Carbon fibre-reinforced polymer (CFRP) composites represent promising lightweight materials for automotive powertrain systems, where increasing demands for weight reduction, energy efficiency, and emission reduction are driving the replacement of conventional metallic components. However, automotive powertrain environments expose CFRP materials to elevated temperatures, cyclic mechanical loading, chemical exposure, and tribological interactions, creating complex degradation conditions that significantly influence long-term durability and reliability. This review systematically analyzes CFRP composites for automotive powertrain applications, focusing on the relationship between operational requirements, material selection, reinforcement architecture, manufacturing technologies, and degradation mechanisms. High-performance thermoplastic systems such as CF/PEEK, CF/PPS, and CF/PEKK are critically compared with conventional thermoset composites. CF/PEEK systems demonstrate superior thermomechanical stability, maintaining significant mechanical performance at temperatures approaching 250 °C and tensile strengths of approximately 1400-1600 MPa, whereas CF/PPS composites provide a more economically efficient compromise between thermal resistance, chemical stability, manufacturability, and recyclability for medium-temperature applications. The review further analyzes dominant degradation mechanisms, including creep deformation, fatigue damage, delamination, fibre-matrix interface degradation, and tribological wear. CFRP degradation is shown to result from the interaction of multiple coupled mechanisms rather than from isolated material failure modes. Tribological wear rates typically range from 10-6 to 10-5 mm3/(N·m), while creep-fatigue interactions may reduce component lifetime by up to 40-60% under combined thermomechanical loading. Advanced design strategies, including fibre orientation optimization, laminate architecture tailoring, thickness gradation, and hybrid metal-composite structures, are evaluated together with major manufacturing technologies such as injection moulding, compression moulding, overmoulding, automated fibre placement, and additive manufacturing. The presented review establishes an integrated framework linking material systems, operating conditions, manufacturing processes, and durability requirements for automotive powertrain applications. The analysis demonstrates that no universal CFRP system exists for all powertrain components and that optimal material selection requires balancing thermal stability, fatigue resistance, tribological performance, manufacturability, recyclability, and economic constraints according to the specific operating conditions of each component category.
The growing demand for sustainable materials has intensified research into natural fiber-reinforced polymer composites as alternatives to conventional fossil-based plastics. In this study, polycarbonate (PC) composites reinforced with Spartium junceum L. (SJL) fibers were developed and systematically characterized to evaluate their potential for automotive applications. Composites containing 5-20 wt % of randomly oriented short SJL fibers were prepared and analyzed in terms of thermal, mechanical, and morphological properties. Dynamic mechanical analysis revealed increased stiffness with rising fiber content, accompanied by a slight reduction in glass transition temperature. Tensile testing showed significant improvements in strength and Young's modulus, with optimal performance observed at 10-15 wt % fiber loading, attributed to improved fiber dispersion and interfacial adhesion. At higher fiber content (20 wt %), mechanical performance declined due to fiber agglomeration and reduced stress transfer efficiency. Thermal analysis indicated good compatibility between SJL fibers and the PC matrix, although a gradual decrease in thermal stability was observed with increasing fiber content. Scanning electron microscopy confirmed generally adequate fiber-matrix interaction, particularly at intermediate fiber loadings. Swelling tests demonstrated minimal water uptake, indicating preserved hydrophobicity of the composites. Overall, the results highlight the potential of SJL fibers as an effective, sustainable reinforcement for polycarbonate composites, offering improved mechanical performance while contributing to environmentally friendly material solutions for the automotive industry.
