Advancement in medicine and technology has resulted into prevention of countless deaths and increased life span. However, it is important to note that, the modern lifestyle has altered the food habits, witnessed increased life-style stresses and road accidents leading to several health complications and one of the primary victims is the bone health. More often than ever, healthcare professionals encounter cases of massive bone fracture, bone loss and generation of critical sized bone defects. Surgical interventions, through the use of bone grafting techniques are necessary in such cases. Natural bone grafts (allografts, autografts and xenografts) however, have major drawbacks in terms of delayed rehabilitation, lack of appropriate donors, infection and morbidity that shifted the focus of several investigators to the direction of synthetic bone grafts. By employing biomaterials that are based on bone tissue engineering (BTE), synthetic bone grafts provide a more biologically acceptable approach to establishing the phases of bone healing. In BTE, various materials are utilized to support and enhance bone regeneration. Biodegradable polymers like poly-(lactic acid), poly-(glycolic acid), and poly-(ϵ-caprolactone) are commonly used for their customizable mechanical properties and ability to degrade over time, allowing for natural bone growth. PEG is employed in hydrogels to promote cell adhesion and growth. Ceramics, such as hydroxyapatite and beta-tricalcium phosphate (β-TCP) mimic natural bone mineral and support bone cell attachment, with β-TCP gradually resorbing as new bone forms. Composite materials, including polymer-ceramic and polymer-glasses, combine the benefits of both polymers and ceramics/glasses to offer enhanced mechanical and biological properties. Natural biomaterials like collagen, gelatin, and chitosan provide a natural matrix for cell attachment and tissue formation, with chitosan also offering antimicrobial properties. Hybrid materials such as decellularized bone matrix retain natural bone structure and biological factors, while functionalized scaffolds incorporate growth factors or bioactive molecules to further stimulate bone healing and integration. The current review article provides the critical insights on several biomaterials that could yield to revolutionary improvements in orthopedic medical fields. The introduction section of this article focuses on the statistical information on the requirements of various bone scaffolds globally and its impact on economy. In the later section, anatomy of the human bone, defects and diseases pertaining to human bone, and limitations of natural bone scaffolds and synthetic bone scaffolds were detailed. Biopolymers, bioceramics, and biometals-based biomaterials were discussed in further depth in the sections that followed. The article then concludes with a summary addressing the current trends and the future prospects of potential bone transplants.

Magnesium alloy based bioceramic reinforced degradable composite scaffolds were fabricated by use of powder metallurgy technique followed by controlled two stage heat treatment. Carbamide was introduced to create porosity in the scaffolds. Selective alloying elements such as calcium, zinc and iron were introduced in a systematic manner to control the degradation rate, while bioactivity was introduced by incorporating micro-hydroxyapatite particulates. Microstructure of fabricated scaffolds were studied by scanning electron microscopy which established predominantly homogeneous distribution of pores. X-ray micro computed tomography results indicated gradient porosities with variation in pore dimensions and shapes throughout the microstructure, an essential feature to biomimick the structure of bone. Degradation rate was evaluated by weight loss method following periodic immersion in Hanks solution. The results suggested controlled and enhanced degradation resistance of composite scaffolds. The results from this study therefore, could demonstrate promising potential of developed composites as lightweight bone scaffolds with tunable degradability.

In recent years, electric vehicles (EVs) have grown in popularity as a viable way to reduce greenhouse gas emissions by replacing conventional vehicles. The need for EV batteries is steadily increasing. An essential and expensive part of electric transportation is the battery. The operating temperature of the lithium-ion (Li-ion) battery significantly impacts the performance of the EV battery pack. Battery packs undergo temperature fluctuations during the charging and discharging procedures due to internal heat generation, necessitating an examination of the temperature distribution of the battery pack. The geometrical spacing between cells is considered larger and identical and is kept open on two sides for free air circulation. A novel battery thermal management system (BTMS) design is required to effectively dissipate heat from the prismatic battery pack module. The electro-thermal behaviour of the prismatic Li-ion battery pack module was investigated based on the high charge/discharge rate. This study presents the development of a three-dimensional free open-source OpenFOAM computational fluid dynamics model for prismatic cell battery packs that simulates heat generation, air flow field, and temperature distribution across the width and depth of the battery pack module. The prismatic battery pack simulation results are compared with the experimental and simulation results of the cylindrical battery pack. It was also revealed that prismatic cells generate more heat on the backside, requiring battery packs to have increased cooling and space between individual cells to ensure sufficient air circulation for cooling and gas removal. The BTMS is improved by designing with increased space among the prismatic battery cells as compared with the conventional prismatic cell battery pack design.

This study deals with the fabrication of magnesium-alloy-based biodegradable porous bone scaffolds. Selected amounts of calcium, zinc and strontium were incorporated into magnesium-metal-matrix as alloying elements to tune the degradation behaviour, while bioactive fluorcanasite nano-particulates were incorporated to promote bone growth. Green compacts with urea porogen were fabricated using the powder metallurgy route, followed by sintering to achieve the desired porosity in the fabricated samples. The microstructure of the fabricated samples was examined by FE-SEM, coupled with EDS, which revealed uniform distribution of pores and alloying elements. Degradation study of the fabricated bio-nanocomposites revealed the retardation of degradation rate, owing to the incorporation of alloying elements. Hydrogen evolution and pH studies of the immersed samples were also found to be in coherence with the degradation behaviour of the samples. These results, therefore, demonstrated that the fabricated magnesium alloy-based bio-nanocomposite scaffolds have promising potential for bone tissue engineering. Graphical Abstract

Thermogravimetric analysis (TGA) is a technique used to investigate the thermal characteristics of materials by observing fluctuations in sample mass with changes in temperature. Amid the increasing worldwide focus on sustainable materials, biocomposites have become popular for their eco-friendly characteristics. Thermal stability is a crucial factor in determining the performance of biocomposites. The present research improved thermal properties by incorporating wheat straw residual filler into an epoxy resin matrix after various chemical treatments of wheat straw fibers, such as alkali (NaOH) or a combination of silane and alkali treatments. Machine learning (ML) analysis performed in WEKA 3.0 was conducted on thermal data derived from the thermogravimetric measurements of the biocomposites. This research took into account several factors, such as filler loading, single or dual chemical treatment, and temperature, to forecast the thermal-degradation behavior during combustion. Sixteen distinct regression models were used to predict the TGA curves. The K-Nearest Neighbor (KNN) classifier outperformed the other 15 models by achieving an R-squared value of 0.9999, indicating remarkable prediction skills. The strong correlation between the experimental data and the anticipated values confirmed the accuracy of the ML computations.