Zein-Coated Zn Metal Particles-Incorporated Nanofibers: A Potent Fibrous Platform for Loading and Release of Zn Ions for Wound Healing Application.

Metal particles incorporated into polymer matrices in various forms and geometries are attractive material platforms for promoting wound healing and preventing infections. However, the fate of these metal particles and their degraded products in the tissue environment are still unknown, as both can produce cytotoxic effects and promote unwanted wound reactions. In this study, we develop biodegradable fibrous biomaterials embedded with metal particles that have an immune activation functions. Initially, biodegradable zinc (Zn) nanoparticles were modified with zein (G), a protein derived from corn. The zein-coated zinc particles (Z-G) were then embedded in polycaprolactone (P) fibers at different weight ratios to create fibrous biomaterials via electrospinning, which were subsequently analyzed for potential wound healing applications. We performed multimodal evaluations of the fibrous scaffolds, examining physicochemical properties such as fiber morphology, mechanical strength, hydrophilicity, degradation, and release of zinc ions (Zn2+), as well as biological properties, including in vitro cell culture studies. We provide evidence that the integration of 2.4 wt % of Z-G particles in polycaprolactone (PCL) nanofibrous scaffolds improved its physicochemical and biological functions. The in vitro cellular response of the scaffolds was evaluated using a series of cytotoxicity assays and immunocytochemistry analyses with three different cell types: mouse-derived fibroblast cell lines (NIH/3T3), human dermal fibroblasts (HDFn), and human umbilical vein endothelial cells (HUVECs). The composite fibrous scaffold exhibited robust activation and proliferation of NIH/3T3 and HDFn cells, along with a significant angiogenic potential in HUVECs. Immunocytochemistry confirmed elevated expression of vimentin and α-smooth muscle actin (α-SMA), suggesting that NIH/3T3 and Haden cells were highly differentiated into myofibroblasts. Additionally, the increased expression of CD31 and VE-cadherin in HUVECs suggests that the scaffold supports tube formation, thereby enhancing neovascularization and promoting an effective immune response. Overall, our findings demonstrate the regenerative potential of the self-enhanced Zn hemostatic bioscaffolds, which deliver both Zn2+ ions and zein proteins to nourish cells. This capability not only modulates cellular activities but also contributes to tissue repair and remodeling, making the scaffolds suitable for wound repair and various bioengineering applications.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

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.

Spectral Features Analysis for Print Quality Prediction in Additive Manufacturing: An Acoustics-Based Approach.

Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic-Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques.

Investigating Laser Ablation Process Parameters for the Fabrication of Customized Microneedle Arrays for Therapeutic Applications.

Microneedles are an innovation in the field of medicine that have the potential to revolutionize drug delivery, diagnostics, and cosmetic treatments. This innovation provides a minimally invasive means to deliver drugs, vaccines, and other therapeutic substances into the skin. This research investigates the design and manufacture of customized microneedle arrays using laser ablation. Laser ablation was performed using an ytterbium laser on a polymethyl methacrylate (PMMA) substrate to create a mold for casting polydimethylsiloxane (PDMS) microneedles. An experimental design was conducted to evaluate the effect of process parameters including laser pulse power, pulse width, pulse repetition, interval between pulses, and laser profile on the desired geometry of the microneedles. The analysis of variance (ANOVA) model showed that lasing interval, laser power, and pulse width had the highest influence on the output metrics (diameter and height) of the microneedle. The microneedle dimensions showed an increase with higher pulse width and vice versa with an increase in pulse interval. A response surface model indicated that the laser pulse width and interval (independent variables) significantly affect the response diameter and height (dependent variable). A predictive model was generated to predict the microneedle topology and aspect ratio varying from 0.8 to 1.5 based on the variation in critical input process parameters. This research lays the foundation for the design and fabrication of customized microneedles based on variations in specific input parameters for therapeutic applications in dermal sensors, drug delivery, and vaccine delivery.

Nanoimprint Lithography for Next-Generation Carbon Nanotube-Based Devices.

This research reports the development of 3D carbon nanostructures that can provide unique capabilities for manufacturing carbon nanotube (CNT) electronic components, electrochemical probes, biosensors, and tissue scaffolds. The shaped CNT arrays were grown on patterned catalytic substrate by chemical vapor deposition (CVD) method. The new fabrication process for catalyst patterning based on combination of nanoimprint lithography (NIL), magnetron sputtering, and reactive etching techniques was studied. The optimal process parameters for each technique were evaluated. The catalyst was made by deposition of Fe and Co nanoparticles over an alumina support layer on a Si/SiO2 substrate. The metal particles were deposited using direct current (DC) magnetron sputtering technique, with a particle ranging from 6 nm to 12 nm and density from 70 to 1000 particles/micron. The Alumina layer was deposited by radio frequency (RF) and reactive pulsed DC sputtering, and the effect of sputtering parameters on surface roughness was studied. The pattern was developed by thermal NIL using Si master-molds with PMMA and NRX1025 polymers as thermal resists. Catalyst patterns of lines, dots, and holes ranging from 70 nm to 500 nm were produced and characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Vertically aligned CNTs were successfully grown on patterned catalyst and their quality was evaluated by SEM and micro-Raman. The results confirm that the new fabrication process has the ability to control the size and shape of CNT arrays with superior quality.

Three-Dimensional-Printed Composite Structures: The Effect of LSCF Slurry Solid Loading, Binder, and Direct-Write Process Parameters.

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.

Understanding the Effect of Dispersant Rheology and Binder Decomposition on 3D Printing of a Solid Oxide Fuel Cell.

Solid oxide fuel cells (SOFCs) are a green energy technology that offers a cleaner and more efficient alternative to fossil fuels. The efficiency and utility of SOFCs can be enhanced by fabricating miniaturized component structures within the fuel cell footprint. In this research work, the parallel-connected inter-digitized design of micro-single-chamber SOFCs (µ-SC-SOFCs) was fabricated by a direct-write microfabrication technique. To understand and optimize the direct-write process, the cathode electrode slurry was investigated. Initially, the effects of dispersant Triton X-100 on LSCF (La0.6Sr0.2Fe0.8Co0.2O3-δ) slurry rheology was investigated. The effect of binder decomposition on the cathode electrode lines was evaluated, and further, the optimum sintering profile was determined. Results illustrate that the optimum concentration of Triton X-100 for different slurries was around 0.2-0.4% of the LSCF solid loading. A total of 60% of solid loading slurries had high viscosities and attained stability after 300 s. In addition, 40-50% solid loading slurries had relatively lower viscosity and attainted stability after 200 s. Solid loading and binder affected not only the slurry's viscosity but also its rheology behavior. Based on the findings of this research, a slurry with 50% solid loading, 12% binder, and 0.2% dispersant was determined to be the optimal value for the fabricating of SOFCs using the direct-write method. This research work establishes guidelines for fabricating the micro-single-chamber solid oxide fuel cells by optimizing the direct-write slurry deposition process with high accuracy.

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review.

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.

Engineered Nanomaterial Coatings for Food Packaging: Design, Manufacturing, Regulatory, and Sustainability Implications.

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.

3D Printing of Biodegradable Polymeric Microneedles for Transdermal Drug Delivery Applications.

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.