Contactless automotive tailgate activation relies on recognizing intentional lower-limb gestures near the rear bumper, yet these systems are developed and evaluated from sensor-specific recordings rather than from the underlying human movement, so quantitative, sensor-independent kinematic reference profiles for these gestures are lacking. This study establishes wearable inertial measurement unit (IMU)-derived reference profiles of two tailgate-activation gestures: a forward kick and a lateral wipe. Lower-body motion was recorded in 56 adult participants using a seven-sensor Xsens Awinda configuration under application-oriented conditions, yielding 6879 segmented movements. To the best of our knowledge, this is among the most extensive of such datasets, providing a sensor-independent, joint- and segment-level movement reference. Both gestures shared a common sagittal structure dominated by knee, ankle, and hip flexion/extension, with mean knee flexion/extension of 45.9∘ for kick and 42.3∘ for wipe movements. Wipe gestures differed through markedly larger non-sagittal components, with hip abduction/adduction of 17.2∘ versus 7.7∘ and ankle internal/external rotation of 16.6∘ versus 8.7∘, confirmed in every participant (p<0.001). Foot-segment kinematics showed the highest velocities, with a mean resultant foot velocity of approximately 1.8m/s. These profiles provide a quantitative biomechanical basis for benchmarking gesture-recognition sensor systems, informing detection-window and threshold selection, and enabling standardized, repeatable testing of contactless automotive HMI systems.
In this study, the cooling performances of various nanofluids were compared under the operating conditions of a real automobile radiator, based on an internal combustion engine vehicle cooling system whose experiments had been previously completed. In the analyses, the radiator inlet fluid temperature was fixed at 70 °C, air inlet velocities were set to 6, 8, and 10 m/s, and fluid flow rates were taken as 17, 19, and 21 L/min. Under these conditions, the cooling capacities were evaluated for three different working fluids whose thermophysical properties were experimentally determined: 100% pure water, water-based 0.3% ZnO nanofluid, and water-based 0.3% ZnO + CuO hybrid nanofluid. Within the scope of this study, a Computational Fluid Dynamics (CFD) model was developed based on the aforementioned experimental parameters and validated with a maximum deviation of 6%. Using the validated model, additional CFD analyses were performed for water-based 0.3% Al2O3 and TiO2 nanofluids, whose thermophysical properties were also experimentally determined, and their cooling performances were assessed. Based on the experimental and numerical results obtained, the highest cooling capacity was determined to be 20.8 kW in the 0.3% TiO2 nanofluid, representing a 69.1% increase in cooling capacity compared to pure water. These findings clearly demonstrate that the use of nanofluids significantly enhances heat transfer performance in automotive cooling systems.
Driven by the automotive industry's strategies for energy conservation, emission reduction, and lightweighting, magnesium alloy wheels have emerged as a key focus of research and industrialization efforts, owing to their high specific strength, excellent vibration-damping properties, and superior heat dissipation performance. This paper provides a systematic review of the performance advantages, material systems, forming processes, applications, and industrialization challenges of magnesium alloy automotive wheels. The core advantages of magnesium alloy wheels in terms of weight reduction, vibration damping, and thermal management are elaborated. The compositional characteristics, suitable processes, and performance differences between cast magnesium alloys (e.g., AZ91D, AM60B) and wrought magnesium alloys (e.g., AZ80, ZK61-Y) are outlined. The technical characteristics, microstructural and property evolution, and limitations of casting processes (gravity, high-pressure, low-pressure, and semi-solid casting), plastic forming processes (isothermal extrusion forging, backward extrusion forging, and spin forming), and hybrid processes are discussed. Combined with the case studies of magnesium alloy wheel applications in the automotive sector, this paper analyzes the core bottlenecks of magnesium alloy wheels in terms of corrosion resistance, production cost, and industrial consistency, and outlines future research directions. This paper aims to provide theoretical references and technical support for the design, manufacturing, and large-scale application of lightweight, high-performance magnesium alloy wheels.
A hydroxyl-thiol dual-site synergistic electrolyte based on glycerol and 2-mercaptoethanol is designed for aqueous zinc-ion batteries. The optimized electrolyte enables stable Zn‖Zn cycling and wide-temperature operation (-20 to 50 °C), offering a molecular-engineering pathway for extreme-condition aqueous zinc-ion batteries.
