The approach offers high deposition rates and a large build envelope, making it attractive for aerospace, automotive and marine applications, while surface finish, dimensional accuracy, anisotropy and fatigue performance remain important limitations. The central finding is that WAAM Ti-6Al-4V cannot be treated as a uniform material. Repeated heating changes the alloy as each new layer is deposited, producing spatial gradients in phase morphology, grain orientation, hardness, residual stress and mechanical behavior.

The alloy contains 6 wt% aluminum and 4 wt% vanadium, which stabilize its alpha and beta phases. Its columnar prior-beta grains often grow epitaxially from the substrate through successive layers, while the alpha laths within them vary with local cooling and reheating. Reported prior-beta grain sizes range from about 0.2 to 4 millimeters.

The review identifies the moving heat source, deposited geometry and later thermal cycles as central mechanisms linking process settings to structure and properties. Three main arc configurations provide different compromises. Gas tungsten arc welding uses a non-consumable tungsten electrode and typically deposits 1 to 2 kilograms per hour.

It is clean and relatively spatter-free, can handle one or two wires, and can be pulsed to alter the thermal profile and reduce porosity. Plasma arc welding constricts the arc inside a nozzle, raising current density and enabling deposition rates of 2 to 4 kilograms per hour. The concentrated arc improves stability and can remove surface oxide through cathode atomization, but greater heat accumulation can increase distortion, grain coarsening and tensile residual stress.

Gas metal arc welding uses consumable wire and reaches 3 to 4 kilograms per hour, or about 9.5 kilograms per hour with a tandem arrangement, but conventional operation is prone to unstable arcs and spatter. Cold metal transfer offers a lower-heat GMAW variant by retracting the wire during short-circuit periods. The review reports that it can reduce heat input by roughly 30 to 50% and dilution from the typical GMAW range of 20 to 30% to below 10%, while producing less spatter.

Its behavior can be adjusted through short-circuit, globular, spray, synergic and pulse-advanced transfer modes. In one comparison, CMT-WAAM Ti-6Al-4V thin walls had a finer, more uniform alpha-lamellar distribution, an average microhardness of 328.9 HV, a tensile strength of 1,045 megapascals and 30.15% elongation compared with a conventional GMA-arc counterpart. These results apply to the tested material and conditions; they do not establish universal performance for every WAAM configuration.

Because Ti-6Al-4V conducts heat at only about 6.7 watts per meter-kelvin, layer-by-layer deposition can create a hot, distorted build with pronounced thermal gradients. The review describes several ways researchers have attempted to dissipate heat, including electric preheating of wire, interlayer dwell periods, forced-air or liquid-carbon-dioxide cooling, progressively reducing heat input as the build rises, fractal-like scanning paths and toolpaths optimized through thermal simulation. A comparison found dwell time more influential than cooling-gas flow under the conditions examined, showing that equipment settings cannot be generalized without considering geometry and build height.

Torch weaving at 1 hertz combined with a low-heat “coldArc” regime also disrupted epitaxial columnar growth and produced a lower-aspect-ratio prior-beta structure and more uniform transformation products. Hybrid processing offers a route to reduce anisotropy, the tendency for strength and ductility to vary with loading direction. Hot forging can create a fully equiaxed microstructure, and one study reported an ultimate tensile strength rising from 961 to 1,137 megapascals and elongation from 8.8% to 15.2%.

In-situ interpass rolling is especially effective because reheating during the next deposition can generate annealing twins and recrystallized grains. Post-process rolling and ultrasonic peening can also introduce compressive residual stress. In a large-scale comparison, hot isostatic pressing achieved full densification and the best overall performance among the treatments examined, while solution treatment and aging improved strength but produced comparatively small gains in fatigue-relevant properties.

These studies also show that strengthening and ductility do not always rise together. Atmospheric control is equally important because hot titanium readily absorbs oxygen, nitrogen and hydrogen. Local argon shielding can restrict oxygen pickup to tens or a few hundred parts per million, although lower regions of large builds may remain exposed as heat accumulates.

A sealed GTAW-WAAM chamber that maintained oxygen below 10 parts per million produced 99.98% near-theoretical density, a refined Widmanstatten microstructure, an ultimate tensile strength of 964 plus or minus 29 megapascals and a dry-sliding wear rate about 40% below conventionally manufactured Ti-6Al-4V. Another controlled study found that increasing oxygen from 1 to 1,000 parts per million raised hardness and tensile strength but reduced elongation from 11.5% to 9.5%. The review notes that direct comparison of local shielding and full enclosure under otherwise identical conditions is still needed.

Fatigue performance remains a demanding test because microscopic defects can override improvements in the bulk microstructure. The review specifically identifies gas pores and surface roughness as important factors in fatigue initiation and crack growth. Tomography of WAAM Ti-6Al-4V made with contaminated wire showed near-surface cracks growing around pores; pore growth accelerated late in life, between 20,000 and 28,000 cycles, equivalent to 65 to 85% of fatigue life.

Samples containing critical pores larger than 100 micrometers had substantially lower fatigue life than nominally defect-free specimens, even though their as-built microstructures and tensile strengths were similar. Clustering of gas porosity also governed fatigue life across process-varied and heat-treated conditions in a large-scale study, reinforcing that microstructural refinement alone cannot guarantee durability. The review’s outlook points toward integrated process design rather than a single universal recipe.

Fractal scanning, pulsed sources, interpass cooling, heat treatment and in-situ deformation are presented as complementary approaches whose effects must be assessed together. Important unanswered questions include how to optimize scanning for residual stress, which grain-refining strategy best balances strength and ductility, and how residual stress affects fatigue-crack growth. Direct evidence linking these variables remains sparse, and the review excludes comprehensive treatment of process monitoring, closed-loop control, digital twins, qualification, certification and high-temperature creep.

Its conclusion is nevertheless clear: WAAM can deliver large Ti-6Al-4V components at high deposition rates, but structural performance emerges from the combined physics of the arc, melt pool, thermal cycling and post-processing, not from deposition speed alone.

Subject of Research: Review of WAAM Ti-6Al-4V process, microstructure, residual stress and mechanical performance

Article Title: Wire arc additive manufacturing of Ti-6Al-4 V alloy: a review

Article References: Mendiratta, M., Chakkravarthy, V., Satish Idury, K. S. N., Singh, N. K., Smirnov, S. A., Tsai, J.-T., Evlashin, S., Chen, K., Zhang, L., Gushchina, M., Klimova-Korsmik, O., & Narayan, R. L. (2026). Wire arc additive manufacturing of Ti-6Al-4 V alloy: a review. Journal of Materials Science: Metallurgy, 1(1), Article 24. https://doi.org/10.1007/s44492-026-00025-x

Image Credits: AI Generated

DOI: 10.1007/s44492-026-00025-x

Keywords: wire arc additive manufacturing, Ti-6Al-4V, directed energy deposition, titanium alloys, microstructure, residual stress, tensile properties, fatigue life, thermal management, Wire, additive, manufacturing

The work, published in Frontiers in Thermal Engineering by Huimin Huang and Qiang Cheng of the State Key Laboratory of Coal Combustion at Huazhong University of Science and Technology, combines first-principles density functional theory calculations with fluctuation-dissipation theory to model two suspended χ3-phase borophene sheets separated by a vacuum gap of 10 nanometers. The researchers applied uniaxial strain ranging from −4 percent compression to +4 percent tension along either the x-axis, which corresponds to the borophene lattice direction a, or the y-axis, perpendicular to it. Their central finding is stark: the direction of strain matters as much as its magnitude. Straining along the x-axis produced a nonlinear response, with the normalized heat transfer coefficient first dipping slightly below unity and then climbing to 1.72 at +4 percent tension. Straining along the y-axis, by contrast, drove a monotonic, switch-like surge, with the coefficient reaching 8.96 at the same tension—roughly a ninefold amplification of near-field heat flow.

