilage injuries that are common in the population. As a result, the demand for new therapeutic options is continually increasing for older people and traumatic young patients. Many attempts have been made to address these clinical needs to treat articular cartilage injuries, including osteoarthritis (OA); however, regenerating highly qualified cartilage tissue remains a significant obstacle. 3D bioprinting technology combined with tissue engineering principles has been developed to create biological tissue constructs that recapitulate the anatomical, structural, and functional properties of native tissues. In addition, this cutting-edge technology can precisely place multiple cell types in a 3D tissue architecture. Thus, 3D bioprinting has rapidly become the most innovative tool for manufacturing clinically applicable bioengineered tissue constructs. This has led to increased interest in 3D bioprinting in articular cartilage tissue engineering applications. Here, we tissue engineering. 

Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin. 

Nanoscale surface topographies mediated with biochemical cues influence the differentiation of stem cells into different lineages. This research focuses on the adsorption behavior of bone morphogenetic protein (BMP-2) on nanopatterned gold substrates, which can aid in the differentiation of bone and cartilage tissue constructs. The gold substrates were patterned as flat, pillar, linear grating, and linear-grating deep based, and the BMP-2 conformation in end-on configuration was studied over 20 ns. The linear grating deep substrate pattern had the highest adsorption energy of around 125 kJ/mol and maintained its radius of gyration of 18.5 Å, indicating a stable adsorption behavior. Secondary structures including α-helix and β-sheet displayed no denaturation, and thus, the bioavailability of the BMP-2, for the deep linear-grating pattern. Ramachandran plots for the wrist and knuckle epitopes indicated no steric hindrances and provided binding sites to type I and type II receptors. The deep linear-grating substrate had the highest number of contacts (88 atoms) within 5 Å of the gold substrate, indicating its preferred nanoscale pattern choice among the substrates considered. This research provides new insights into the atomistic adsorption of BMP-2 on nanoscale topographies of a gold substrate, with applications in biomedical implants and regenerative medicine.