Transportation Applications Research Articles

Tuning of optical, thermodynamic, and thermoelectric properties of Cs2CuBiX6 (X = Cl, Br, I) halide perovskites for solar cells and energy harvesting applications

The double perovskites are outstanding materials for solar cells and transport applications to clean harvest energy. Therefore, the Cs2CuBiX6 (X = Cl, Br, I) are discussed comprehensively for energy harvesting by modified Becke and Johnson (mBJ) potential. The studied DPs fit the structural, mechanical, and dynamic stability scale by tolerance factor, Born–Huang criteria, and phonon dispersion band structures. The band gaps (1.20, 1.0, 0.70) eV for (Cl, Br, I) based DPs ensure the Cs2CuBiCl6 has an absorption band in the visible region while Cs2CuBiBr6 and Cs2CuBiI6 has an absorption band in the infrared region. Heavy elements’ spin–orbit coupling effect (Cs, Bi) reduces the band gap to 0.08 eV. Thermoelectric behavior regarding the merit scale against dopant carriers and temperature has been elaborated. The ultralow lattice thermal conductivity, large Debye temperature, hardness, and melting temperature increase their implication for thermoelectric and other thermodynamic applications. The variation in band gap makes them important for diverse optoelectric and thermoelectric applications. The Cs2CuBiCl6 with a band gap of 1.20 eV is suitable for solar cells, while Cs2CuBiBr6 and Cs2CuBiI6 with band gaps of 1.0 eV and 0.70 eV are significant for thermoelectric generators.

Modeling and dynamic performance of distributed force in high-temperature superconducting pinning magnetic levitation

High-temperature superconducting (HTS) pinning magnetic levitation (maglev) systems are highly promising for future applications in rail transportation. Through the interaction between onboard HTS bulks and the permanent magnet guideway (PMG), this system generates levitation and guidance forces, known as the HTS-PMG relationship. Under practical conditions, this interaction manifests itself as a distributed force. However, past models have only considered concentrated forces. This study aims to design experiments to investigate the HTS-PMG relationship and propose a more realistic discretized lateral-vertical coupling HTS-PMG relationship to accurately reflect torques in dynamic simulations. Based on this, a dynamic model is established to analyze the Sperling index and the five-degree-of-freedom dynamic response of the HTS pinning maglev vehicle system under different HTS-PMG relationship models. The results indicate that the proposed discretized lateral-vertical coupling HTS-PMG relationship accurately characterizes the torque generated by the HTS levitator, influencing lateral, roll, pitch, and yaw motions to some extent. Compared with the concentrated force model, the discrete force model exhibits superior dynamic performance, particularly in terms of lateral dynamics and the lateral Sperling index. This study provides valuable insights into the field of HTS maglev, particularly regarding investigating HTS-PMG relationships and their impact on HTS maglev system dynamic performance.

Prospects of green hydrogen production in the Philippines from solar photovoltaic and wind resources: A techno-economic analysis for the present and 2030

As global temperatures continue to rise, reducing greenhouse gas emissions is more important than ever – demanding an urgent transition to renewable energy systems. In this study, the potential for green hydrogen production from solar and wind sources in the Philippines is explored. Renewable energy systems can yield higher decarbonization by producing green hydrogen, potentially an ‘energy vector’ for industrial, power, and transportation applications, thereby representing a promising solution for the nation's energy transition. This study explores the competitiveness of green hydrogen production in the Philippines through a comprehensive techno-economic analysis to predict the levelized cost of hydrogen (LCOH) derived from solar- and wind-powered electrolysis. The 2030 projections explore three scenarios, each representing varying research and development intensities resulting in different electrolyzer costs. The resulting LCOH values underscore the need for economic policy interventions to slash hydrogen costs to a competitive level. The present and near-future costs and attributes of electrolysis technologies still fall short compared to fossil fuel-based pathways. Furthermore, sensitivity and uncertainty analyses are conducted to investigate subsidy and policy requirements, and random changes to costs. The findings of this work provide a comprehensive understanding of the potential fluctuations and challenges in the economic landscape in the Philippines.