The physical mechanism behind this anisotropy lies in borophene’s dielectric function, the quantity that describes how the material’s electrons respond to electromagnetic fields. Borophene is intrinsically anisotropic: its optical response along the lattice direction differs sharply from the perpendicular direction, a property the team confirmed by calculating the complex dielectric function over the frequency range of 0 to 4 electronvolts. Under x-axis compression, the real part of the dielectric function εxx maintains a pronounced resonance peak around 0.8 to 1.0 electronvolts whose intensity grows with increasing compression, while the imaginary part exhibits a stable absorption peak near 1.1 to 1.3 electronvolts that intensifies without shifting position. Compression along the loading direction therefore strengthens the electronic polarization response in that direction, whereas the perpendicular component εyy remains nearly untouched.

Y-axis strain operates through an entirely different channel. Compression along the y-axis leaves the main resonance and absorption structures essentially intact, producing only slight local intensity fluctuations. But large tensile strain along the y-direction—particularly at +3 and +4 percent—triggers a distinct uplift in the low-frequency region of the real dielectric function near zero energy, in both εxx and εyy. This enhanced low-frequency polarization background does not destroy the original resonance modes; instead, it raises the material’s ability to support low-energy evanescent-wave coupling. Because near-field heat transfer between two closely spaced sheets is dominated by the tunneling of evanescent waves at frequencies set by the surface electromagnetic modes, this strain-induced dielectric uplift modifies the resonance condition of those modes and dramatically increases their coupling probability within the dominant heat-transfer frequency window.

The electronic band structure calculations reinforce this picture. The team found that χ3-borophene remains metallic under all investigated strain conditions, with several bands continuously crossing the Fermi level. X-axis strain produces only moderate changes in the electronic structure near the Fermi level, indicating limited reconstruction of low-energy states. Y-axis tensile strain, however, induces pronounced band flattening and denser electronic states near the Fermi level, especially at +4 percent tension. This densification of available states enhances low-energy interband transitions, strengthens the low-frequency dielectric response, and ultimately feeds through to the near-field heat-transfer spectrum via the Fresnel reflection coefficients and the energy transmission coefficient of evanescent waves.

The spectral heat transfer coefficient h(ω) reveals these effects quantitatively. Under single-layer x-axis compression, the main spectral peak—located around ω ≈ 2 × 10^14 radians per second—rises from 4.6 × 10^−16 to 5.5 × 10^−16 joules per radian per kelvin per square meter at −4 percent strain, with the peak position essentially stable. Under single-layer y-axis tension, the response is far more dramatic: the peak value climbs to 9.56 × 10^−15 at +4 percent, roughly an order of magnitude above the corresponding x-direction condition. The team attributes this staged, nonlinear surge to the strain-induced enhancement of the low-frequency dielectric response, which improves the surface electromagnetic response and strengthens evanescent-wave coupling across the dominant frequency range.

When both borophene sheets are strained simultaneously in the same direction—what the authors call bilayer co-directional strain—the modulation effects amplify considerably. Bilayer x-axis tension at +4 percent produces a normalized coefficient of 3.64, with the spectral peak reaching 2.10 × 10^−15, approximately 4.6 times the unstrained value, accompanied by a redshift suggesting strengthened interfacial mode coupling. Bilayer y-axis tension is the standout configuration: the normalized heat transfer coefficient rises from 0.65 under −4 percent compression to 1.31 at +2 percent, then leaps to 28.11 at +3 percent and 125.92 at +4 percent. The spectral peak under this configuration reaches 1.72 × 10^−13, roughly 220 times the unstrained state. The reason both sheets undergo similar y-axis tensile-strain-induced dielectric modulation, so the simultaneous enhancement of the low-frequency dielectric response at the two interfaces improves the matching of surface modes, thereby increasing the evanescent-wave transmission coefficient and producing a much higher spectral peak.

A third configuration—bilayer orthogonal strain, where one sheet is strained along the x-direction and the other along the y-direction at equal magnitude—produces an intermediate outcome. Under compression, the normalized coefficient fluctuates only slightly around unity, with a variation amplitude of approximately 0.08, indicating that strain modulation in orthogonal directions partially cancels out. Under large tensile strain, the coefficient climbs to 4.13 at +3 percent and 20.58 at +4 percent, with the spectral peak reaching 1.83 × 10^−14. The team attributes this enhancement primarily to the y-axis tensile layer, whose strengthened low-frequency dielectric response dominates the coupling within the heat-transfer band. However, because the x-strained layer’s dielectric response and surface-mode dispersion do not fully match those of the y-strained layer, the energy transmission coefficient is enhanced but falls short of the bilayer co-directional y-axis tensile level, demonstrating that the mismatch of strain-modified dielectric responses limits synergistic coupling between the two interfaces.

The strain range investigated here—±4 percent—was deliberately chosen to remain within the elastic deformation regime. Previous first-principles studies have shown that free-standing χ3 borophene possesses good dynamical and mechanical stability, with in-plane Young’s moduli of 180 to 210 newtons per meter along both lattice directions, and prior strain-engineering work suggests deformation up to roughly ±6 percent remains mechanically sustainable. All compressive strain cases converged successfully in geometry optimization without structural collapse or severe out-of-plane instability, lending confidence that the predicted enhancements are physically realizable rather than artifacts of an unstable model.

The implications reach well beyond borophene itself. Near-field radiative heat transfer already underpins emerging technologies including near-field thermophotovoltaics, thermal rectification, localized heat dissipation of nanostructures, and near-field thermal microscopy, and functional devices such as thermal switches, thermal diodes, and thermal transistors have enabled precise heat-flow management at the micro- and nanoscale. Previous strain-engineering efforts in two-dimensional materials—up to 73 percent modulation in black phosphorus and more than 60 percent reduction in thermal conductance in bilayer graphene—demonstrated the principle, but the order-of-magnitude switching behavior predicted here for borophene represents a substantial leap in modulation depth. The authors note that their work provides a theoretical basis for developing efficient thermal management devices based on borophene, and that carefully designing the direction and magnitude of strain enables precise control over near-field heat transport.

Caveats remain. The study is based on an ideal freestanding borophene model; in practical applications, substrates may alter the dielectric response and surface-mode dispersion, while defects, vacancies, and surface contamination may introduce additional scattering or damping. The mechanical and thermal stability of borophene under large tensile strain, as well as the experimental control of strain and nanoscale gaps, must be addressed in future work. Borophene was first synthesized on silver substrates in 2015, and its excellent electronic properties have already driven interest in metal-ion batteries, gas sensing, and micro/nanoelectronics; its outstanding optical properties enable near-field thermal radiation capability that surpasses conventional metals and metal oxides. If strain-tunable borophene devices can be fabricated with the precision the theory demands, the result could be a new class of nanoscale thermal switches in which a few percent of mechanical deformation flips heat flow by two orders of magnitude—a capability that would reshape how engineers manage heat in the smallest devices ever built.

