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1. Extrinsic control of advanced manufacturing techniques for bioinspired materials

   Naleway, S.E.; Porter, D.L.; Fernquist, J.; Yin, T.J.; Schmitz, M.; Hotz, E.; Alexander, J.; Mroz, M. (January 2022, International Conference and Expo on Advanced Ceramics and Composites 2022, January 23-28, 2022Daytona Beach, FL)

   Bioinspired fabrication techniques that are able to mimic the structure and properties of biological materials are of interest to a wide range of scientific and engineering fields. We propose that these bioinspired techniques can be controlled through either intrinsic (those that modify from within by altering the constituents) or extrinsic (those that apply external forces or templates) means. Through these classifications, examples of extrinsic (through energized magnetic and ultrasound external fields) freeze cast, aerogel, and FDM printed structures will be discussed with a focus on providing advanced control of the final material structure and properties. Applications in biomedical and defense applications will be discussed. 

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2. 3D‐printed biomimetic and bioinspired soft actuators

   https://doi.org/10.1049/csy2.70001  

   Sparks, Sonja_S; Obando, Alejandro_G; Li, Yizong; Chen, Si; Yao, Shanshan; Qiu, Kaiyan (November 2024, IET Cyber-Systems and Robotics)

   Abstract A major intent of scientific research is the replication of the behaviour observed in natural spaces. In robotics, these can be through biomimetic movements in devices and inspiration from diverse actions in nature, also known as bioinspired features. An interesting pathway enabling both features is the fabrication of soft actuators. Specifically, 3D‐printing has been explored as a potential approach for the development of biomimetic and bioinspired soft actuators. The extent of this method is highlighted through the large array of applications and techniques used to create these devices, as applications from the movement of fern trees to contraction in organs are explored. In this review, different 3D‐printing fabrication methods, materials, and types of soft actuators, and their respective applications are discussed in depth. Finally, the extent of their use for present operations and future technological advances are discussed. 

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3. Enhancing membrane-based soft materials with magnetic reconfiguration events

   https://doi.org/10.1038/s41598-022-05501-7  

   Makhoul-Mansour, Michelle M.; El-Beyrouthy, Joyce B.; Mao, Leidong; Freeman, Eric C. (February 2022, Scientific Reports)

   Abstract Adaptive and bioinspired droplet-based materials are built using the droplet interface bilayer (DIB) technique, assembling networks of lipid membranes through adhered microdroplets. The properties of these lipid membranes are linked to the properties of the droplets forming the interface. Consequently, rearranging the relative positions of the droplets within the network will also alter the properties of the lipid membranes formed between them, modifying the transmembrane exchanges between neighboring compartments. In this work, we achieved this through the use of magnetic fluids or ferrofluids selectively dispersed within the droplet-phase of DIB structures. First, the ferrofluid DIB properties are optimized for reconfiguration using a coupled experimental-computational approach, exploring the ideal parameters for droplet manipulation through magnetic fields. Next, these findings are applied towards larger, magnetically-heterogeneous collections of DIBs to investigate magnetically-driven reconfiguration events. Activating electromagnets bordering the DIB networks generates rearrangement events by separating and reforming the interfacial membranes bordering the dispersed magnetic compartments. These findings enable the production of dynamic droplet networks capable of modifying their underlying membranous architecture through magnetic forces. 

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4. Characterization and Quantification of Hierarchical Particle Microstructures in External Field-Processed Composites

   https://doi.org/10.1115/SMASIS2021-68127  

   Papula, Dashiell; Ounaies, Zoubeida; von Lockette, Paris; Widdowson, Denise; Erol, Anil; Masud, Abdulla (September 2021, ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)

   Abstract In this study, we discuss the characterization and quantification of composite microstructures formed by the external field manipulation of high aspect ratio magnetic particles in an elastomeric matrix. In our prior work, we have demonstrated that the simultaneous application of electric and magnetic fields on hard magnetic particles with geometric anisotropy can create a hierarchy of structures at different length scales, which can be used to achieve a wide range of properties. We aim to characterize these hierarchical structures and relate them to final composite properties so we can achieve our ultimate goal of designing a material for a prescribed performance. The complex particle structures are formed during processing by using electric and magnetic fields, and they are then locked-in by curing the polymer matrix around the particles. The model materials used in the study are barium hexaferrite (BHF) particles and polydimethylsiloxane (PDMS) elastomer. BHF was selected for its hard magnetic properties and high geometric anisotropy. PDMS was selected for its good mechanical properties and its tunable curing kinetics. The resulting BHF-PDMS composites are magnetoactive, i.e., they will deform and actuate in response to magnetic fields. In order to investigate the resulting particle orientation, distribution and alignment and to predict the composite’s macro scale properties, we developed techniques to quantify the particle structures. The general framework we developed allows us to quantify and directly compare the microstructures created within the composites. To identify structures at the different length scales, images of the composite are taken using both optical microscopy and scanning electron microscopy. We then use ImageJ to analyze them and gather data on particle size, location, and orientation angle. The data is then exported to MATLAB, and is used to run a Minimum Spanning Tree Algorithm to classify the particle structures, and von Mises Distributions to quantify the alignment of said structures. Important findings show 1) the ability to control structure using a combination of external electric, magnetic and thermal fields; 2) that electric fields promote long range order while magnetic fields promote short-range order; and 3) the resulting hierarchical structure greatly influence bulk material properties. Manipulating particles in a composite material is technologically important because changes in microstructure can alter the properties of the bulk material. The multifield processing we investigate here can form the basis for next generation additive manufacturing platforms where desired properties are tailored locally through in-situ hierarchical control of particle arrangements. 

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5. Machine Learning Methods in Drug Discovery

   https://doi.org/10.3390/molecules25225277  

   Patel, Lauv; Shukla, Tripti; Huang, Xiuzhen; Ussery, David W.; Wang, Shanzhi (November 2020, Molecules)

   null (Ed.)

   The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed. 

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