Formulation challenges associated with microbial biofertilizers in sustainable agriculture and paths forward

AbstractSustainable increase in agriculture productivity is confronted by over‐reliance and over‐use of synthetic chemical fertilizers. With a market projection of $5.02 billion by 2030, biofertilizers are gaining momentum as a supplement and, in some cases, as an alternative to chemical fertilizers. Biofertilizers can improve the nutritional supply to the plant and simultaneously can improve soil health, reduce greenhouse emissions, and hence directly contribute towards environmental sustainability. Plant growth‐promoting microbes (PGPMs) are particularly receiving significant attention as biofertilizers. They are widely known for their ability to improve plant growth via increasing nutrient availability and use efficiency. However, except for a few successful cases, the commercialization of PGPM‐based inoculants is still limited, mainly due to lack of field efficacy and consistency. Lack of effective formulation technologies that keep microbial inoculants viable during storage, transport and field application is considered one of the key factors that drive inconsistent efficacy of microbial biofertilizers. In this review, we identify current challenges associated with the application and formulation of microbial inoculants. We propose future paths, including advancement in formulation technologies that are potentially efficient, eco‐friendly and cost‐effective. We argue that to enhance the global adoption of biofertilizers, new innovations based on transdisciplinary approaches are indispensable. The emerging framework should encompass a robust quality control system at all stages. Additionally, the active partnership between the academic and industry stakeholders will pave the way for enhanced global adoption of microbial fertilizers.

Chiral silicene nanoribbons under transverse and vertical electric fields: Role of edge states

In this paper, we study the edge effects on the electronic transmission of chiral silicene nanoribbons (CSiNRs) using the tight binding method and Green’s function approach. The investigation covers SiNRs of different chiralities and widths under the influence of transverse and vertical electric fields. The results show significant variations in the electronic properties of SiNRs based on these parameters. The study further explores the role of edge states in the electronic properties of CSiNRs, a topic of recent interest due to potential applications for efficient transport of electrical or optical signals. The results contribute to the understanding of the anisotropic nature of electronic transport in silicene ribbons and open new avenues for the exploration of silicene-based electronic and optoelectronic devices.

Evaluation of Heating Pad using Electrical Heating Element for Flexitank Transportation Application

Liquid transportation is accomplished in various ways, including the use of tankers and containers. Recently, most liquid shipments have utilized Flexitank as liquid containers, where steam heating is employed to melt frozen cargo liquids. However, due to limitations and disadvantages associated with steam-type heating pads, particularly in terms of the time taken to melt the liquid, researchers are exploring alternative methods for heating liquid within the Flexitank. This project aims to investigate the possibility of implementing electrical heating pads as an option for liquid heating in liquid transportation. Various types of electrical heating pads have been studied to explore their characteristics and operational capabilities. Experimental testing has been conducted on several electrical heating pads to compare their characteristics, especially in terms of the desired temperature requirement for specific liquid temperatures, as well as current consumption for each heating pad. This study will assist in determining the best option for implementing electrical heating pads in Flexitank. The results show a promising future for electrical heating pads in Flexitank, but further study is required to examine various heating pad arrangements in actual implementation.

High entropy alloys for hydrogen storage applications: A machine learning-based approach

Hydrogen is a clean energy carrier and has potential applications in energy storage, power generation, and transportation. This study explores the efficient and safe storage of hydrogen, particularly through solid-state methods using high entropy alloys (HEAs). HEAs have garnered attention for their versatility in tailoring properties for hydrogen storage. The integration of Machine Learning (ML) in designing HEAs offers an expedited approach, analyzing datasets and predicting material properties to enhance storage capacity, kinetics, and stability. Despite significant progress, the study acknowledges certain research limitations, particularly its relatively narrow focus on applying ML to HEAs for hydrogen storage. One of the biggest challenges with HEAs is their complexity, which.necessitates larger datasets to develop accurate predictive models. Collecting and analyzing existing HEA data for hydrogen storage using ML techniques is the main objective. Using algorithms like support vector regression (SVR), K-nearest (KNN), and random forest (RF), hydrogen-to-metal ratio (H/M) and valence electron configuration (VEC) are accurately predicted. The study proposes alloys with potential for HEA formation, identifying 741 with quaternary and 631 with quinary HEAs. These compositions are newly proposed and do not yet exist. Out of these, 774 HEAs are identified as potential candidates for hydrogen storage applications. Applying ML techniques, the selection process is more efficient, reducing the dependency on time-consuming experiments and making it easier to discover promising candidates.

Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

With the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.