Subject of Research: Strain-tunable near-field radiative heat transfer in borophene sheets

Article Title: Regulation of near-field radiative heat transfer between two-dimensional borophene sheets under uniaxial strain based on first-principles calculations

Article References: Huang, H., & Cheng, Q. (2026). Regulation of near-field radiative heat transfer between two-dimensional borophene sheets under uniaxial strain based on first-principles calculations. Frontiers in Thermal Engineering, 6, Article 1864229. https://doi.org/10.3389/fther.2026.1864229

Image Credits: AI Generated

DOI: 10.3389/fther.2026.1864229

Keywords: borophene, near-field radiative heat transfer, mechanical strain, first-principles calculations, thermal management, two-dimensional materials, Regulation, near-field, radiative, heat, transfer, between

The stakes are considerable. During operation, power batteries generate substantial heat, and inadequate heat dissipation does more than shave performance. Excessively high operating temperatures accelerate battery aging, hasten capacity degradation, reduce driving range, and can trigger thermal runaway, a chain reaction that in severe cases leads to fire or explosion. Yet cooling itself consumes energy, so the central engineering challenge is to maximize heat removal without inflating the power needed to pump coolant through the system. The research team, led by Miao Li and Min Xiong with colleagues at institutions supported by the National Natural Science Foundation of China, set out to determine which channel design strategy best meets specific requirements for peak temperature, temperature uniformity, and energy consumption.

The benchmark system is a power battery pack comprising twelve series-connected battery modules, each built from twenty cylindrical 18650 lithium-ion cells with a nominal voltage of 3.6 volts, arranged in parallel with liquid cooling plates on both sides. Rather than simulating the entire pack, the researchers exploited the identical structure of each module and modeled a single representative module in three dimensions. The most punishing operating condition considered was high-temperature fast charging at an ambient temperature of 43 degrees Celsius and a charge rate of 4C, from 30 to 80 percent state of charge. Under these conditions the cells generate heat at a power density of 1.2 times ten to the fifth watts per cubic meter, a figure the team adopted as a constant simplification. Coolant, an ethylene glycol water solution, enters at 25 degrees Celsius with a flow velocity of 0.5 meters per second, and the copper cooling plate substrate strips heat from the cells by conduction into the flowing liquid.

The baseline against which all new designs were judged is the traditional straight channel liquid cooling plate, a configuration with six parallel branch channels commonly used in commercial battery modules. The numerical model was first validated against experimental data from a previously reported straight channel system, with maximum errors of 7.24 percent for flow rate versus pressure drop and 3.27 percent for maximum temperature, demonstrating reliability. Under the 4C charging condition, the straight channel design produced a peak battery temperature of 38.73 degrees Celsius, comfortably below the 45 degree upper limit for normal operation, but a stable operating temperature of 30.30 degrees Celsius, just above the optimal 20 to 30 degree window. The fluid pressure drop, which dictates pumping energy consumption, stood at 981.43 pascals. All four new channel designs were then created using the same fluid volume fraction of 0.5, ensuring a fair comparison of channel morphology at identical material usage.

Three of the new designs came from the bionic design method, which translates efficient natural transport structures into engineered channel layouts. The leaf vein-shaped channel copies the fractal, multi-level branching and self-similar architecture that allows plants to distribute water with remarkable efficiency, increasing channel surface area and improving coolant distribution uniformity. The honeycomb-shaped channel exploits the large surface area and internal multi-branching nature of cellular structures, which promotes higher fluid velocity along inner channel sections while flow along the sides remains slower, thinning the thermal boundary layer and enhancing heat transfer. The serpentine channel, inspired by winding natural paths, increases contact area between coolant and plate while the curved geometry slows the coolant, prolonging its residence time and improving cooling performance.

The fourth design emerged from topology optimization, a computational method that requires no natural template. The researchers formulated a multi-objective optimization problem on a two-dimensional design domain matching the cooling plate dimensions, with inlet and outlet ports arranged in parallel at diagonal corners. Using the Brinkman penalty model, the design domain is treated as a porous medium whose resistance depends on a design variable, the unit pseudo-density gamma, which characterizes material distribution at every element. When gamma equals one the element is fluid and flow proceeds unpenalized; when gamma equals zero the element is solid and flow velocity is driven to zero. The objective function balances maximizing heat transfer against minimizing coolant power dissipation, with a weighting coefficient of 0.5 giving equal priority to both goals. Density filtering via a Helmholtz partial differential equation addressed grid dependency and ill-posedness, while a hyperbolic tangent projection method eliminated grey-scale elements, yielding a crisp fluid-solid boundary that produces channel geometry close to practically manufacturable designs. The optimized two-dimensional channel shape was then extruded along the plate thickness to construct the three-dimensional cooling channel.

The thermal comparison revealed subtle but meaningful differences. Peak temperatures across all designs clustered tightly: 38.65 degrees for the leaf vein channel, 38.43 for honeycomb, 38.42 for serpentine, and 38.37 for the topology-optimized design, the latter achieving a 0.93 percent reduction compared with the straight channel baseline. The topology-optimized channel therefore controlled the highest temperature reached during the entire charging process better than any other configuration. During the early simulation phase, module temperature rose as heat accumulated faster than the coolant could remove it, but as time progressed the temperature declined and stabilized once the system reached thermal equilibrium.

Temperature uniformity within individual cells matters as much as absolute temperature, because non-uniform temperatures can drive excessive charging or discharging in particular cells, accelerate aging, and raise safety hazards. The researchers quantified uniformity using the temperature standard deviation of individual cells, where a smaller value indicates a more even distribution. Compared with the straight channel plate, the leaf vein, honeycomb, and topology-optimized designs all reduced the standard deviation, with the topology-optimized plate exhibiting the most superior uniformity across the cells analyzed.

Stable operating temperatures told a different story. The honeycomb, serpentine, and topology-optimized channels achieved stable operating temperatures of 29.21, 29.09, and 29.49 degrees Celsius respectively, all below 30 degrees and within the optimal operating range, representing reductions of 3.60, 3.99, and 2.67 percent compared with the straight channel. The leaf vein channel, by contrast, produced a stable temperature of 30.47 degrees, slightly higher than the baseline. For applications demanding prolonged stable operation, the honeycomb and serpentine structures proved preferable, while temperature uniformity as the priority pointed toward the leaf vein or topology-optimized structures.

Energy consumption, reflected in the pressure drop the coolant experiences, delivered the study’s most striking contrast. The topology-optimized design achieved the lowest pressure drop at 855.25 pascals, a 12.86 percent reduction versus the straight channel, with the leaf vein channel second lowest at 877.52 pascals, a 10.59 percent reduction. The honeycomb channel increased pressure drop by 4.87 percent, and the serpentine channel proved the most expensive by far, at 1579.16 pascals, a 60.90 percent increase that translates directly into higher pumping power under the same flow rate. Although the serpentine channel offered minor thermal improvement, its flow resistance should weigh heavily in practical applications. Taken together, the results show that when both heat dissipation performance and energy consumption are considered, the topology-optimized cooling channel holds distinct advantages, while bionic designs excel in specific niches. Beyond battery thermal management, the researchers note that the channel optimization methods can extend to cooling high heat flux electronic devices such as chips and CPUs, and future work will incorporate turbulence-based optimization models and sensitivity analyses of key operating parameters to better match channel structures to real operating conditions.

