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1. Manufacturing of Soft Magnets using the Fused Filament Fabrication Process

   https://doi.org/doi.org/10.13140/RG.2.2.32731.90407  

   Hasanov, Seymur; Fidan, Ismail (January 2020, MatEDU Resource Center)

   Stoebe, Thomas (Ed.)

   Rare-earth (RE) materials are currently used to fabricate permanent magnets through various additive manufacturing (AM) methods. Fused filament fabrication (FFF) is one of the most commonly used polymer-based AM methods and has recently been used to produce metal-matrix composites, known as “green parts,” using a metal powder-infused filament. The FFF method has gained much attention in various industries including the automotive, aerospace, and medical fields. Therefore, involving RE in the FFF process using magnetic powder-infused filaments promises to result in the fabrication of low-cost, efficient, and complex magnetic components based on application areas. This module introduces the FFF process and provides a case study for high school and technical college students to gain a fundamental understanding of how magnetic powders are infused and how parts are fabricated using this method. 

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2. Additive Manufacturing of Magnetic Materials for Energy, Environment, Healthcare, and Industry Applications

   https://doi.org/10.1002/adfm.202416823  

   Rezaei, Bahareh; Moni, Hur‐E‐Jannat; Karampelas, Ioannis_H; Sharma, Arjun; Mostufa, Shahriar; Azizi, Ebrahim; Liu, Xiaolong; Zeng, Minxiang; Gómez‐Pastora, Jenifer; He, Rui; et al (November 2024, Advanced Functional Materials)

   Abstract Recent advancements in additive manufacturing (AM) techniques have significantly expanded the potential applications of magnetic materials and devices. This review summarizes various AM methods, including ink‐based and ink‐free processes, and their use in fabricating complex magnetic structures with specific properties tailored for different fields. Key applications discussed include energy‐harvesting devices enhanced with magnetic nanoparticles, water decontamination through magnetically guided microswimmers, and magnetic soft composites in robotics and medical devices. In addition, the integration of AM in producing wearable and flexible magnetic sensors is highlighted, demonstrating its transformative impact on human‐machine interactions. Furthermore, rare‐earth‐free magnets and electric motor designs enabled by AM techniques are also discussed. Despite material compatibility and scalability challenges, AM provides opportunities for creating multifunctional, sustainable devices with reduced waste. Future research should focus on optimizing these techniques for complex applications and large‐scale production, particularly in eco‐friendly and industrial settings. 

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3. Additive manufacturing as a tool for high-throughput experimentation

   https://doi.org/10.20517/jmi.2022.19  

   Xiong, Wei (January 2022, Journal of Materials Informatics)

   Additive manufacturing (AM) is a disruptive technology with a unique capability in fabricating parts with complex geometry and fixing broken supply chains. However, many AM techniques are complicated with their processing features due to complex heating and cooling cycles with the melting of feedstock materials. Therefore, it is quite challenging to directly apply the materials design and processing optimization method used for conventional manufacturing to AM techniques. In this viewpoint paper, we discuss some of the ongoing efforts of high-throughput (HT) experimentation, which can be used for materials development and processing design. Particularly, we focus on the beam- and powder-based AM techniques since these methods have demonstrated success in HT experimentation. In addition, we propose new opportunities to apply AM techniques as the materials informatic tools contributing to materials genome. 

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4. Digital Light Process 3D Printing of Magnetically Aligned Liquid Crystalline Elastomer Free–forms

   https://doi.org/10.1002/adma.202414209  

   Herman, Jeremy_A; Telles, Rodrigo; Cook, Caitlyn_C; Leguizamon, Samuel_C; Lewis, Jennifer_A; Kaehr, Bryan; White, Timothy_J; Roach, Devin_J (October 2024, Advanced Materials)

   Abstract Liquid crystalline elastomers (LCEs) are anisotropic soft materials capable of large dimensional changes when subjected to a stimulus. The magnitude and directionality of the stimuli‐induced thermomechanical response is associated with the alignment of the LCE. Recent reports detail the preparation of LCEs by additive manufacturing (AM) techniques, predominately using direct ink write printing. Another AM technique, digital light process (DLP) 3D printing, has generated significant interest as it affords LCE free‐forms with high fidelity and resolution. However, one challenge of printing LCEs using vat polymerization methods such as DLP is enforcing alignment. Here, we document the preparation of aligned, main‐chain LCEs via DLP 3D printing using a 100 mT magnetic field. Systematic examination isolates the contribution of magnetic field strength, alignment time, and build layer thickness on the degree of orientation in 3D printed LCEs. Informed by this fundamental understanding, DLP is used to print complex LCE free‐forms with through‐thickness variation in both spatial orientations. The hierarchical variation in spatial orientation within LCE free‐forms is used to produce objects that exhibit mechanical instabilities upon heating. DLP printing of aligned LCEs opens new opportunities to fabricate stimuli‐responsive materials in form factors optimized for functional use in soft robotics and energy absorption. 

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5. Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment

   Xu, Hao; Zhou, Zhengyang; Hong, Pengyu (July 2024, AI4Science Workshop of 41st International Conference on Machine Learning)

   Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, iso- mer recognition, and peak assignment. In response, this paper introduces a novel solution, Knowledge-Guided Multi-Level Multimodal Alignment with Instance-Wise Discrimination (K-M3 AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K- M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge- guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3 AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores the effectiveness of K-M3AID in multiple zero- shot tasks. 

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