More Like this

1. Additive Manufacturing of High-Entropy Alloys: A Review

   https://doi.org/10.3390/e20120937  

   Chen, Shuying ; Tong, Yang ; Liaw, Peter ( December 2018 , Entropy)

   Owing to the reduced defects, low cost, and high efficiency, the additive manufacturing (AM) technique has attracted increasingly attention and has been applied in high-entropy alloys (HEAs) in recent years. It was found that AM-processed HEAs possess an optimized microstructure and improved mechanical properties. However, no report has been proposed to review the application of the AM method in preparing bulk HEAs. Hence, it is necessary to introduce AM-processed HEAs in terms of applications, microstructures, mechanical properties, and challenges to provide readers with fundamental understanding. Specifically, we reviewed (1) the application of AM methods in the fabrication of HEAs and (2) the post-heat treatment effect on the microstructural evolution and mechanical properties. Compared with the casting counterparts, AM-HEAs were found to have a superior yield strength and ductility as a consequence of the fine microstructure formed during the rapid solidification in the fabrication process. The post-treatment, such as high isostatic pressing (HIP), can further enhance their properties by removing the existing fabrication defects and residual stress in the AM-HEAs. Furthermore, the mechanical properties can be tuned by either reducing the pre-heating temperature to hinder the phase partitioning or modifying the composition of the HEA to stabilize the solid-solution phase or ductile intermetallic phase in AM materials. Moreover, the processing parameters, fabrication orientation, and scanning method can be optimized to further improve the mechanical performance of the as-built-HEAs. 

   more » « less
2. Best practices for generating and analyzing 16S rRNA amplicon data to track coral microbiome dynamics

   https://doi.org/10.3389/fmicb.2022.1007877  

   Silva, Denise P. ; Epstein, Hannah E. ; Vega Thurber, Rebecca L. ( February 2023 , Frontiers in Microbiology)

   Over the past two decades, researchers have searched for methods to better understand the relationship between coral hosts and their microbiomes. Data on how coral-associated bacteria are involved in their host’s responses to stressors that cause bleaching, disease, and other deleterious effects can elucidate how they may mediate, ameliorate, and exacerbate interactions between the coral and the surrounding environment. At the same time tracking coral bacteria dynamics can reveal previously undiscovered mechanisms of coral resilience, acclimatization, and evolutionary adaptation. Although modern techniques have reduced the cost of conducting high-throughput sequencing of coral microbes, to explore the composition, function, and dynamics of coral-associated bacteria, it is necessary that the entire procedure, from collection to sequencing, and subsequent analysis be carried out in an objective and effective way. Corals represent a difficult host with which to work, and unique steps in the process of microbiome assessment are necessary to avoid inaccuracies or unusable data in microbiome libraries, such as off-target amplification of host sequences. Here, we review, compare and contrast, and recommend methods for sample collection, preservation, and processing (e.g., DNA extraction) pipelines to best generate 16S amplicon libraries with the aim of tracking coral microbiome dynamics. We also discuss some basic quality assurance and general bioinformatic methods to analyze the diversity, composition, and taxonomic profiles of the microbiomes. This review aims to be a generalizable guide for researchers interested in starting and modifying the molecular biology aspects of coral microbiome research, highlighting best practices and tricks of the trade. 

   more » « less
3. A Review on Electrospun Luminescent Nanofibers: Photoluminescence Characteristics and Potential Applications

   https://doi.org/10.2174/1573413715666190112121113  

   George, Gibin ; Luo, Zhiping ( April 2020 , Current Nanoscience)

   Background: Photoluminescent materials have been used for diverse applications in thefields of science and engineering, such as optical storage, biological labeling, noninvasive imaging,solid-state lasers, light-emitting diodes, theranostics/theragnostics, up-conversion lasers, solar cells,spectrum modifiers, photodynamic therapy remote controllers, optical waveguide amplifiers andtemperature sensors. Nanosized luminescent materials could be ideal candidates in these applications.

   Objective: This review is to present a brief overview of photoluminescent nanofibers obtainedthrough electrospinning and their emission characteristics.

