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.

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.

Lithium-ion batteries (LIBs) have become the leading energy storage technology because of their high specific energy, excellent efficiency, and longer lifespan. This review offers a comprehensive overview of the lithium battery industry, covering lithium materials and the global supply chain, as well as examining traditional and sustainable extraction methods. The discussion includes the technical and environmental challenges of each extraction method, with particular emphasis on feedstock selection, which greatly influences lithium recovery efficiency, yield, and ecological impact, and capital and operating expenditures. It also investigates the growth of the global battery recycling industry and provides an outlook on innovations in this area. These innovations include recycling systems, a case study on Biomass Energy Systems Inc., and the battery chemistries needed to support a circular economy. The review highlights industrial applications of LIBs in sectors such as automotive, consumer electronics, and aerospace. Finally, it addresses supply chain and recycling challenges related to LIBs, positioning cost, environmental footprint, and regulatory compliance as central considerations for the future of the battery industry.

Lithium-ion batteries (LIBs) have become the prominent energy storage technology because of their high specific energy, longer lifespan, and excellent efficiency. Traditional lithium extraction processes are energy intensive and time-consuming. Direct lithium extraction (DLE) methods provide a more sustainable and efficient alternative. This review offers a comprehensive overview of lithium-ion battery resources and direct lithium extraction methods. The detailed discussion of the DLE methods, which include adsorption, ion exchange, solvent extraction, membranes separation, and electro-chemical systems is presented. A comprehensive analysis of the recent technological advances of the direct lithium extraction processes in terms of technology readiness levels, and commercial potential is reported. The advantages and the technical challenges of the DLE methods are also reported. Finally, the review outlines the artificial intelligence outlook of the DLE processes. The review aims to provide deeper insights into the limitations and the opportunities of DLE methods towards crucial future research efforts for lithium-ion batteries advancements.

Additive manufacturing (AM) provides greater design flexibility and shorter lead times, butqualifying AM parts remains difficult due to complex thermal histories that create heterogeneous microstructures and location-dependent mechanical properties. These factors make conventional testing costly and time-consuming. This study addresses these challenges by characterizing the location-specific mechanical behavior of SS 316L parts produced by Directed Energy Deposition. Mini tensile specimens (3 mm × 1.2 mm × 0.25 mm) were extracted from various regions of an AM part to assess stiffness, yield strength, tensile strength, and strain-to-failure. The results reveal spatial variability in mechanical performance, which correlates with the in situ temperature during fabrication. Maintaining the temperature at 1300 ± 50°C ensures consistent mechanical properties throughout the component. This approach also identifies critical regions within the part that have lower strength and failure strain and examines their dependence on temperature. Overall, the study provides new insight into the spatial heterogeneity of AM processes and establishes a foundation for more representative performance assessments.