Hydrogen production from ammonia decomposition in electric field over La-based materials

Hydrogen is rapidly emerging as a pivotal clean energy carrier for both stationary and transport applications. However, the storage of hydrogen as compressed gas at high pressures poses significant challenges in terms of cost, transportation, and safety. Chemical storage, particularly using compounds like ammonia, has emerged as a viable alternative due to its high hydrogen content and narrow flammable range. This study investigates the catalytic decomposition of ammonia under an electric field using Ni based La2O3 catalysts. The catalytic performance is evaluated in terms of ammonia conversion and energy efficiency. High catalytic activity is obtained in the presence of 15 wt%NiO/La2O3, the energy efficiency is 0.29 mmol/kJ. This is attributed to the semiconducting properties of NiO. Catalyst characterization techniques, including X-ray diffraction, transmission electron microscopy, and photoelectron spectroscopy, provide insights into catalyst morphology, chemical composition before and after reaction. It is shown that hydroxylation of La2O3 proceeds during ammonia decomposition and a reaction mechanism is provided.

Toward Linux-based safety-critical systems—Execution time variability analysis of Linux system calls

Modern transportation and industrial domain safety-critical applications, such as autonomous vehicles and collaborative robots, exhibit a combination of escalating software complexity and the need to integrate diverse software stacks and machine learning algorithms, consequently demanding complex high-performance hardware. Linux’s extensive platform support and library ecosystem make it a valuable general-purpose operating system for developing complex software systems. However, because the Linux kernel has not been designed to comply with safety standards, it has a high execution path variability and does not provide execution time guarantees. In this context, several research initiatives have studied the usage of Linux for developing complex safety-related systems, focusing on topics that include its development process, isolation architectures, or test coverage estimation. Nonetheless, execution-time analysis and providing temporal guarantees is still a challenge. This work extends the novel statistical analysis of Linux system call execution paths with the analysis of execution-time variability and proposes a method for estimating the worst-case execution time, forming a sound approach for an in-depth analysis of the Linux kernel execution paths and execution times for safety-related systems. The proposed method is applied to a representative use case that implements an Autonomous Emergency Brake application in an NVIDIA Jetson Nano board connected to the CARLA autonomous driving simulator.

Evaluation of TOPAS MC tool performance in optical photon transport and radioluminescence-based dosimetry

Radiation therapy plays a pivotal role in modern cancer treatment, demanding precise and accurate dose delivery to tumor sites while minimizing harm to surrounding healthy tissues. Monte Carlo simulations have emerged as indispensable tools for achieving this precision, offering detailed insights into radiation transport and interaction at the subatomic level. As the use of scintillation and luminescence dosimetry becomes increasingly prevalent in radiation therapy, there arises a need for validated Monte Carlo tools tailored to optical photon transport applications. In this paper, an evaluation process of the TOPAS (TOol for PArticle Simulation) Monte Carlo tool for Cerenkov light generation, optical photon transport and radioluminescence based dosimetry is presented. Three distinct sources of validation data are utilized: one from a published set of experimental results and two others from simulations performed with the Geant4 code. The methodology employed for evaluation includes the selection of benchmark experiments, making use of opt3 and opt4 Geant4 physics models and simulation setup, with observed slight discrepancies within the calculation uncertainties. Additionally, the complexities and challenges associated with modeling optical photons generation through luminescence or Cerenkov radiation and their transport are discussed. The results of our evaluation suggests that TOPAS can be used to reliably predict Cerenkov generation, luminescence phenomenon and the behavior of optical photons in common dosimetry scenarios.

Pola Penggunaan Aplikasi Transportasi Online di Kalangan Asisten Rumah Tangga

This study aims to reveal and map patterns or models of information search for household assistants as consumers of users of online transportation services. Using a case study approach is expected to reveal consumer behavior patterns and information search models relating to new economic products that can be developed for new professions in Indonesia.The results showed there were differences in ART in the search for information, for personal interests such as work leave, going with friends to the Mall, delivering to the station, ordering food and there were some who used it to support their work. Usually they prefer cash payments, and take advantage of discount promos.In the study also found three behavioral patterns in ART, namely (1) the usual personal pattern of informants using applications for personal needs. (2) Work Support Patterns on ART that they usually use online transportation applications to support their work, and (3) Social Patterns, where use is intended to help other ART members who cannot use the application.