As wearable sensors advance toward long-term motion monitoring and operation in humid environments, performance priorities are shifting from sensitivity to sustained reliability. Hydrogels are attractive sensing materials due to their tissue-like compliance, biocompatibility, and tunable conductivity; however, their hydrated networks readily absorb water under perspiration, high humidity, and underwater conditions, leading to structural relaxation, interfacial instability, conductive pathway disruption, and signal drift. Thus, anti-swelling design should move beyond reducing swelling ratios toward coordinated regulation of water transport, internal water environment, interfacial integrity, and signal stability. This review summarizes recent advances in anti-swelling hydrogel-based wearable sensors, focusing on structural engineering strategies, including network confinement, surface hydrophobicity, core-shell architectures, and gradient structures, as well as material regulation mechanisms, including ionic/coordination crosslinking, nanoconfinement, zwitterionic hydration, and solvation-mediated anti-water exchange, highlighting their synergistic roles in long-term anti-swelling performance and environmental adaptability. Representative applications in perspiration monitoring, underwater motion sensing, rehabilitation, and intelligent interaction demonstrate the importance of anti-swelling regulation for reliable sensing in wet environments. Finally, the remaining challenges are summarized, together with future perspectives on the synergistic design of structures, materials, and interfaces, standardized evaluation systems for realistic motion environments, and scalable manufacturing. Anti-swelling hydrogel sensors are expected to evolve from low-swelling materials into environmentally adaptive sensing platforms for aqueous environments, enabling advances in underwater sports monitoring, digital health, and underwater human-machine interaction.
The intercooler is a critical heat-exchange component in an automobile's turbocharging system, essential for maintaining engine efficiency and maximizing power output. However, its performance is often compromised during the spring-to-summer season by widespread poplar catkins (PCs). In this study, the effects of physical blockage and chemical corrosion induced by poplar catkin pollution on the intercooler's performance were investigated, employing an integrated approach that combined field sampling, experimental simulation, and multi-technique characterization. The results demonstrate that poplar catkins are readily trapped by the intercooler and accumulate on the windward side, resulting in a significant increase in pressure drop. After 100 min of operation in an environment with a poplar catkin concentration of 800 μg/m3, the pressure drop rose from 83 to 268 Pa. Additionally, the hydrolysis products of poplar catkins contain corrosive substances such as acetic acid. Corrosion experiments reveal that localized perforations were observed on the fin surfaces after 3 days of exposure to acetic acid environment, and the number and area of the perforations increasing over time. Among the evaluated mitigation strategies, 20 Pores Per Inch (PPI) filter cotton exhibits the best comprehensive performance for the interception. This filter cotton exhibits a filtration efficiency of 96.9% for poplar catkins and effectively mitigates the increase in pressure drop caused by them.
Accurate junction temperature (Tj) sensing is essential for the reliability of silicon carbide (SiC) power modules in electric vehicles. Nonetheless, the physical separation and consequent thermal signal delay between sensing elements and chips pose significant challenges to precise junction temperature monitoring. To solve this issue, an embedded temperature sensing structure integrated into the designed double-sided cooling (DSC) SiC power module is proposed, which leverages 3D vertical interconnects to enhance temperature observability. The customized design of a copper spacer serves as the primary heat dissipation path and electrical connection between the upper and lower chips in the same location. A compact thermal resistance network and 3D finite-element simulations are developed to reveal the vertical thermal coupling between the spacers and the chips, enabling accurate junction temperature estimation from spacer temperature. The proposed concept is experimentally validated on a fabricated prototype using embedded K-type thermocouples and an IR camera under power cycling conditions. The measured temperature differences between the copper spacers and the junction temperature are maintained within approximately 0.5-2 °C under the tested operating range. This approach provides a potential application in real-time condition monitoring and thermal management in high-power-density electric drives.
The evaluation of automotive sound quality is of considerable significance for improving driving comfort. However, existing methodologies suffer from notable limitations, including inconsistencies in subjective evaluations and weak correlations between objective metrics and auditory perception. In response to these challenges, an automated evaluation method incorporating electroencephalogram (EEG) signals and ensemble deep learning is proposed herein. Initially, EEG data is acquired from 30 subjects during exposure to 16 automobile sounds with sporty quality. Subsequently, the LSTMS-B model is incorporating Swish activation into LSTM to mitigate gradient vanishing and enhancing Bagging through optimized majority voting, achieving 90.8% accuracy with superior performance over conventional LSTM variants; Furthermore, an innovative ResNet-based regression model is developed to establish the automobile sound-EEG feature mapping, enabling the LSTMS-B model to achieve 89.75% average F1 score in sound quality classification using brain auditory representations while reducing reliance on conventional EEG paradigms. This study develops a novel sound quality evaluation paradigm through deep-ensemble learning integration, where the proposed cross-modal feature mapping method provides a transferable AI framework for interpreting human auditory perception mechanisms.