Subject of Research: Liquid cooling channel design for electric vehicle power batteries using bionic and topology optimization methods

Article Title: Liquid cooling channels designs by both bionic design method and topology optimization method for electric vehicle main power battery

Article References: Li, M., Xiong, M., Lin, C., Yang, J., Ding, X., & Zhang, H. (2026). Liquid cooling channels designs by both bionic design method and topology optimization method for electric vehicle main power battery. Results in Engineering, 32, Article 113473. https://doi.org/10.1016/j.rineng.2026.113473

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113473

Keywords: electric vehicle, power battery, liquid cooling, topology optimization, bionic design, thermal management, lithium-ion battery, pressure drop, thermal runaway, battery pack, Liquid, cooling

The research team, led by Anuj Prajapati of Trinity Institute of Technology and Research in Bhopal, together with colleagues at MIT Art Design and Technology University in Pune and partner institutions, drew on the NASA Ames Prognostics Center of Excellence lithium-ion battery ageing dataset. This publicly available resource documents commercial 18,650-format cells cycled repeatedly until they lost roughly 30 percent of their nominal 2.0 ampere-hour capacity, dropping to about 1.4 ampere-hours. From four batteries — designated B0005, B0006, B0007, and B0018 — the researchers extracted 169,766 discharge-cycle samples recorded at an ambient temperature of 24 degrees Celsius under an approximately 2 ampere discharge current, corresponding to roughly a 1C rate relative to nominal capacity. The sampling interval was non-uniform, varying between approximately 9 and 20 seconds, a detail that would prove important for how the team engineered its features.

What distinguishes this study from many prior efforts is its insistence on what the authors call leakage-safe validation. Conventional temperature-prediction studies often split data randomly across samples, which means that measurements from the same physical battery can appear in both the training and test sets. The model effectively gets glimpses of the very cell it is being evaluated on, and the resulting accuracy figures — sometimes approaching perfect correlation — can dramatically overstate real-world performance. The Indian team instead adopted a leave-one-battery-out strategy: in each of four validation runs, one complete battery was held out as an entirely unseen test cell, while data from the remaining three batteries were combined for training. Training sets ranged from 121,467 to 137,775 samples, and test sets from 31,991 to 48,299, depending on which battery was withheld. This design mirrors what a real battery management system must do — estimate temperature on a new cell it has never encountered, not merely interpolate within a familiar one.

The feature engineering was equally deliberate. Rather than feeding raw signals alone, the researchers constructed five physically meaningful inputs: measured voltage, measured current, instantaneous discharge power, cycle-wise cumulative discharged energy, and voltage drop. Power was computed as the product of measured voltage and the absolute magnitude of discharge current, ensuring a positive quantity in watts despite the negative sign convention of discharge current. Because the sampling interval was non-uniform, cumulative energy was calculated by integrating instantaneous power over each time increment between consecutive samples, with the accumulation restarted at the beginning of every discharge cycle to prevent energy carry-over between independent cycles. Voltage drop — the difference between the charge or reference voltage and the measured voltage — served as a proxy for internal-resistance losses. Measured temperature was the prediction target, and the battery identifier was used only for grouping during validation, never as a model input.

Descriptive analysis before model training revealed striking relationships. Measured voltage and power each showed strong negative correlations with temperature, with correlation coefficients of approximately −0.92, while cumulative discharged energy showed a strong positive correlation of roughly 0.90 — confirming that temperature climbs as a cell expends more energy over a discharge cycle. Voltage drop showed only a weak positive association, and measured current displayed negligible direct linear correlation. Yet the team retained current as an input, reasoning that tree-based ensemble learners can capture nonlinear and interaction effects that linear correlation statistics miss entirely. Smoothed trend curves reinforced the picture: average temperature rose consistently with cumulative energy, though the rise flattened somewhat at higher energy levels, while the temperature–power relationship was nonlinearly inverse, reflecting the fact that voltage and power are highest during the early, cooler stages of discharge and decline as the cell warms toward later states.

Three supervised models competed for the crown. Linear Regression served as the interpretable baseline, Random Forest brought bootstrap-aggregated decision trees to bear on the nonlinear electrothermal behaviour, and Light Gradient Boosting Machine — LightGBM — represented the state of the art, with its leaf-wise tree growth strategy and histogram-based optimization well suited to high-frequency thermal data. The results were decisive. LightGBM achieved the strongest overall generalization, with a mean root mean square error of 1.070 degrees Celsius and a mean coefficient of determination of 0.910 across the four unseen-battery runs. Random Forest was closely competitive, with a mean RMSE of 1.081 degrees Celsius, an R² of 0.909, and the lowest mean absolute error of the three models at 0.893 degrees Celsius. Linear Regression trailed badly, with a mean RMSE of 1.826 degrees Celsius, a mean absolute error of 1.576 degrees Celsius, and an R² of just 0.744. Relative to the linear baseline, LightGBM reduced RMSE by approximately 41.4 percent, cut mean absolute error by 43.0 percent, improved R² by 22.3 percent, and reduced unexplained variance by roughly 64.8 percent.

The battery-wise breakdown revealed instructive variation. Both ensemble models performed best on cell B0007, where LightGBM posted an RMSE of just 0.678 degrees Celsius and an R² of 0.967, while errors were comparatively higher for B0006 and B0018 — suggesting that cell-specific ageing behaviour and cycle-level operating differences genuinely influence how well a model trained on other cells transfers to a new one. Uncertainty analysis, quantified through the standard deviation of metrics across the four validation runs, showed that the ensemble models delivered far more stable performance than the linear baseline, whose larger error bars betrayed its brittleness. The team also partitioned the measured temperature data into four ranges — 20 to 30 degrees, 30 to 35 degrees, 35 to 40 degrees, and above 40 degrees Celsius — and found that prediction error increased at elevated temperatures for all models, indicating that the nonlinear, cell-specific behaviour of hot batteries is inherently harder to model under unseen-cell conditions. Breusch–Pagan and White heteroscedasticity tests, both returning p-values below 0.001 for every model, confirmed statistically that residual variance was non-constant across the prediction range, validating the temperature-range-wise approach to stability assessment.

Residual diagnostics painted the same story in finer grain. The linear model’s residuals stretched from roughly −9 to +6 degrees Celsius, were not centred on zero, showed a clear bimodal tendency, and exhibited curved, widening bands against predicted temperature — with systematic underprediction at higher temperatures that betrayed its inability to capture the nonlinear thermal response. Random Forest narrowed the residual spread to within approximately ±3 degrees Celsius, though a mild downward trend persisted at elevated predictions. LightGBM matched that compact error distribution while maintaining the best overall balance between accuracy, residual control, and explanatory power. The team then turned to SHAP — SHapley Additive exPlanations — a game-theoretic interpretability method that assigns each feature a contribution value for every prediction. The analysis identified power, cumulative discharged energy, and measured voltage as the dominant predictors. Lower power values pushed predictions toward higher temperatures, consistent with the discharge progression in which power falls as cells warm, while higher cumulative energy shifted predictions upward and lower voltage readings signalled hotter states. Intriguingly, the SHAP dependence analysis showed that power’s influence was not independent: below roughly 7 watts, its SHAP values were predominantly positive, but beyond that threshold they shifted toward zero and then negative, with the direction modulated by the simultaneous voltage condition of the cell — a genuine interaction effect that a linear model could never represent.

