Hybrid functional materials, constituting both inorganic and organic components, are considered potential platforms for applications in extremely diverse fields such as optics, micro-electronics, transportation, health, energy, energy storage, diagnosis, housing, environment and the highly relevant area is Internet of Things (IoT). Material properties of hybrid materials can be tuned by modification of the composition on the molecular scale to produce smart materials. Cross-cutting approaches, to synergistically couple molecular engineering and processing allows to tailor complex hybrid systems of various shapes with perfect control over size, composition, functionality, and morphology. The detailed description and discussion of variety of hybrid functional organic-inorganic materials and their contribution in the designing of specific modern technologies is the prime focus of this review.
T. Ambaye, M. Vaccari, A. Bonilla-Petriciolet
et al.
Due to increasing anthropogenic activities, especially industry and transport, the fossil fuel demand and consumption have increased proportionally, causing serious environmental issues. This attracted researchers and scientists to develop new alternative energy sources. Therefore, this review covers the biofuel production potential and challenges related to various feedstocks and advances in process technologies. It has been concluded that the biofuels such as biodiesel, ethanol, bio-oil, syngas, Fischer-Tropsch H2, and methane produced from crop plant residues, micro- and macroalgae and other biomass wastes using thermo-bio-chemical processes are an eco-friendly route for an energy source. Biofuels production and their uses in industries and transportation considerably minimize fossil fuel dependence. Literature analysis showed that biofuels generated from energy crops and microalgae could be the most efficient and attractive process. Recent progress in the field of biofuels using genetic engineering has larger perspectives in commercial-scale production. However, its large-scale production is still challenging; hence, to resolve this problem, it is essential to convert biomass in biofuels by developing novel technology to increase biofuel production to fulfil the current and future energy demand.
Abstract Powder bed fusion process is one of the basic technique associated with additive manufacturing. It follows the basic principle of manufacturing the product layer by layer and their fusion. A heat source focuses its heat over a powder base material and heats the selected cross section area. Sources like laser beam, electron beam and infrared beam are used as heating tool. The process of heating allows the powder to take the shape of the intended object. Powder bed fusion process is compatible to every engineering material such as metals, ceramics polymers, composites etc. this technique is widely used in many industrial sectors such as aerospace, energy sector, transportation etc. A comprehensive overview on powder bed fusion process is presented in this review paper. Other popular techniques like selective laser melting (SLM), selective laser sintering (SLS), and electron beam melting (EBM) are also reviewed.
The 2050 carbon ‐ neutral vision spawns a novel energy structure revolution, and the construction of the future energy structure is based on equipment innovation. Insulating material, as the core of electrical power equipment and electrified transportation asset, faces unprecedented challenges and opportunities. The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed. The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated. Insulating materials, including intelligent insulating material, high thermal conductivity insulating material, high energy storage density insulating material, extreme environment resistant insulating material, and environmental ‐ friendly insulating material, are cat-egorised with their scientific issues, opportunities and challenges under the goal of carbon neutrality being discussed. In the context of carbon neutrality, not only improves the understanding of the insulation problems from a macro level, that is, electrical power equipment and electrified transportation asset, but also offers opportunities, remaining issues and challenges from the insulating material level. It is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules. The authors also hope that this paper can be helpful in future development and research of novel insulating materials, which promote the realisation of the carbon ‐ neutral vision.
Abstract The recent electronic appliances and hybrid vehicles need a high energy density supercapacitor that can deliver a burst and a quick power supply. The high energy density supercapacitor can be obtained by designing proper electrode materials along with appropriate electrolytes. This review begins with different mechanisms of energy storage, giving a brief idea regarding how to design and develop different materials to achieve proper electrodes in the pursuit of high-energy density supercapacitor without compromising its stability. This review later focuses on the engineering of different electrode materials like conducting polymer, metal oxides, chalcogenides, carbides, nitrides, and MXenes. Lastly, the hybrid electrodes made up from composites between graphene and other novel materials were investigated. The hybrid electrodes have high chemical stability, long cycle life, good electronic properties, and efficient ionic transportation at the electrode-electrolyte interface, showing great potential for commercial usage.
Abstract Energy demand makes a noteworthy concern in these days. In engineering fields, heat exchangers were utilized for cooling purpose and to improve the efficiency, tapes with various configurations can be installed. Utilizing multi helical tape (MHT) is main purpose of current context. In current turbulent simulation, transportation of nanomaterial within a tube has been scrutinized. To augment flow blockage, helical tape (HT) was employed with various width ratio (BR). Multiple tapes make the flow more disturber and increase the resistance. Widths of MHT and inlet velocity were supposed as main variables and outputs were illustrated in view of contours and bar charts. Due to increase of velocity gradient, Sgen,f (frictional entropy) increases by about 58.9% with augment of width of tapes. The increased Sgen,f with augment of Reynolds number (Re) is attributed to greater secondary flow with augment of pumping power. Reduction of temperature of wall with rise of width of tapes results in higher Nusselt number. Moreover, Nusselt number (Nu) for maximum inlet velocity is around 2.3 times greater than that of minimum inlet velocity. Sgen,th (thermal irreversibility) reduces about 89.95% with augment of pumping power at BR = 0.098. Lower surface temperature causes exergy drop to decline as a result of augmenting in Re.
This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, raise open questions, and help suggest the building blocks and areas where AI could play a role in mixed autonomy. We divide the stage of autonomous vehicle (AV) deployment into four phases: the pure HVs, the HV-dominated, the AVdominated, and the pure AVs. This paper is primarily focused on the latter three phases. It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also reach out to other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.