In recent years, the field of metallic materials has undergone rapid development, driven by increasing demands for high-performance structural and functional materials in industries such as aerospace, automotive, energy, defense, and biomedical engineering [...].
The presence of organic acids in fuel ethanol can significantly increase the corrosivity of automotive fuel systems. This study evaluated the relationship between ethanol composition and corrosion of stainless steel fuel injector valves and investigated the removal of acetic acid from fuel ethanol using ion-exchange resins. Long-term immersion assays (90 days at 70 °C) were performed and revealed no visible corrosion in anhydrous ethanol, while hydrated ethanol promoted severe corrosion accompanied by acetic acid concentrations exceeding 900 mg·L-1. Commercial ethanol samples also showed corrosion when total acidity increased up to 498.6 mg·L-1. Synthetic ethanol tests confirmed that increasing acetic acid concentrations intensified corrosion and that ethanol-metal interactions can promote acid formation, reaching up to 372.6 mg·L-1 even in initially acid-free solutions. To mitigate this problem, the adsorption performance of Amberlite IRA-67 and IRA-96 resins was evaluated. IRA-67 exhibited superior performance, reaching adsorption equilibrium within 60-90 min and maximum adsorption capacities between 14 and 25 mg·g-1. The resin maintained good selectivity in the presence of competing acids and retained about 85% of its adsorption capacity after five regeneration cycles. In real fuel ethanol samples, acetic acid removal ranged from 30 to 41%, reducing concentrations below regulatory limits. Continuous fixed-bed experiments showed initial removal efficiencies above 95%, with breakthrough occurring after approximately 50 min. The results confirmed the feasibility of ion-exchange technology for fuel ethanol purification and can be used to design a filter to be used in gas stations or in an automotive fuel injection system.
Against the backdrop of electrification and supply chain resilience, the global automotive industry is in an era of great transformation. Using component supply information provided by MarkLines, this study investigated the impact on inter-firm dependencies among major global automakers from 2018 to 2024. Specifically, it analyzed changes in the community structure of the interdependency network between manufacturers, based on common suppliers for each model year and component category. The results revealed the impact of electrification: a reduction of roughly two-thirds (about 64%) in transactions for internal combustion engine (ICE) powertrains and an approximately eight-fold increase in e-powertrain transactions. Furthermore, the results of community detection also revealed a structural reorganization: while geographical clustering among manufacturers intensified for ICE components, new cross-border interdependencies formed for e-powertrain components, leading to the fragmentation of the traditionally integrated Japan-US-Europe bloc and the emergence of a China-centric ecosystem. This study provides new empirical evidence on the structural realignment of the global automotive value chain, offering important implications for management strategy and industrial policy in an era of great technological and geopolitical change.
The development of low-noise, high-sensitivity magnetic sensors is essential for applications such as automotive systems, biomedical imaging, and magnetic microscopy. However, sensor performance has long been impeded by a fundamental constraint that noise and sensitivity increase concomitantly, which imposes a performance limit. Here, we show that this limit can be overcome by engineering spin-texture dynamics. While maintaining high sensitivity, the sensor noise is demonstrated to decrease inversely with enhanced spin-texture dynamics. Leveraging this mechanism, using synthetic ferrimagnets with accelerated spin-texture dynamics, low-noise and high-sensitivity anomalous-Hall sensors are constructed. With an active sensing area of 20×20  μm^{2}, the device demonstrates a field detectability of 15.7  nT/√Hz at 1 Hz, nearly an order of magnitude improvement over existing sensors based on ferromagnetic materials. Our results establish active control of spin textures as a general pathway to ultrasensitive, low-noise magnetic sensing platforms.
