Search

Patchy interactions and heterogeneous charge distribution make nanoclay (NC) a promising biomaterial to interact with different biomolecules, polymers, and biological components. Many researchers have studied the polymer/clay nanocomposites in recent years. However, some deficiencies, such as poor impact strength, limit the application of polymer/clay nanocomposites in different fields. As a result, many attempts have been made to resolve this problem. Also, researchers have developed calcium carbonate (CaCO3) nanoparticles as biomedical materials. The nontoxic properties and biocompatibility of both CaCO3 and NC make their nanocomposites ideal for biomedical applications. In this article, a detailed review of the ternary polymer nanocomposites containing NC and CaCO3 is presented. The morphological, thermal, mechanical, and rheological characteristics, in addition to the modeling of behavior and foam properties, are studied in this article. In addition, the potential challenges for ternary nanocomposites and their biomedical applications are discussed.

Several models have been used in literature studies to describe the mechanical behavior of hydroxyapatite (HA) polymer nanocomposites. However, previous models have often overlooked the size of HA and the properties of the interphase. In this study, we further develop an equation originally proposed by Kolarik. This advanced equation predicts the tensile strength of polymer/HA nanocomposites by considering the interphase parameter (A), rod‐like HA size, HA concentration, and interphase properties, including depth and strength. We validate our method using experimental data from various examples and through parametric inspections. Both the strength and the depth of the interphase directly affect the 'A' value and, consequently, the strength of the nanocomposites. For instance, an HA radius (R) of 6 nm yields the highest 'A' value of 4.97, enhancing the nanocomposite strength by up to 250%. In contrast, 'R' value of 20 nm fails to reinforce the samples effectively. Additionally, the thickness of the interphase (t) and the concentration of HA directly handle the nanocomposite strength. The strength of the samples significantly improves by 137% and 160% with an interphase thickness of 50 nm and an HA volume fraction of 0.2, respectively. Generally, the length of HA and interphase characteristics (thickness and strength) directly control the strength of samples, but the HA radius has an inverse relationship with nanocomposite strength. Highlights: Kolarik equation is developed to predict the tensile strength of polymer HA nanocomposites.Interphase parameter, HA size, HA concentration and interphase properties are considered.HA radius of 6 nm produces the maximum enhancement of nanocomposite strength by 250%.Interphase properties and concentration of HA directly control the nanocomposite strength.All parameters reasonably influence the strength of samples confirming the developed model. [ABSTRACT FROM AUTHOR]

In the past decade, global sustainability efforts have increasingly focused on two critical paradigms: achieving net zero emissions (NZE) and advancing the circular economy (CE). This article provides a detailed examination of the challenges and breakthroughs in harmonizing these approaches, drawing from a broad range of academic discussions, technological innovations, policy developments, and practical implementations. We identify specific obstacles, such as technological limitations, policy inertia, and economic and social barriers, that hinder progress towards NZE and CE. The paper then outlines targeted solutions, including cutting-edge technologies like carbon capture and storage, policy frameworks that incentivize sustainable practices, and examples of successful circular economy models. Through a critical analysis of both the synergies and tensions between NZE and CE, the article highlights the necessity for crosssector collaboration, robust policy support, and ongoing innovation to overcome these challenges. Concluding with a forward-looking perspective, we emphasize strategic pathways for integrated sustainability efforts, advocating for a multifaceted approach that combines technological advancement, policy reform, and stakeholder engagement. By offering a more nuanced understanding of the interplay between theoretical goals and practical realities, this revised abstract aims to inspire action and foster a collective move towards a sustainable global future. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the integration of 3D printing technology in biomedical robotics education with undergraduate students learning about the design of robotic systems. The research investigated the effectiveness of this new teaching strategy in enhancing students’ understanding of complex engineering principles by comparing their learning experiences, and academic performance with results from traditional teaching methods. The students’ learning and performance were evaluated through a survey and grade analysis. The Mann – Whitney U test was used to analyse the data, revealing statistically significant improvements in both student satisfaction and grades. The Mann-Whitney U test results show a clear preference for the 2017 3D printing and project-based learning strategy over the 2016 traditional method. With a U-value of 0 and a critical U-value at p < 0.05 being 23, coupled with a z-score of −3.742, the data significantly indicates enhanced student outcomes with the newer teaching approach compared to conventional methods. The study also found that 3D printing technology fostered creativity, critical thinking, and enhanced spatial visualisation skills among students. However, we also identify potential challenges, including equipment maintenance, safety concerns, and the need for faculty expertise. The study concludes by providing potential strategies to overcome these challenges. This research offers valuable insights for educators and policy-makers in leveraging technology for improved learning outcomes in engineering education. [ABSTRACT FROM AUTHOR]

Herein, stacks of graphene nanosheets resulting from an incomplete dispersion of nanoparticles in polymer graphene nanocomposites are considered. The volume fraction, aspect ratio and conduction of stacks are expressed by the distance between nanosheets (s), thickness of an individual nanosheet (t), nanosheet diameter (D), thickness of the interphase zone (ti) and tunneling length (d). Moreover, the percolation onset, actual filler quantity and portion of networked nanosheets are stated by the stacks of nanosheets, interphase depth and tunneling length. Finally, an advanced model for the conductivity of a graphene-based system is presented using the mentioned terms. The influence of all properties of stacks, tunneling and interphase areas on the percolation onset, portion of percolated nanosheets and conductivity are examined. Furthermore, the tested values of conductivity are applied to confirm the predictability of the model. The larger quantity of thin sheets included in stacks produces a higher conductivity for samples. In addition, a thicker interphase and smaller tunnels can result in higher conductivity. The calculations of conductivity match the tested data at all filler amounts. [ABSTRACT FROM AUTHOR]