Performance enhancements of HTS power cables by minimizing the electric field enhancements for electric transport applications

Abstract Design innovations to manage the electric field enhancements and successful use of cryogenic epoxy EP37 as electrical insulation for high temperature superconducting (HTS) cables for electric aircraft applications are reported. Detailed finite element analysis (FEA) of the electric field distribution shows that the highest electric field is at the interface of the ground and electrical insulation layers. The FEA led to the design, fabrication, and experimental characterization of four model cables with stress cones and a direct bond of the ground and insulation layers. Enhanced partial discharge inception voltage (PDIV) was achieved in the model cables by minimizing the electric field enhancements at the ground layer interface. Using a helium gas mixture with 4 mol% H2, with higher intrinsic dielectric strength than pure helium, further enhanced the PDIV. The results warrant further studies on long HTS cables with EP37 as electrical insulation with a direct bond between the insulation and ground layers.

A Light-Powered Self-Circling Slider on an Elliptical Track with a Liquid Crystal Elastomer Fiber.

In this paper, we propose an innovative light-powered LCE-slider system that enables continuous self-circling on an elliptical track and is comprised of a light-powered LCE string, slider, and rigid elliptical track. By formulating and solving dimensionless dynamic equations, we explain static and self-circling states, emphasizing self-circling dynamics and energy balance. Quantitative analysis reveals that the self-circling frequency of LCE-slider systems is independent of the initial tangential velocity but sensitive to light intensity, contraction coefficients, elastic coefficients, the elliptical axis ratio, and damping coefficients. Notably, elliptical motion outperforms circular motion in angular velocity and frequency, indicating greater efficiency. Reliable self-circling under constant light suggests applications in periodic motion fields, especially celestial mechanics. Additionally, the system's remarkable adaptability to a wide range of curved trajectories exemplifies its flexibility and versatility, while its energy absorption and conversion capabilities position it as a highly potential candidate for applications in robotics, construction, and transportation.

Creating high, portable proton polarization with photo-excited triplet DNP

Dynamic nuclear polarization (DNP) is a powerful tool to polarize nuclear spins and enhance the intensity of their magnetic resonance signal. For DNP a sample is doped with an agent providing unpaired electron spins. Then the sample is cooled in a strong magnetic field to polarize these electron spins and a microwave field is applied to transfer this polarization to the nuclear spins. While DNP is very efficient, it has two inherent issues: the electron spins needed to polarize the nuclear spins are also the main source of polarization decay. Furthermore, polarizing the electron spins requires strong magnets and powerful cryogenics, that may obstruct further use of the polarized nuclear spins.These issues can be addressed by using the electron spin of photo-excited triplet states for DNP. After DNP the light creating the electron spins can be shut off, thus eliminating the main source of decay of the nuclear polarization. Moreover, for some well-chosen molecules the photo-excitation process creates the triplet state in a highly polarized state, so magnets and cryogenics can be significantly simplified.The present article presents the state of the art of producing a high proton polarization – up to 0.80 – with a long lifetime – typically 50 h at liquid nitrogen temperature and in a field of 0.75 T – using the photo-excited triplet state of pentacene in a naphthalene host. It describes sample preparation, experimental equipment and procedures required to obtain this result, as well the theoretical background required to maximize the polarization transfer from the triplet spins to the proton spins and to optimize the photo-excitation process. It finishes with methods for long-distance transport and final application of polarized samples.

Bending fatigue performance of three‐dimensional woven composites—A review

AbstractThree‐dimensional woven composites (3DWCs) are gradually used in marine, transportation, military, and other applications owing to their high interlayer properties, load‐bearing capacity, and good near‐net‐shape forming abilities. 3DWCs are often subjected to bending fatigue loads, which negatively affect the lives of structural parts. In this review, typical types of preform structures and their evolution characteristics are described, and the micro‐modeling methods for 3DWCs are discussed. The effect of the warp yarn paths on the bending properties and the preform structure on the bending fatigue properties of 3DWCs are summarized, and the application and current status of microstructural models in the study of bending fatigue properties are summarized. In addition, the bending fatigue mechanical properties, damage evolution process, and modes of failure in 3DWCs are discussed. The study status and development of the fatigue life models of the laminated composites and 3DWCs are summarized, and future research work is prospected. This review may be beneficial to preform structural design and contribute to the study bending fatigue properties of 3DWCs.Highlights The microstructural modeling methods for 3DWCs. Types and variation patterns of 3D woven preform structures are described. Effects of the structure on the bending performance are discussed. Bending fatigue properties of 3DWCs are summarized. The development trends of predicting the lifespans of composites.