Tribological systems involving rolling and sliding contacts generate coupled mechanical interactions that govern friction, wear, and surface degradation. These interactions produce multiaxial residual stresses that influence crack initiation, accelerate wear, and promote environmentally assisted damage. In corrosive environments, tribo-corrosion further intensifies material degradation through the combined action of mechanical wear and electrochemical reactions. Protective organic and metallic coatings are widely used to mitigate these effects; however, their performance depends on adhesion, stress evolution, and resistance to coupled mechanical and chemical degradation. Among the principal failure mechanisms, cathodic blistering is strongly influenced by diffusion, interfacial stresses, and tribological loading. This review therefore links cathodic blister evolution with coating degradation under combined tribological and corrosive conditions. The review critically examines the Khan-Nazir meso-mechanics Models I, II, and III, which integrate stress-assisted diffusion, residual stress development, mixed-mode fracture, and coating-substrate delamination. Recent developments have extended these models through substrate deformation, multilayer coating architectures, and electro-chemo-mechanical phase-field simulations. The models demonstrate how diffusion-induced and residual stresses interact with tribological loading to initiate and propagate interfacial defects. The analysis shows that blister evolution is primarily governed by elastic modulus mismatch and friction-induced stress fields, while stability criteria predict non-axisymmetric blister morphologies associated with buckling and delamination. Overall, this review highlights the significance of the Khan-Nazir models for understanding wear, friction, and coating durability in engineering systems. The unified framework provides valuable guidance for the design and optimisation of advanced multilayer protective coatings for marine, automotive, energy, and manufacturing applications operating under rolling/sliding contact and tribo-corrosion environments.
This study investigated the relationship between fixation-frequency-based Shannon entropy and dwell-time-based entropy across two different visual task domains: emotional evaluation of automotive exterior designs and safety-critical monitoring of nuclear power plant emergency scenarios. Although gaze entropy has been widely used to explain emotional responses, task performance, and situation awareness, the relationship between entropy measures derived from fixation counts and fixation durations remains insufficiently examined. Eye-tracking data were analyzed from two experiments with different attentional characteristics. In the emotional visual task, 10 participants evaluated three automotive design images. In the safety-critical task, 20 participants performed four nuclear power plant emergency monitoring scenarios. Shannon entropy and dwell-time entropy were calculated using fixation count and fixation duration distributions across Areas of Interest, respectively. Pearson correlation and simple regression analyses were conducted within each task domain. The results showed strong positive associations between Shannon entropy and dwell-time entropy in both domains. The emotional task showed a correlation of r = 0.844, while the safety-critical task showed a correlation of r = 0.890. These findings suggest that fixation-frequency-based and dwell-time-based entropy measures exhibit substantial overlap across different visual task contexts. However, the observed associations may partly reflect mathematical dependency between fixation frequency and cumulative dwell-time, and the findings should be interpreted as exploratory evidence rather than proof of metric interchangeability. The study highlights that gaze entropy metrics should be interpreted in relation to task-dependent attentional contexts. Higher entropy may be associated with exploratory visual attention in emotional evaluation, whereas lower entropy may be associated with focused monitoring in safety-critical tasks.
High-temperature CO2 detection in automotive exhaust remains a formidable challenge due to the harsh chemical environment and significant cross-sensitivity of solid electrolyte sensors. This study reports a robust sensing platform integrating a NASICON-based solid electrolyte sensor with a random forest (RF) machine learning (ML) architecture for high-fidelity carbon emission monitoring. To achieve superior electrochemical performance, the sensor utilizes surface-optimized catalysts and stabilizers, with NASICON doping to enhance ionic conductivity and structural integrity under thermal cycling. The hardware is coupled with an automotive-grade data acquisition system, enabling in situ signal capture directly from the engine tailpipe. To circumvent the analytical limitations of solid electrolyte sensors-specifically thermal drift and multi-gas cross-interference-a software-defined compensation strategy is proposed. Rather than relying on auxiliary gas sensors, the RF model leverages intrinsic vehicle operating parameters as surrogate multidimensional features to dynamically decouple interfering factors from the CO2 response. The sensor reliability was rigorously validated across multiple combustion platforms under standardized transient cycles, including the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the World Harmonized Transient Cycle (WHTC), and the Real Driving Emissions (RDE) protocols. The ML-enhanced sensor achieved a coefficient of determination (R2) exceeding 0.8 even in highly non-linear transient states. Crucially, the cumulative mass emission error was suppressed to less than 0.4%, demonstrating metrological-grade accuracy in total carbon quantification. By synergizing solid-state electrochemistry with intelligent data analytics, this work provides a scalable, cost-effective solution for real-time carbon footprint tracking, directly supporting global initiatives for transportation decarbonization and environmental compliance.