   Methods: To prepare bulk-scale nanosized materials efficiently and cost-effectively, electrospinningis a widely used technique. By the electrospinning method, a sufficiently high direct-current voltageis applied to a polymer solution or melt; and at a certain critical point when the electrostatic forceovercomes the surface tension, the droplet is stretched to form nanofibers. Polymer solutions or meltswith a high degree of molecular cohesion due to intermolecular interactions are the feedstock. Subsequentcalcination in air or specific gas may be required to remove the organic elements to obtainthe desired composition.

   Results: The luminescent nanofibers are classified based on the composition, structure, and synthesismaterial. The photoluminescent emission characteristics of the nanofibers reveal intriguing featuressuch as polarized emission, energy transfer, fluorescent quenching, and sensing. An overview of theprocess, controlling parameters and techniques associated with electrospinning of organic, inorganicand composite nanofibers are discussed in detail. The scope and potential applications of these luminescentfibers also conversed.

   Conclusion: The electrospinning process is a matured technique to produce nanofibers on a largescale. Organic nanofibers have exhibited superior fluorescent emissions for waveguides, LEDs andlasing devices, and inorganic nanofibers for high-end sensors, scintillators, and catalysts. Multifunctionalitiescan be achieved for photovoltaics, sensing, drug delivery, magnetism, catalysis, andso on. The potential of these nanofibers can be extended but not limited to smart clothing, tissueengineering, energy harvesting, energy storage, communication, safe data storage, etc. and it isanticipated that in the near future, luminescent nanofibers will find many more applications in diversescientific disciplines.

    

   more » « less
4. Machine Learning for Structural Materials

   https://doi.org/10.1146/annurev-matsci-110519-094700  

   Sparks, Taylor D. ; Kauwe, Steven K. ; Parry, Marcus E. ; Tehrani, Aria Mansouri ; Brgoch, Jakoah ( July 2020 , Annual Review of Materials Research)

   null (Ed.)

   The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compounds tend to derive their fascinating properties from a myriad of interactions spanning multiple scales, from localized chemical bonding to macroscopic interactions between grains. This diversity has limited the ability of researchers to develop new materials on a reasonable timeline. Fortunately, the advent of machine learning in materials science has provided a new approach to analyze high-dimensional space and identify correlations among the structure-composition-property-processing relationships that may have been previously missed. In this review, we examine some successful examples of using data science to improve known structural materials by analyzing fatigue and failure, and we discuss approaches to develop entirely new classes of structural materials in complex composition spaces including high-entropy alloys and bulk metallic glasses. Highlighting the recent advancement in this field demonstrates the power of data-driven methodologies that will hopefully lead to the production of market-ready structural materials. 

   more » « less
5. High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning

   https://doi.org/10.1039/d1ta07553e  

   Al-Fahdi, Mohammed ; Ouyang, Tao ; Hu, Ming ( December 2021 , Journal of Materials Chemistry A)

   Discovering new materials with desired properties has been a dominant and crucial topic of interest in the field of materials science in the past few decades. In this work, novel carbon allotropes and ternary B–C–N structures were generated using the state-of-the-art RG 2 code. All structures were fully optimized using density functional theory with first-principles calculations. Several hundred carbon allotropes and ternary B–C–N structures were identified to be superhard materials. The thermodynamic stability of some randomly selected superhard materials was confirmed by evaluating the full phonon dispersions in the Brillouin zone. The new carbon allotropes and ternary B–C–N structures possess a wide range of mechanical properties generally and Vickers hardness specifically. Through 2D Pearson's correlation map, we first reproduced the well-accepted explanation and relationship of the Vickers hardness of the generated structures with other mechanical properties such as shear modulus, bulk modulus, Pugh's ratio, universal anisotropy, and Poisson's ratio. We then propose two fundamentally new descriptors from the electronic level, namely local potential and electron localization function averaged over a unit cell, both of which exhibit a strong correlation with Vickers hardness. More importantly, these descriptors are easy to access from first-principles calculations (at least two orders of magnitude faster than the traditional calculation of elastic constants), and thus can serve as a fast and accurate approach for screening superhard materials. We also combined these new descriptors with known composition and structural descriptors in the machine learning training process. The new descriptors significantly enhance the performance of the trained machine learning model in predicting the Vickers hardness of unknown materials, which provides strong evidence for local potential and electron localization function to be considered in future high-throughput computation. This work unravels more fundamental but previously unexplored knowledge about superhard materials and the newly proposed electronic level descriptors are expected to accelerate the discovery of new superhard materials. 

   more » « less