The practical implications reach deep into battery management system design. Because the framework identifies which parameters matter most, engineers can focus sensor selection and data acquisition on the electrical and electrothermal variables that genuinely govern thermal behaviour, rather than instrumenting everything indiscriminately. The residual and uncertainty analyses offer a template for assessing reliability before deployment, and the SHAP results give designers a physically interpretable map of how instantaneous loading, cumulative energy throughput, and voltage response jointly control thermal evolution. The authors are candid about limitations: the study rests on a single dataset of four 18,650 cells under controlled conditions, with no information on exact cell chemistry, sensor accuracy, or data-acquisition hardware, and no inclusion of ageing indicators, impedance response, coolant behaviour, or real driving-load profiles. Future work, they argue, should extend validation to broader chemistries, C-rates, ambient temperatures, and dynamic drive cycles, and pursue hybrid physics-informed machine learning that fuses data-driven prediction with electrochemical and thermal-domain knowledge. For now, the study stands as a compelling demonstration that honest validation — testing on cells the model has truly never seen — is not just methodological rigour, but the difference between a temperature estimate that looks good in a notebook and one that can be trusted inside a moving electric vehicle.

Subject of Research: Machine learning prediction of lithium-ion battery temperature during discharge cycles with battery-wise validation and SHAP interpretability

Article Title: Optimizing temperature prediction in automotive lithium-ion battery discharge cycles using machine learning and SHAP analysis

Article References: Prajapati, A., Thorat, S. G., Pradhan, G., Gund, S., Paraye, P., & Singh, V. (2026). Optimizing temperature prediction in automotive lithium-ion battery discharge cycles using machine learning and SHAP analysis. Frontiers in Mechanical Engineering, 12, Article 1949090. https://doi.org/10.3389/fmech.2026.1949090

Image Credits: AI Generated

DOI: 10.3389/fmech.2026.1949090

Keywords: lithium-ion batteries, machine learning, LightGBM, Random Forest, SHAP analysis, thermal management, battery management systems, electric vehicles, NASA battery dataset, temperature prediction, leave-one-battery-out validation, feature engineering

The study, authored by Mohammad Waseem of Jamia Millia Islamia in New Delhi, takes an unusually broad view of the field. Rather than focusing on a single function, it critically assesses 120 research articles and review papers to build a system-level picture of how artificial intelligence and machine learning are transforming battery management across state estimation, cell balancing, thermal management, fault diagnosis, predictive maintenance, intelligent charging, edge computing, and cloud connectivity. According to the review, this integrated perspective is what previous surveys have lacked, and it reveals both how far the technology has come and where its most stubborn bottlenecks remain.

At the heart of every battery management system, or BMS, lies a deceptively simple question: what is the battery actually doing right now? Two quantities dominate the answer. The state of charge, or SoC, describes how much energy remains in the pack relative to its nominal capacity, while the state of health, or SoH, captures how much the battery has degraded over its lifetime. Neither can be measured directly. Both must be inferred from voltage, current, and temperature readings using computational algorithms, a task made harder by the fact that lithium-ion cells behave in strongly nonlinear ways that shift with temperature, aging, and driving patterns.

Conventional BMS architectures have long relied on model-based techniques such as coulomb counting, equivalent circuit models, and Kalman filtering to estimate these hidden states. These methods have served the industry well, but the review highlights a fundamental weakness: they struggle to represent the nonlinear electrochemical behavior of lithium-ion batteries under varying operating conditions. As battery packs in modern electric vehicles grow larger and more complex, the amount of real-time sensor data they generate has outstripped what traditional models can meaningfully process, and subtle degradation patterns slip through the cracks.

This is where machine learning enters the picture. Artificial neural networks have been widely applied to state of charge estimation because of their ability to capture complex nonlinear battery dynamics, and experimental studies cited in the review show that ANN-based models can achieve significantly improved accuracy compared with conventional estimation techniques, particularly under dynamic operating conditions. Support vector machines and ensemble learning methods have proven effective at detecting battery faults and abnormal operating conditions at early stages, flagging problems before they escalate into safety hazards. The trade-off is real, however: these models demand large training datasets and substantial computational resources, which can complicate deployment on the embedded processors that actually live inside a vehicle.

Deep learning has pushed the frontier further. Long short-term memory networks, convolutional neural networks, and deep neural networks have been extensively explored for predicting battery degradation and remaining useful life, the parameter known as RUL. LSTM networks are particularly well suited to this task because they excel at analyzing time-series data and capturing the temporal dependencies that characterize how batteries age. The review notes that studies published in leading energy journals have demonstrated that deep learning models can achieve high prediction accuracy and improved robustness compared with traditional analytical models, though the price is longer training times and heavier computational demands.

Beyond estimation, artificial intelligence is reshaping how batteries are charged and protected. Reinforcement learning algorithms learn optimal charging policies through interaction with the battery system and its environment, dynamically adjusting charging rates based on real-time inputs such as state of charge, state of health, temperature, and grid conditions. The goal is to balance charging speed against battery longevity while avoiding degradation mechanisms like lithium plating and thermal stress. The review reports that reinforcement learning approaches have shown improved energy efficiency and reduced battery aging compared with traditional charging methods, although they require extensive training cycles and carefully designed reward functions to perform stably.

Thermal management represents another critical frontier. Because lithium-ion battery performance and safety are tightly coupled to temperature, improper thermal regulation can accelerate degradation and, in the worst case, trigger thermal runaway, a failure mode characterized by uncontrolled heat generation and potential fire. Smart BMS designs now incorporate per-cell or per-module temperature sensing using thermistors and integrated sensing devices, feeding real-time thermal data into control units that dynamically activate cooling or heating mechanisms and adjust charge and discharge limits. Convolutional neural networks and autoencoders have proven especially effective at detecting thermal anomalies and hidden fault patterns in sensor data, giving the BMS an early warning system that rule-based logic alone cannot match.

The review also spotlights two emerging hardware and infrastructure trends that could define the next generation of battery management. The first is FPGA-based edge implementation, in which field-programmable gate arrays and embedded processors execute machine learning models with the low latency required for safety-critical real-time control. Recent work on FPGA-based intelligent battery management units demonstrates dynamic power distribution techniques, though the review cautions that these platforms bring higher implementation cost and design complexity. The second is IoT-enabled cloud integration, in which distributed sensors transmit battery data to centralized platforms where machine learning models perform fleet-level optimization, predictive diagnostics, and over-the-air updates. Prototype systems integrating these technologies have demonstrated improved reliability and predictive maintenance capabilities, but they introduce new concerns around cybersecurity, communication latency, and data management at scale.