The highly active surface of LiNi0.8Co0.1Mn0.1O2(NCM811) cathodes frequently causes rapid surface degradation, leading to diminished cycling and rate capabilities, particularly under high voltage conditions exceeding 4.5 V. Herein, we report an approach to boost the electrochemical performance of NCM811 through integrated surface engineering. The highly conductive La0.2Sr0.8TiO3(LSTO) acts as an effective protective layer to mitigate interfacial side reactions. Moreover, this coating strategy prevents the additional impedance commonly induced by standard surface treatments. After 300 cycles at 1C, the optimized N-LSTO2 cathode material exhibits a remarkably enhanced capacity retention of 88.19%, whereas the pristine NCM811 only maintains 38.73%. Additionally, the N-LSTO2 cathode exhibits a discharge capacity of 120.3 mAh g-1 at 10C rate, which significantly outperforms the 89.6 mAh g-1 observed for the pristine cathode. The enhanced electrochemical properties originate from the highly conductive LSTO protective barrier, which creates an amorphous layer of approximately 4 nm in thickness on the NCM811 surface. The effective separation between NCM811 and the electrolyte provided by this coating leads to the inhibition of severe interfacial side reactions, coupled with enhanced electron transfer kinetics at the electrode/electrolyte interface.
Despite the advantages of phosphoric acid (PA)-doped membranes in high-temperature proton exchange fuel cells (HT-PEMFCs), their limited low-temperature performance and acid leaching hinder further widespread deployment. Herein, we report a gel-state polyelectrolyte with quaternized hierarchical channels (QA-PBI-X-Gel) that overcomes these limitations through integrated structural and chemical design. The membranes feature a multiscale 3D porous architecture enriched with dual binding motifs: imidazole rings and quaternary ammonium (QA+) groups. This design enables efficient operation across a wide temperature range from -20°C to 240°C, fulfilling the dual role of an acid reservoir, where PA is stably retained through hydrogen bonding and ion-pair interactions, and a proton generator, which actively promotes acid dissociation even under subzero conditions. The membrane delivers a fivefold enhancement in low-temperature conductivity, achieves 0.411 S/cm at 240°C, and supports highly competitive peak power densities exceeding 2 W/cm2 while simultaneously enabling reliable subzero operation at -20°C, addressing a critical challenge of HT-PEMFCs in automotive applications. Durability tests confirm its robustness, with voltage degradation rates below 50 µV/h at both 40°C and 180°C. These findings establish a unified design that redefines the operational scope of PEMFCs, bridging the gap between existing low- and high-temperature technologies.
Artificial photosynthesis of C2+ hydrocarbon fuels still faces a fundamental challenge in the rational design of active sites on photocatalysts. Herein, high-spin tetrahedral Co(II) sites (Co2+Td) are implanted through the controllable substitution of CaII ions in nano-hydroxyapatite (HAP). Co-inclusion not only effectively regulates the electronic structure to promote photogenerated charge carrier migration but also modulates the surface properties of CoCaAP nanosticks to enhance CO2 capture and activation. Ex situ XRD and XPS results indicate that CoCaAP transforms into more stable hydrated CoCa phosphate, and partial Co2+ sites are oxidized to Coδ+ with a higher oxidation state in photocatalytic CO2 reduction, thus effectively promoting H2O oxidation and proton transfer. As a result, an exceptional performance for selective CO2-to-ethanol conversion is achieved by CoCaAP, with an ethanol production rate of 779 µmol g-1 h-1 in the absence of any co-catalysts or sacrificial agents under xenon-light irradiation. In situ FT-IR spectra indicate CO2 reduction to ethanol via the CO2 → *HCO3 → *COCOH → *OCH2CH3 pathway. This work provides an accessible strategy for engineering single-atom active sites in HAP to advance efficient solar-to-fuel systems.