Bridging the digital divide: Consumer engagement with transportation payment apps in emerging economies

AbstractThe digital divide remains a formidable challenge in emerging economies, highlighting disparities in technology access and utilisation. This study explores consumer engagement with mobile payment applications for transportation in Lagos, Nigeria, a critical context where these disparities manifest. The research presents consumer perceptions and behaviours using a triangulated approach encompassing ethnographic observation, semi‐structured interviews, and the ALARA model of information search. Anchored in the Engel–Kollat–Blackwell (EKB) theory of consumer behaviour, which depicts the five stages of consumer decision‐making—problem recognition, information search, evaluation of alternatives, purchase decision, and post‐purchase behaviour—this study identifies five key themes: awareness, information search, alternatives evaluation, engagement, and post‐engagement evaluation. Insights from the study reveal that cultural preferences and trust in traditional payment methods significantly influence users' willingness to adopt mobile payment apps. These insights underscore the importance of addressing socio‐cultural factors in technology adoption strategies. The findings highlight the need for an inclusive technological strategy to bridge the digital divide, emphasising the necessity of accessible information channels and user‐friendly interfaces to enhance consumer engagement. Continuous app refinement based on user feedback is crucial for optimising usability. Additionally, a nuanced understanding of socio‐cultural influences on technology adoption is essential for informing policy and business strategies. Overall, this study contributes to understanding technology adoption in emerging economies and provides actionable insights to foster inclusive digital transformations and mitigate the digital divide.

A lexicographic optimization-based approach for efficient task allocation in industrial transportation multi-robot systems

In industrial transportation applications, multi-robot systems (MRS) are assigned to perform transportation tasks until their batteries are depleted, requiring them to move to battery charging stations. This temporary unavailability of the robots during charging decreases system productivity that measures the number of transportation tasks accomplished with the available robot energy. As a result, maximizing task completion becomes crucial, especially with prioritized tasks and increased workload. This can be achieved through an energy management strategy. In this context, the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach is proposed to maximize the reward in terms of system productivity with a limited energy consumption. The existing multi-robot energy management approaches consider the energy management during the tasks execution and neglect workload scaling issue with increasing tasks, whereas the LO-MRTA approach accounts for the energy consumption in the large-scale tasks allocation process. The main idea of the proposed approach is to consider the global state of the robots in the MRS system before carrying out the tasks execution, enhancing the task allocation decisions in terms of the energy management. The evaluation scenarios show the promising performance of the LO-MRTA approach in comparison with three existing baselines in terms of the system productivity, overall energy consumption and workload scaling effects.

Facile one-step functionalised graphene oxide-phytic acid coating towards effective wax inhibition in waxy oil transportation

Wax deposition in pipelines is a global issue during the production and transportation of oil. Recently, superhydrophilic/underwater superoleophobic coatings have garnered much attention due to their wax inhibition potential based on water film theory. Herein, carbon steel (CS) substrates were made superhydrophilic by coating phytic acid (PA) functionalised graphene oxide (GO) materials prepared by very simple process. The superhydrophilic GO-PA coatings induced the formation of a hydration layer on the surface, thus inherently showing underwater superoleophobic properties in a water-contained waxy oil system. The optimised GO-PA/CS surface possessed a water contact angle of nearly 0° and an underwater oil contact angle of ∼ 160°. This coating demonstrated up to 89.5 % enhancement in oil passing speed during the oil transportation test. This enhancement is particularly significant for reducing wax deposition in laminar and transitional flow regimes. Moreover, in the cold-finger wax deposition tests, a wax inhibition efficiency of 95.8 % was achieved with the optimised GO-PA/CS surface in the laminar flow regime. Therefore, this research provides insights into a mechanically and chemically stable GO-based coating achieved via a facile one-step functionalisation, which has not been previously reported in the literature for applications in waxy oil transportation.

Programmable curvilinear self‐propelling of droplets without preset channels

AbstractCurvilinear self‐propelling of droplets has attracted great interest in the past few decades due to their irreplaceable roles in many areas. Conventional understanding is that a droplet moves only along a preset channel formed by morphology or chemical components. Achieving programmable curvilinear droplet motion independent of a preset channel remains greatly challenging. Here, we report a programmable curvilinear self‐propelling of droplets (circle, divergence, and convergence) based on the collaboration of the curvilinear wetting gradient and the Leidenfrost effect. This design achieves motion trajectory in a well‐controlled manner as well as high velocity and long distance of droplet transport independent of the preset channel. Moreover, the motion behaviors of droplets could be predicted accurately by theoretic simulation. We envision that our unique design could manifest extensive practical applications in fluidic devices, liquid transport, and heat transfer systems.