The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques The focus of this review is the various attempts to improve new hydroxyapatite-based materials First, we comment on the most used processing routes, highlighting their advantages and disadvantages We will now focus on other routes, less common due to their specificity and/or recent development We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy The review concludes with an outlook for possible new research directions

https://www.mdpi.com/1420-3049/26/11/3190/pdf

Advanced Materials Based on Nanosized Hydroxyapatite

Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed.

/pdf/prediction-and-construction-of-energetic-materials-based-on-1dy719v5.pdf

Prediction and Construction of Energetic Materials Based on Machine Learning Methods

Optimized energy storage performance in NaNbO3-based ceramics via composition modification and micro-structure control

Advanced electrostatic energy-storage science and technology based on dielectric ceramics capacitors are widely applied in pulsed power systems. Recently, entropy increasing design engineering has been considered as an available method to promote electrical characteristics in dielectric energy-storage ceramics. In this study, medium-entropy (Na0.25Bi0.25Ca0.25Sr0.25)TiO3 (NBCSTO) ceramics with good energy-storage properties were obtained by a conventional solid-state reaction method. The microstructure and element analysis results show that a perovskite structure was identified for the NBCSTO sample, and no secondary phases were detected. Furthermore, a dense microstructure and small grain size lead to a high breakdown electric field strength (EB) of 430 kV/cm of the NBCSTO ceramic, which also displays the features of relaxor ferroelectrics. Importantly, a high Wrec⁓5.84 J/cm3 along with a medium η⁓80 % were simultaneously achieved in the NBCSTO ceramic, as well as good energy-storage stability. Moreover, the NBCSTO ceramic exhibits both a high PD and a fast discharging speed. All the above results indicate that the NBCSTO ceramic may be potential eco-friendly material for capacitor used in the pulsed power systems. 

Achieving high energy-storage performance of medium-entropy (Na0.25Bi0.25Ca0.25Sr0.25)TiO3 lead-free relaxor ferroelectric ceramic for pulsed power capacitor

Experimental and theoretical characterization of Dy-doped hydroxyapatites

Structural features modeling of substituted hydroxyapatite nanopowders as bone fillers via machine learning

Controllable preparation of Na0.4K0.1Bi0.5TiO3–CaZrO3–NaNbO3 nanoceramics with excellent temperature-stable energy storage performance by combining sol-gel synthesis and two-step sintering

It is difficult to take both texture degree and density into account by the conventional method, especially when one-dimensional particles are used as templates to prepare textured ferroelectric ceramics. Here, we proposed a strategy to improve the texture degree and density of ceramics simultaneously by a grain-orientation-engineered technique. To solve the problem of directional arrangement of template particles, template layers without mixed matrix were prepared by the brushing technique. Dense textured NaSr2Nb5O15 ceramics with grain size gradient distribution were designed by the lamination of template and matrix layers. An evaluation method of texture degree was developed. Ferroelastic and ferroelectric domains were observed simultaneously in the oriented grains, which was further confirmed that the ferroelectric-ferroelastic phase transition occurred for the NaSr2Nb5O15 ceramics at the low temperature. The obtained textured NaSr2Nb5O15 ceramics exhibited the high piezoelectric constant (d33=134 pC/cm2). The present research offers a route for designing grain-oriented ferroelectric ceramics. 

Grain-orientation-engineered multilayer NaSr2Nb5O15 ferroelectric ceramics via templated grain growth

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Papers

Structural features modeling of substituted hydroxyapatite nanopowders as bone fillers via machine learning

Controllable preparation of Na0.4K0.1Bi0.5TiO3–CaZrO3–NaNbO3 nanoceramics with excellent temperature-stable energy storage performance by combining sol-gel synthesis and two-step sintering

Grain-orientation-engineered multilayer NaSr2Nb5O15 ferroelectric ceramics via templated grain growth