Batteries are of paramount importance for the energy storage, consumption, and transportation in the current and future society. Recently machine learning (ML) has demonstrated success for improving lithium-ion technologies and beyond. This in-depth review aims to provide state-of-art achievements in the interdisciplinary field of ML and battery research and engineering, the battery informatics. We highlight a crucial hurdle in battery informatics, the availability of battery data, and explain the mitigation of the data scarcity challenge with a detailed review of recent achievements. This review is concluded with a perspective in this new but exciting field.
Salamat Ali, S. S. Ahmad Shah, Muhammad Sufyan Javed
et al.
The fast growth of electrochemical energy storage (EES) systems necessitates using innovative, high‐performance electrode materials. Among the various EES devices, rechargeable batteries (RBs) with potential features like high energy density and extensive lifetime are well suited to meet rapidly increasing energy demands. Layered transition metal dichalcogenides (TMDs), typical two dimensional (2D) nanomaterial, are considered auspicious materials for RBs because of their layered structures and large specific surface areas (SSA) that benefit quick ion transportation. This review summarizes and highlights recent advances in TMDs with improved performance for various RBs. Through novel engineering and functionalization used for high‐performance RBs, we briefly discuss the properties, characterizations, and electrochemistry phenomena of TMDs. We summarised that engineering with multiple techniques, like nanocomposites used for TMDs receives special attention. In conclusion, the recent issues and promising upcoming research openings for developing TMDs‐based electrodes for RBs are discussed.
Adsorption phosphorus removal is a sustainable treatment technology with dual functions of waste recycling and environmental protection. In this study, biochar and zeolite were modified by mechanical ball milling and compounded to study the adsorption effect of composite materials on phosphorus in water. The experimental materials were measured by scanning electron microscopy, Fourier transform infrared spectroscopy and specific surface area and pore size analyzer to reveal the phosphorus removal mechanism of the composite material. The results showed that the best preparation conditions for phosphorus adsorption efficiency of modified zeolite-peanut shell biochar and modified zeolite-corn straw biochar were ball milled at 600r/min for 4 h. When the initial concentration of phosphorus was 40 mg/L, the removal rate of phosphorus by modified zeolite-corn straw biochar was 76.74 %, and the removal rate of phosphorus by modified zeolite-peanut shell biochar was 77.63 %. In the environment of low concentration phosphorus solution, when the input amount of the two composites was 0.15 g, the removal rate of phosphorus reached 84.72 %. It can be seen that the modified zeolite-biochar composite adsorption material has good adsorption performance for phosphorus in water, which provides a broad application prospect for the study of phosphorus adsorption materials.
Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R<sup>2</sup> < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R<sup>2</sup> = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation.
Rock masses have complex hierarchical structures, and the deformation of rock masses under both static and dynamic conditions is primarily concentrated at weak structural layers, which provides the possibility for the whole translation and rotation of rock blocks, and may induce a new type of nonlinear-alternating displacement waves that are completely different from traditional seismic waves, namely the pendulum-type waves (including longitudinal and transverse pendulum-type waves) and rotational waves. Pendulum-type waves have the characteristics of low frequency, low velocity, large amplitude and high energy, which can cause strong compression and anomalously low friction effect in weak layers of rock masses, and may lead to overall instability and lateral sliding of the roadway structure, thereby inducing the rockburst disasters. At present, the study of pendulum-type waves has obtained many achievements and is gradually being applied in engineering, but there are still many problems to be solved. Therefore, it is necessary to systematically review and summarize the pendulum-type wave phenomenon in blocky rock masses. Firstly, a systematic summary of domestic and foreign research achievements related to the pendulum-type waves is conducted, and the discovery, verification, on-site tests, laboratory tests, theoretical modeling and application of pendulum-type waves are briefly described. Secondly, the research achievements of the author's team in the field of pendulum-type waves in recent years are briefly introduced. Through theoretical analysis and experimental research, the propagation laws and typical characteristics of pendulum-type waves in 1D and 2D blocky rock masses have been thoroughly studied, and the influence of hierarchical structures on pendulum-type wave propagation in blocky rock masses is determined. Furthermore, the application of pendulum-type wave theory in anti-impact support of roadway is analyzed, and the mechanism of low-frequency and low-velocity characteristics, rock rotation and the disaster induced by pendulum-type waves are revealed. Finally, the future research focus and development trend of pendulum-type waves in blocky rock masses are prospected, which can provide reference for related researchers.
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.
This paper presents a method to design a path-tracking controller with an adaptive preview distance scheme for autonomous vehicles. Generally, the performance of a path-tracking controller depends on tire–road friction and is severely deteriorated on low-friction roads. To cope with the problem, it is necessary to design a path-tracking controller that is robust against variations in tire–road friction. In this paper, a preview function is introduced into the state-space model built for better path-tracking performance. With the preview function, an adaptive preview distance scheme is proposed to adaptively adjust the preview distance according to the variations in tire–road friction. Front-wheel steering (FWS) and four-wheel steering (4WS) are adopted as actuators for path tracking. With the state-space model, a linear quadratic regulator (LQR) is adopted as a controller design methodology. In the adaptive preview distance scheme, the best preview distance is obtained from simulation for several tire–road friction conditions. Curve fitting with an exponential function is applied to those preview distances with respect to the tire–road friction. To verify the performance of the adaptive preview distance scheme under variations in tire–road friction, a simulation is conducted on vehicle simulation software. From the simulation results, it was shown that the path-tracking controller with an adaptive preview distance scheme presented in this paper was effective for path tracking against variations in tire–road friction in the peak’s center offset, and the settling delays were reduced by 60% and 23%, respectively.