Perhaps the review’s most consequential conclusion is that no single artificial intelligence technique is universally optimal. Artificial neural networks and LSTM models deliver high estimation accuracy for state of charge and state of health, convolutional architectures excel at fault diagnosis, and support vector machines and random forests offer robust performance with lower computational requirements that suit embedded systems. The most promising pathway forward, the author argues, lies in hybrid approaches that combine model-based methods with data-driven algorithms, leveraging the physical understanding embedded in equivalent circuit models and Kalman filters alongside the pattern-recognition power of machine learning. Future directions identified in the review include physics-informed machine learning, digital twins, edge intelligence, and cloud-connected battery ecosystems, all of which point toward battery management systems that are not merely protective but genuinely predictive and autonomous.

Subject of Research: AI and machine learning applications in smart battery management systems for electric vehicles

Article Title: AI/ML enabled smart battery management systems in electric vehicles

Article References: Waseem, M. (2026). AI/ML enabled smart battery management systems in electric vehicles. Frontiers in Batteries and Electrochemistry, 5, Article 1883201. https://doi.org/10.3389/fbael.2026.1883201

Image Credits: AI Generated

DOI: 10.3389/fbael.2026.1883201

Keywords: battery management system, electric vehicles, lithium-ion batteries, machine learning, artificial intelligence, state of charge, state of health, deep learning, thermal management, predictive maintenance, FPGA, IoT

The thermal problem begins with a deceptively simple mechanism. During every charge and discharge cycle, current flowing through the electrolyte and electrode materials encounters the equivalent series resistance, or ESR, generating Joule heat inside the cell. That internal heat accumulates, and at elevated temperatures it accelerates the decomposition of electrolytes and electrodes, speeds up self-discharge, and in extreme cases can trigger electrolyte breakdown, gas evolution, or even thermal runaway. Cold is no friend either: at low temperatures, ionic conductivity in the electrolyte drops, viscosity rises, and the accessible capacitance shrinks. The result is a bidirectional thermal challenge in which both extremes of the operating envelope degrade performance, just through different mechanisms.

What makes the review particularly timely is a counterintuitive discovery from recent research. Li and colleagues identified a negative Joule heat effect in supercapacitor modules under specific conditions, namely high-frequency charge-discharge cycling at low current densities in multi-cell configurations. In other words, under certain operating regimes the modules do not simply heat up as expected, adding a layer of complexity to thermal behavior prediction that existing models struggle to capture. This phenomenon underscores how much remains unknown about heat generation in devices that are often assumed to be thermally simpler than batteries.

The degradation story is told at the level of individual components. Electrolytes, which transport ions between electrodes, follow Arrhenius-type behavior in which ionic conductivity increases with temperature, but only up to a point: at high temperatures, decomposition, solvent evaporation, and rising internal pressure take over. Organic electrolytes typically operate between roughly 233 K and 343 K, aqueous systems between 273 K and 333 K, while ionic liquids can function up to about 450 K, making them increasingly attractive for high-temperature applications. Electrodes made of activated carbon or carbon nanotubes face their own thermal limits, and the binders that hold them together, such as polyvinylidene fluoride, can soften or melt, causing delamination between active material and current collectors. Separators, the ion-permeable membranes between electrodes, can lose structural integrity at high temperatures, risking short circuits, while below about 243 K ion mobility through their pores becomes severely restricted.

The aging mechanisms are particularly insidious because they feed on themselves. High temperatures generate gases such as hydrogen and carbon monoxide that build internal pressure, sometimes venting through safety valves before the conventional 20 percent capacitance-loss end-of-life threshold is even reached. Solid decomposition products block the pores of the carbon electrodes, reducing the effective surface area and thus capacitance. Joule heating accelerates electrochemical degradation, which raises ESR, which in turn generates more Joule heat, a positive feedback loop that progressively elevates internal temperature. Current collectors oxidize at high temperatures, increasing contact resistance, and acetonitrile-based cells have been reported to lose roughly 25 percent of their weight through electrolyte evaporation after 12,000 seconds at 3 V and 343 K, an accelerated stress condition well beyond normal operating limits.

Against this backdrop, the review evaluates the arsenal of thermal management strategies, dividing them into passive and active systems. Passive approaches, which consume no parasitic electrical energy, include natural convection, heat pipes, and phase change materials, or PCMs. Heat pipes exploit a closed two-phase cycle in which a working fluid evaporates at the hot end and condenses at the cool end, achieving near-isothermal heat transport. In supercapacitor-specific experiments, heat pipe cooling reduced peak cell temperature by 7.5 to 11.7 percent and improved capacitance retention by about 2.1 percent compared with natural convection. PCMs, typically paraffin wax with melting points in the 303 to 333 K range, absorb latent heat during high-power bursts and release it during rest, buffering temperature excursions, though their low intrinsic thermal conductivity of 0.1 to 0.3 watts per meter-kelvin limits heat transfer rates unless enhanced with metal foams, fins, or conductive meshes.

Active systems trade simplicity for performance. Forced air cooling is the most widely implemented active approach, and careful engineering pays dividends: Voicu and colleagues found that a staggered module with cell spacing of 1.25 times the cell diameter optimizes heat removal, while Soltani and colleagues identified an optimal air speed of 5 meters per second and 5-millimeter spacing for a 12-cell lithium-ion capacitor module, with predicted and measured temperatures agreeing within 2 K. Liquid cooling, with water’s thermal conductivity roughly 25 times that of air, offers the highest heat removal capacity, achieving up to 35 percent lower maximum temperatures in supercapacitor-specific experimental studies. But it brings complexity, weight, sealing requirements, and leakage risks. The most striking results come from hybrid systems: combining PCMs with heat pipes achieved approximately 35 percent lower peak cell temperatures and 86 percent lower temperature non-uniformity than PCM-only cooling, a configuration well matched to the burst-and-rest duty cycles that characterize supercapacitor applications.

The review does not shy away from the field’s shortcomings. The authors identify critical research gaps, including limited understanding of molecular-level degradation mechanisms, an absence of standardized testing protocols that would make results from different laboratories directly comparable, and insufficient development of cost-effective thermal interface materials. The numbers are telling: thermal interface resistance between cells and aluminum cold plates is estimated at 10 to 50 K cm²/W for commercial silicone pads, comparable in magnitude to the cell-level thermal resistance itself. Meanwhile, the longest calendar aging studies span only 12 to 18 months, yet the Arrhenius rule is routinely used to project 10-to-15-year lifetimes, an extrapolation of nearly an order of magnitude beyond validated data.

The future directions the authors outline are equally concrete. They call for hybrid and adaptive thermal management systems that co-optimize PCM melting point, layer thickness, heat pipe geometry, and condenser configuration as a coupled system, along with adaptive control strategies that modulate cooling intensity based on real-time temperature and state-of-health estimates. They also highlight the need for non-invasive in-situ temperature mapping, noting that although the electrode is the dominant thermal resistance in supercapacitor stacks, nearly all reviewed studies measure surface temperature only, leaving internal gradients to be inferred from models. Fiber Bragg grating-based internal temperature mapping of a 20-cell module during fast cycling, with spatial resolution of 5 millimeters or better, is proposed as a measurable milestone.

