Frontiers in Materials 

About: Frontiers in Materials is an academic journal published by Frontiers Media. The journal publishes majorly in the area(s): Materials science & Composite material. It has an ISSN identifier of 2296-8016. It is also open access. Over the lifetime, 2734 publications have been published receiving 23463 citations.

Natural Fibers as Sustainable and Renewable Resource for Development of Eco-friendly Composites: A Comprehensive Review

Abstract: The increase in awareness of the damage caused by synthetic materials on the environment has led to the development of eco-friendly materials. The researchers have shown a lot of interest in developing such materials which can replace the synthetic materials. As a result, there is an increase in demand for commercial use of the natural fiber-based composites in recent years for various industrial sectors. Natural fibers are sustainable materials which are easily available in nature and have advantages like low-cost, lightweight, renewability, biodegradability and high specific properties. The sustainability of the natural fiber-based composite materials has led to upsurge its applications in various manufacturing sectors. In this paper, we have reviewed the different sources of natural fibers, their properties, modification of natural fibers, the effect of treatments on natural fibers, etc. We also summarize the major applications of natural fibers and their effective use as reinforcement for polymer composite materials.

Encapsulating Mo-Doped TiO2 Anatase in N-Doped Amorphous Carbon With Excellent Lithium Storage Performances

Abstract: For improving the capability, cycling stability and rate capacity of anatase TiO2-based electrode, Mo-doped TiO2 anatase encapsulated in nitrogen-doped amorphous carbon (denoted for Mo-TiO2@NC) were synthesized through a facile hydrothermal method and followed by coating with polyaniline (PANI) and heating treatment. When tested as anode for lithium ion batteries, the Mo-TiO2@NC electrode showed initial discharge and charge capacities of 850.7 and 548.3 mAh g-1 at a current density of 85 mA g-1, respectively, with a remarkable discharge capacity maintained at 449.2 mAh g-1 after 100 cycles. Even at high current density of 850 mA g-1, a reversible capacity of 154 mAh g-1 after 200 cycles is obtained, displaying good rate capacity and long-term cycling stability. The outstanding electrochemical performance of Mo-TiO2@NC could be attributed to the synergistic effect of aliovalent ions doping and carbon coating.

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Abstract: Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

2D/2D Graphitic Carbon Nitride (g-C3N4) Heterojunction Nanocomposites for Photocatalysis: Why Does Face-to-Face Interface Matter?

Abstract: In recent years, two-dimensional (2D) graphitic carbon nitride (g-C3N4) has elicited interdisciplinary research fascination among the scientific communities due to its attractive properties such as appropriate band structures, visible-light absorption, and high chemical and thermal stability. At present, research aiming at engineering 2D g-C3N4 photocatalysts at an atomic and molecular level in conquering the global energy demand and environmental pollution has been thriving. In this review, the cutting-edge research progress on the 2D/2D g-C3N4-based hybrid nanoarchitectures will be systematically highlighted with a specific emphasis on a multitude of photocatalytic applications, not only in waste degradation for pollution alleviation, but also in renewable energy production (e.g. water splitting and carbon dioxide (CO2) reduction). By reviewing the substantial developments on this hot research platform, it is envisioned that the review will shed light and pave a new prospect for constructing high photocatalytic performance of 2D/2D g-C3N4-based system, which could also be extended to other related energy fields, namely solar cells, supercapacitors and electrocatalysis.

Microbially Induced Calcium Carbonate Precipitation (MICP) and Its Potential in Bioconcrete: Microbiological and Molecular Concepts

Abstract: In this review, microbiological and molecular concepts of Microbially induced Calcium Carbonate Precipitation (MICP) and their role in bioconcrete are discussed. MICP is a widespread biochemical process in soils, caves, freshwater, marine sediments and hypersaline habitats. MICP is an outcome of metabolic interactions between diverse microbial communities with organic and/or inorganic compounds present in environment. Some of the major metabolic processes involved in MICP at different levels are urea hydrolysis, denitrification, dissimilatory sulfate reduction and photosynthesis. Currently, MICP directed by urea hydrolysis, denitrification and dissimilatory sulfate reduction has been reported to aid in development of bioconcrete and demonstrated improvement in mechanical and structural properties of concrete. Bioconcrete is a promising sustainable technology in reducing the negative environmental impacts due to CO2 emission from construction sector and as well as in terms of economic benefits by way of promoting self-healing process of the concrete structures. Among the metabolic processes mentioned above, urea hydrolysis is the most applied in concrete repair mechanisms. MICP by urea hydrolysis is induced by a series of reactions driven by urease (Ur) and carbonic anhydrase (CA). Catalytic activity of these two enzymes depends on diverse parameters, which are currently being studied under laboratory conditions to understand the biochemical mechanisms involved and their regulation in microorganisms. It is clearly evident that microbiological and molecular components are essential to improve the process and performance of bioconcrete.