Dear readers,
Summer holidays are over, and I hope everyone is now rested enough to dive into the 2026 September issue of the EMMR journal. This issue questions the critical role played by the composition and the processing of materials in a context where engineers look for more sustainability and materials that are more environmentally friendly. Actually, in order to reduce pollution due to material waste, the design of materials must integrate the recycling potential issue sufficiently in advance. This requires optimizing both the material components and the manufacturing process when designing a new product. In a technological world under permanent mutation, materials must address very specific needs related to demanding applications. Engineers and researchers must develop materials that are sustainable, but which provide adequate physical and mechanical properties. The goal of our journal is to point out all these strategies that are conducted toward designing more optimized material solutions. Papers presented in this issue describe, in particular, how additives can improve the properties of environmentally friendly materials and how manufacturing processes can be improved to reduce energy consumption.
Bilgiç-Tüzemen et al.1 present a strategy for increasing the mechanical properties of jute fiber/epoxy composite materials by adding graphene nanoparticles (GNP). The hybridization of natural fibers with nanofillers, such as graphene, allows for the tailoring of individual fiber and filler properties, resulting in enhanced mechanical performance of the composites. The electrochemical exfoliation process used allows to obtain graphene nanofillers with limited chemical waste and faster production. The mechanical tests performed on the obtained composites showed an increase of 5% in strength and of 75% in elastic modulus. Moreover, the water absorption induced by the jute fibers in the epoxy resin can be reduced by nearly 15% with the GNPs.
Producing materials from natural components is also presented by Thomas et al.2 It studies the adsorption potential of a novel fertilizer based on magnesium-impregnated cow dung biochar encapsulated in a chitosan biopolymer matrix (CH@Mg/CD). The purpose of the composite is to adsorb and filter phosphorus and ammonium in water. Batch adsorption experiments were conducted to evaluate the effects of various parameters on the adsorption efficiency. The CH@Mg/CD composite demonstrated high adsorption capacities for phosphate (36.02 mg/g) and ammonium (23.88 mg/g), with removal efficiencies exceeding 90% under optimized conditions of pH, dosage, and contact time.
Physical and mechanical property improvements of sustainable construction materials is studied in the next two papers. Both papers investigate how to replace traditional cement with low-carbon cementitious solutions. The paper by Jin et al.3 focuses in particular on magnesium oxysulfate (MOS) cement, which has been largely studied in terms of engineering characteristics, but less analyzed regarding its frost resistance. The intention of this paper was to inquire into the technical feasibility of enhancing the freezing–thawing (F–T) resistance of MOS cement by using granite powders (GPs). Results show that GPs can enhance the frost resistance of MOS cement by reducing the total porosity and refining pores. The ultimate number of F–T cycles can be increased by 50% when GPs are added to the mixture.
The construction materials studied by Naresh et al.4 are investigated in order to evaluate the influence of binder content and solution-to-binder ratio on mechanical and durability properties of geopolymer concrete. This concrete has emerged as a promising cement-free binder system that utilizes industrial by-products – ground granulated blast furnace slag, fly ash, and micro silica (MS) – as binder material, activated solely with a neutral-grade water-glass solution. The optimized mix design demonstrated high mechanical performance. The incorporation of up to 15% MS significantly enhanced compressive, tensile, and flexural strength. Durability assessments confirmed reduced porosity and extended corrosion resistance. The exclusive use of neutral-grade water glass provided a safer and more feasible pathway for low-carbon concrete production.
