Additive manufacturing (AM) has gained significant attention due to its ability to drive technological development as a sustainable, flexible, and customizable manufacturing scheme. Among the various AM techniques, direct ink writing (DIW) has emerged as the most versatile 3D printing technique for the broadest range of materials. DIW allows printing of practically any material, as long as the precursor ink can be engineered to demonstrate appropriate rheological behavior. This technique acts as a unique pathway to introduce design freedom, multifunctionality, and stability simultaneously into its printed structures. Here, a comprehensive review of DIW of complex 3D structures from various materials, including polymers, ceramics, glass, cement, graphene, metals, and their combinations through multimaterial printing is presented. The review begins with an overview of the fundamentals of ink rheology, followed by an in‐depth discussion of the various methods to tailor the ink for DIW of different classes of materials. Then, the diverse applications of DIW ranging from electronics to food to biomedical industries are discussed. Finally, the current challenges and limitations of this technique are highlighted, followed by its prospects as a guideline toward possible futuristic innovations.

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Understanding of the material properties of layered transition-metal dichalcogenides (TMDs) is critical for their applications in flexible electronics. Data-driven machine learning (ML)-based approaches are being developed in contrast to the traditional experimental or computational methods to predict and understand material properties under varied operating conditions. In this study, we used two ML algorithms, namely, long short-term memory (LSTM) and feed forward neural network (FFNN), combined with molecular dynamics (MD) simulations to predict the mechanical properties of MX 2 (M = Mo, W and X = S, Se) TMDs. The LSTM model is found to be capable of predicting the entire stress−strain response, whereas the FFNN is used to predict material properties such as fracture stress, fracture strain, and Young's modulus. The effects of operating temperature, chiral orientation, and pre-existing crack size on the mechanical properties are thoroughly investigated. We carried out 1440 MD simulations to produce the input dataset for the neural network models. Our results indicate that both LSTM and FFNN are capable of predicting the mechanical response of monolayer TMDs under different conditions with more than 95% accuracy. The FFNN model exhibits lower computational cost than LSTM; however, the capability of the LSTM model to predict the entire stress−strain curve is advantageous for assessing material properties. The study paves the pathway toward extending this approach to predict other important properties, such as optical, electrical, and magnetic properties of TMDs.

The refining process of petroleum crude oil generates asphaltenes, which poses complicated problems during the production of cleaner fuels. Following refining, asphaltenes are typically combusted for reuse as fuel or discarded into tailing ponds and landfills, leading to economic and environmental disruption. Here, we show that low-value asphaltenes can be converted into a high-value carbon allotrope, asphaltene-derived flash graphene (AFG), via the flash joule heating (FJH) process. After successful conversion, we develop nanocomposites by dispersing AFG into a polymer effectively, which have superior mechanical, thermal, and corrosion-resistant properties compared to the bare polymer. In addition, the life cycle and technoeconomic analysis show that the FJH process leads to reduced environmental impact compared to the traditional processing of asphaltene and lower production cost compared to other FJH precursors. Thus, our work suggests an alternative pathway to the existing asphaltene processing that directs toward a higher value stream while sequestering downstream emissions from the processing.