For engineers designing the next generation of energy storage, the message is clear: no single cooling approach is universally optimal, and the choice depends on power density, available volume, cost constraints, and operating temperature range. As supercapacitors push into ever more demanding roles, from regenerative braking to grid-scale stabilization, the invisible battle against heat will increasingly determine whether these remarkable devices fulfill their potential. The Wichita State team’s synthesis provides both a warning and a blueprint, showing that thermal management is not an afterthought but a first-order design constraint for the future of high-power energy storage.

Subject of Research: Thermal management strategies and temperature effects in supercapacitors

Article Title: Thermal management of supercapacitors: a review of thermal challenges, management strategies, future directions

Article References: Atayo, A., Subeshan, B., Asmatulu, E., & Rahman, M. (2026). Thermal management of supercapacitors: a review of thermal challenges, management strategies, future directions. Frontiers in Thermal Engineering, 6, Article 1783912. https://doi.org/10.3389/fther.2026.1783912

Image Credits: AI Generated

DOI: 10.3389/fther.2026.1783912

Keywords: supercapacitors, thermal management, Joule heating, phase change materials, heat pipes, liquid cooling, electrolyte degradation, thermal runaway, aging mechanisms, lithium-ion capacitors, energy storage, cooling systems

The reactor in question stores hydrogen in LaNi5, a lanthanum-nickel alloy that absorbs hydrogen reversibly into its crystal lattice. Solid-state storage in metal hydrides is attractive because it packs hydrogen at high volumetric density, operates safely at moderate pressures, and avoids the cryogenic or high-pressure tanks required for gaseous hydrogen. But desorption, the process of releasing hydrogen from the hydride, is endothermic: it consumes heat. If heat cannot reach the reactive bed quickly enough, the reaction slows, part of the bed sits idle, and the reactor’s discharge rate suffers. Thermal management has long been identified as a major obstacle for practical hydride devices.

Previous work had established that inserting conductive metal foams into the hydride bed shortens the thermal path from the heated wall to the reactor core. Among fixed alternatives, a CuCr1Zr foam at 89 percent porosity gave the strongest desorption response, completing discharge in about 10 minutes. The new study asks a more ambitious question: what if the foam is not uniform at all, but instead graded radially, with a dense, highly conductive CuCr1Zr-rich region near the heated wall and a lighter, more permeable AlSi10Mg-rich composition toward the core? The researchers also coupled this structural grading with a transient operating schedule, using stronger heating and lower desorption pressure in the opening minutes before conditions relax to milder values.

To explore this continuous design space, the team developed PG-SADE-GHF, a physics-guided self-adaptive differential evolution framework for graded hybrid foam design. The method parameterizes the foam with smooth radial porosity profiles, spatial alloy allocation, and time-dependent heat-transfer and pressure schedules, all encoded in eight design variables. A self-adaptive differential evolution algorithm, which adjusts its mutation and crossover parameters based on successful trials, searches the space. In an equal-budget stress test, the adaptive variant achieved a lower final diagnostic objective in 29 of 30 paired runs compared with a fixed-parameter version, supporting its use for the search procedure.

Crucially, the study does not rely on a cheap surrogate model for its final claims. The original MATLAB surrogate predicted a desorption completion time of 7.95 minutes for its selected design. But when those exact coordinates were evaluated directly in OpenFOAM, a high-fidelity finite-volume CFD solver, the same design took 12.192 minutes. This 53 percent mismatch is retained transparently in the paper as a negative result, demonstrating that the CFD model was not tuned to recover the desired value. Instead, the researchers re-optimized the same eight variables using actual OpenFOAM evaluations within the original design bounds, arriving at a high-fidelity optimum of 7.817 minutes, only 1.68 percent from the original surrogate magnitude and 21.83 percent faster than the 10-minute uniform CuCr1Zr benchmark.

The team then performed an expanded-bound sensitivity study, widening the engineering envelope for heating intensity, pressure, and schedule duration. This separate extension reached 6.442 minutes, 35.58 percent faster than the uniform reference. Full 360-degree three-dimensional verification on a 196,608-cell mesh confirmed both results, with the expanded-bound wedge, standard full-3D, and high-resolution full-3D calculations all giving identical 6.442-minute completion times. Mesh and time-step independence checks showed maximum deviation of only 0.129 percent from the finest result, confirming that the reported performance is not an artifact of numerical resolution.

Beyond the reactor design itself, the study makes a methodological contribution to how AI-assisted engineering should handle the gap between cheap and expensive models. The researchers trained a CFD-corrected multifidelity model on the difference between surrogate predictions and OpenFOAM outcomes across 32 development designs. They then generated 12 additional Sobol/maximin challenge designs whose predictions were frozen and SHA-256 locked before their CFD outcomes were computed. On this locked held-out challenge, the original surrogate yielded a root-mean-square error of 3.354 minutes, while the CFD-corrected model achieved R-squared of 0.982, RMSE of 0.159 minutes, and 100 percent coverage of the nominal 95 percent prediction intervals. This corresponds to a 95.27 percent RMSE reduction, showing that sparse high-fidelity information can systematically correct low-fidelity bias.

The optimized coordinates reveal why the high-fidelity solution differs from the original surrogate point. OpenFOAM places more CuCr1Zr in the core-side transition, extends the conductive wall zone, uses stronger early heat transfer, and maintains a lower early outlet pressure for longer. These trends suggest a stronger need for heat delivery and desorption driving force than the reduced model estimated. A matched-condition decomposition also prevents over-attribution of the total speed-up to grading alone: within the original bounds, most of the gain comes from operating-schedule optimization, while the expanded design exhibits a larger independent architecture benefit.

The study is careful to distinguish its contribution from prior graded-foam work. Graded porosity itself is not new; previous studies reported roughly 11.5 to 20 percent improvements during hydrogen absorption with gradient-porosity foams. The novelty here is narrower and process-specific: desorption is endothermic and requires sustained heat input while hydrogen pressure is kept below equilibrium, so the design problem couples where conductive material is placed with when heat-transfer intensity and outlet pressure are applied. This simultaneous radial material allocation and transient scheduling, followed by transparent surrogate-to-CFD re-optimization, is the central contribution.

The researchers define a concrete experimental validation pathway rather than leaving future work unspecified. A first prototype should manufacture the conductive outer and more porous inner lattice regions as separately controlled additively manufactured inserts, with X-ray micro-CT and gravimetric measurements verifying porosity, strut dimensions, and pore connectivity. The assembled reactor should be instrumented with radial and axial thermocouples, pressure sensing, and hydrogen mass-flow measurement, with the primary comparison being measured desorption time against the same-bound OpenFOAM prediction. Critical uncertainties include foam-wall and foam-hydride contact resistance, manufactured pore size, alloy-interface behavior, and kinetic drift with cycling. The OpenFOAM model is a porous-continuum representation rather than a pore-resolved strut simulation, and the authors state these limitations explicitly. Experimental validation remains required before the predicted performance can be considered established.