The effect of additives is also studied by Rouabah et al.,5 but this time associated with an elastomeric material. The study investigates the potential of magneto-rheological elastomers (MREs). These materials are composed of a soft elastomeric matrix (RTV141) and ferromagnetic fillers (carbonyl iron powder). They can be considered smart materials, whose properties are tunable in real time in order to adapt to evolving needs. They avoid combining several complicated technologies, each dedicated to one need. This presents a sustainable strategy to produce low-weight and cost-effective systems, which require less energy for manufacturing. The objective of this study is to conduct an in-depth analysis of the influence of strain amplitude, magnetic field intensity, excitation frequency, and temperature on the dynamic viscoelastic properties of MRE samples. The obtained results show that the MRE exhibits a combination of strong magnetic responsiveness, dynamic mechanical adaptability, and thermal stability, making it a promising candidate for adaptive systems in vibration control, damping, and smart isolation applications.
Regarding processes that help save manufacturing energy and material waste, laser additive manufacturing (LAM) has become one of the most promising technologies over the last decade. Anand Babu et al.6 present a global review on the potential and limits of additive manufacturing. They show in particular how sustainability can be achieved throughout the entire product life cycle. Based on examples taken from the industrial and medical component world, it is demonstrated how reverse engineering can first be used to analyze and optimize existing products and designs and then how LAM can be used to produce these optimized designs with reduced material waste, costs, energy consumption, and carbon dioxide emissions.
LAM presents, however, limits in some cases, as described by Jia et al.7 The study discusses the brittleness of specific Ni–Mn-based alloys, which makes the machining of these alloys difficult. These materials are, however, very useful in the industry due to their unique shape memory properties, which make them candidates for solid-state refrigeration applications, thus reducing the harmful effects of Freon-based refrigeration substances on the environment. Powder-based alloy parts are easier to obtain with LAM, but require additive substances in order to make them more processable. In this work, the effect of rare earth Y element addition to the alloy was assessed. The vacuum induction melting gas atomization technique was used to prepare Ni–Co–Mn–Al–Y spherical powder. The powder is dominated by cellular crystals, supplemented by dendrites. Results show that the Ni–Co–Mn–Al–Y alloy prepared by laser directed energy deposition has good printability. Moreover, the heat treatment tests performed on the obtained alloy samples demonstrate significant potential for the alloy to be used as a solid-state refrigeration material.
To optimize the LAM process for specific applications, machine learning can be combined with statistical methods in order to predict the mechanical behavior of LAM-obtained parts and optimize the post-processing strategies for fabricated components. Faniband et al.8 investigate in particular how to optimize the laser powder bed fusion (LPBF) process applied to the fabrication of complex duplex stainless steel (DSS) components. The purpose of the approach is to integrate metallurgical understanding, statistical discrimination, and machine learning (ML)-based prediction within a single cohesive framework tailored to DSS. S2507 DSS specimens were first manufactured with the LPBF process and characterized for mechanical properties as well as microstructural composition. Multivariate statistical evaluations were then performed, which showed that various thermal processing strategies induced significant mechanical and microstructural differentiation. ML models demonstrated strong predictive capabilities for estimating mechanical properties of LPBF-fabricated DSS.
Another way to optimize a process and predict the outcomes more precisely is to use neural network modeling (BPNN). This is presented by Azadi Moghaddam et al.,9 where the authors apply the methodology to optimize a gas tungsten arc welding process. As was the case for the LAM process, the optimization allows limiting the waste of energy required to fabricate the parts. In this case, particle swarm optimization and simulated annealing were applied to optimize the BPNN-driven model. Welding speed (S), current (C), and flux combination (F) are considered as input variables, and depth of penetration, weld bead width, and aspect ratio as output variables. The study employs nanoscale particles and systematically analyzes their combined effects on both penetration and bead geometry, leading to a more comprehensive understanding of the synergistic behavior of the fluxes.
Modeling in general can help choose the right materials combination for a specific need. Gui et al.10 explore a fluid dynamic simulation tool to simulate fire propagation among materials of various kinds positioned in a defined environment. Results show that the tool can, for example, help design the advertising board positioning in an airport terminal building to limit fire propagation.
Based on the description of the papers I presented above, I hope you will enjoy the content of this issue, and you will find the scientific and technical information that you need to make your research go forward.