Several studies have been directed to find scalable, swift, accurate, and cost‐effective strategies for detecting, monitoring, and treating coronavirus disease 2019 (COVID‐19). Indeed, the lack of a fast and practical method for detecting the infected regions makes decision‐making challenging to combat the critical pandemic‐struck situations. The probable 'wrong', or rather inadequate, decisions not only have a boomerang effect on the economy but also can lead to an increase in the number of infected individuals, degree of hospitalization, and death counts. Although the current clinical methods are effective, they are costly, time‐consuming, and, more particularly, inadequate because of the virus's mutation patterns. In addition, contamination of biomedical wastes with the COVID‐19 virus is a matter of grave concern. Therefore, there is a perpetual need for novel methodologies to delineate the contaminated regions and determine whether those viruses contaminate the wastewater. Although several review papers have been recently published to discuss those concerns, there is a lack of a comprehensive survey of the detection and treatment of the COVID‐19 virus in aqueous media. Herein, we review techniques available as spreading signifiers for detecting the COVID‐19 virus in water resources and wastewater. We classify and integrate techniques into wastewater, sewage, and sludge detection and monitoring. Treatment of COVID‐19‐contaminated wastewater is discussed by classifying and ranking the methodologies nurtured from nanotechnology, including nanoparticle‐based biosensors used in the detection and nanotechnology‐based filtration systems for the removal of COVID‐19 from wastewater. We also highlight the compilation of the detection methodologies in contaminated aqueous media and provide insight into the challenges associated with treating COVID‐19‐contaminated wastewater. The article concludes that international and robust guidelines for virus/bacteria treatment in wastewater are urgently needed to protect the environment and public health, where nanotechnology plays a key role. [ABSTRACT FROM AUTHOR]

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries. [ABSTRACT FROM AUTHOR]

Highlights In this study, we present a model for predicting the Young's modulus of polymer/nanodiamond (ND) composites, taking into account the interphase properties. The Christensen‐Lo model is adapted and refined to predict the modulus of ND‐based nanocomposites by considering the properties of spherical NDs and their surrounding interphase. The outcomes of the developed model are benchmarked against experimental data, exploring the impact of various parameters, such as ND radius, ND concentration, and interphase properties (thickness and modulus) on the nanocomposite modulus. Findings suggest an inverse correlation between the size of the nanoparticles and the modulus of nanocomposite. Specifically, it is observed that nanoparticles with a minimum radius of 1 nm and a maximum concentration of 5 vol% can increase the nanocomposite modulus by 180%. Additionally, maximum thickness and modulus of the interphase can significantly improve the nanocomposite modulus. The highest interphase thickness of 10 nm and maximum interphase modulus of 100 GPa can grow the modulus of samples by 300%. The predicted influences of all parameters on the nanocomposite modulus validate the accuracy of the developed model. A model for the Young's modulus of polymer nanodiamond (ND) composites is presented. ND radius, ND concentration, and interphase thickness and modulus are considered. The outcomes of the developed model are compared to the experimental data. 5 vol% of the smallest ND (R = 1 nm) increases the nanocomposite modulus by 180%. The thickest and the toughest interphase enhance the nanocomposite modulus by 300%. [ABSTRACT FROM AUTHOR]

In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools. [ABSTRACT FROM AUTHOR]

Near‐infrared (NIR) spectroscopy is a rapid analytical method for food products. In this study, NIR spectroscopy, data pretreatment techniques and multivariate data analysis were used to predict fine particle size fraction, dispersibility and bulk density of various milk powder samples, which are believed to have a significant impact on milk powder quality. Predictive models using partial least‐squares (PLS) regression were developed using NIR spectra and milk powder physical and functional properties, and it was concluded that the PLS models predicted milk powder quality with an accuracy of 88‐90 per cent. [ABSTRACT FROM AUTHOR]

As hybrid porous structures with outstanding properties, metal–organic frameworks (MOFs) have entered into a large variety of industrial applications in recent years. As a result of their specific structure, that includes metal ions and organic linkers, MOFs have remarkable and tunable properties, such as a high specific surface area, excellent storage capacity, and surface modification possibility, making them appropriate for many industries like sensors, pharmacies, water treatment, energy storage, and ion transportation. Although the volume of experimental research on the properties and performance of MOFs has multiplied over a short period of time, exploring these structures from a theoretical perspective such as via molecular dynamics simulation (MD) requires a more in-depth focus. The ability to identify and demonstrate molecular interactions between MOFs and host materials in which they are incorporates is of prime importance in developing next generations of these hybrid structures. Therefore, in the present article, we have presented a brief overview of the different MOFs’ properties and applications from the most recent MD-based studies and have provided a perspective on the future developments of MOFs from the MD viewpoint. [ABSTRACT FROM AUTHOR]