Subject of Research: hydrogen storage

Article Title: Artificial intelligence-assisted design and CFD re-optimization of graded hybrid metal foams for faster hydrogen desorption in LaNi5 metal-hydride reactors

Article References: Djeffal, S., Bendriss, B., Chibani, A., Ghoul, A., Morakchi, M. R., & Rashid, F. L. (2026). Artificial intelligence-assisted design and CFD re-optimization of graded hybrid metal foams for faster hydrogen desorption in LaNi5 metal-hydride reactors. Frontiers in Energy Research, 14, Article 1956543. https://doi.org/10.3389/fenrg.2026.1956543

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1956543

Keywords: hydrogen storage, LaNi5, metal hydride, metal foam, CFD, OpenFOAM, differential evolution, surrogate model, desorption, thermal management, additive manufacturing, multifidelity

The work, published in the journal Results in Chemistry by A.M. Metha and S. Sumathi, addresses a persistent dilemma in pigment chemistry. Traditional dark pigments, including many iron oxide and chromium-based compounds, absorb broadly across both the visible and near-infrared regions, converting absorbed sunlight into surface heat. This contributes to the urban heat island effect, in which cities become substantially warmer than their surroundings because of heat-absorbing construction materials, and it drives up the energy demand for air conditioning. Many classic pigments also carry a toxic burden, relying on cadmium, selenium, chromium, or lead, all of which pose risks to human health and the environment. A benign pigment that combines strong color with high near-infrared reflectance has therefore become a prized target in materials research.

The Indian team chose strontium molybdate, SrMoO4, as the host lattice, and the choice is chemically shrewd. The compound adopts the tetragonal scheelite structure, an arrangement in which isolated tetrahedral molybdate groups sit among strontium ions. Its wide band gap of about 4.2 electronvolts means the material itself is essentially colorless and nearly transparent to visible light, and its low intrinsic absorption in the near-infrared makes it an excellent reflector in that band. Because of its forgiving framework, foreign ions can be slipped into the lattice without destroying the structure, which is precisely what doping experiments require. Previous studies had already exploited this versatility, producing yellow pigments with cerium doping and luminescent materials with europium, dysprosium, and terbium.

To generate color, the researchers turned to iron, a proven chromophore for warm tones. They synthesized a complete series of compounds with the formula Sr1-xFexMoO4, where the iron fraction ranged from zero to half of the strontium content, using a hydrothermal method that is remarkably gentle by pigment standards. Aqueous solutions of strontium nitrate, ferric nitrate, and ammonium molybdate were mixed, the pH was adjusted to neutral with ammonia, and the mixture was sealed in a Teflon-lined autoclave at 180 degrees Celsius for fifteen hours. Conventional reddish-brown pigments often require calcination at 600 to 1200 degrees Celsius or beyond, so a low-temperature, water-based route carries both energy and cost advantages for industrial scale-up.

Structural analysis confirmed the doping strategy worked cleanly. Powder X-ray diffraction showed that every composition, from pure SrMoO4 through the highest iron loading, crystallized in the single tetragonal scheelite phase with no secondary impurities, and Rietveld refinement verified that the lattice remained intact with high goodness-of-fit values. Interestingly, the lattice parameters increased slightly with iron content rather than contracting, even though iron ions are much smaller than strontium. The authors attribute this to local structural relaxation, a mixed iron valence state, and charge-compensation effects within the crystal. Fourier-transform infrared spectroscopy supported the same conclusion, showing the characteristic molybdate vibrations but no separate iron oxide signals, which would have indicated undissolved impurity phases.

Where the chemistry gets truly interesting is in the electronic structure. X-ray photoelectron spectroscopy of the optimal composition, Sr0.8Fe0.2MoO4, revealed a mixed-valence world at the surface: iron present as both Fe2+ and Fe3+, and molybdenum coexisting as Mo5+ and Mo6+. This charge redistribution, together with iron-induced oxygen vacancies, introduces new energy levels inside the wide band gap of the host. The result is dramatic in the optical data. The undoped compound absorbs only in the ultraviolet, through a ligand-to-metal charge-transfer transition within the molybdate tetrahedra, and its band gap of 4.22 electronvolts falls steadily to 1.87 electronvolts at the highest iron loading. The new absorption channels include oxygen-to-iron charge transfer, iron d-d transitions, and intervalence charge transfer between neighboring iron ions, all of which soak up visible light and paint the powder in warm brown tones.

Color measurements in the CIE Lab* system traced this evolution quantitatively. The pure compound is nearly white, with a lightness value of 93.67. Iron doping drags the lightness down to about 45 at the highest loading while pushing the red and yellow coordinates positive, and the hue angle slides from 63.6 to 44.5 degrees, crossing into the 40-to-60-degree window that defines true reddish-brown shades. Crucially, the near-infrared solar reflectance, calculated against the ASTM G173-03 standard solar spectrum, stayed impressively high: 82.4 percent for the undoped host, 70.0 percent at ten percent iron, and 69.8 percent at the optimal twenty percent loading, only falling to 58.4 percent at the heaviest doping. That trade-off between color intensity and reflectance is the central challenge of cool pigment design, and the molybdate framework held the line better than most alternatives.

The comparison with published reddish-brown pigments makes this performance striking. Lanthanum ferrite and related orthoferrite pigments, typically fired at 1200 degrees Celsius, manage reflectances of only 33 to 43 percent. Bismuth ferrite variants reach about 52 percent, iron-substituted yttrium aluminate 45 percent, and cerium-praseodymium oxide 58 percent. The iron-doped strontium molybdate made here at 180 degrees Celsius beats them all, delivering comparable color with 60 to 70 percent reflectance. The authors credit the molybdate lattice itself, which retains its near-infrared reflectivity even after iron is introduced, effectively decoupling the visible coloration from the thermal performance.

Real-world testing put the numbers to work. In a laboratory setup, metal sheets coated with a PVDF-based paint containing the Sr0.8Fe0.2MoO4 pigment were irradiated with a 250-watt infrared lamp thirty centimeters away. The bare sheet climbed from 31.8 to a scorching 62.3 degrees Celsius within ten minutes, while the coated sheet peaked at 57.1 degrees, and the coated surface stayed roughly 3.6 degrees cooler on average over an hour of continuous irradiation. Under genuine outdoor sunlight on the Vellore campus in early September, with ambient temperatures near 37 degrees and solar irradiance approaching 1000 watts per square meter, the pigment-coated sheet ran about 4 to 5 degrees cooler than bare metal, and a version layered over a white titanium dioxide base coat performed best of all, reaching 45.2 degrees when the bare surface hit 61.2 degrees. Commercial iron oxide paint, by contrast, provided only about half that benefit.

The pigment also passed the durability gauntlet that determines whether a laboratory curiosity can become a commercial coating. Thermogravimetric analysis showed only about 6 percent weight loss up to 800 degrees Celsius, with the crystal structure surviving intact, and exposure to dilute hydrochloric acid, nitric acid, and sodium hydroxide produced total color differences between 1.84 and 4.96, all within the accepted threshold of 5 for chemical resistance. Taken together, the results sketch a coherent recipe for the next generation of cool coatings: a benign, iron-based chromophore locked inside a highly reflective molybdate framework, manufactured at low temperature, and capable of shaving several degrees off the surfaces that bake under the sun. For a warming world covered in dark roofs, that modest temperature difference, multiplied across entire cities, could translate into meaningful savings in cooling energy and a measurable dent in the urban heat island effect.

Subject of Research: Iron-doped SrMoO4 near-infrared reflective cool pigments for energy-efficient coatings

Article Title: Design of reddish-brown iron-doped SrMoO 4 pigments with high NIR reflectance for cool coating applications

Article References: Metha, A., & Sumathi, S. (2026). Design of reddish-brown iron-doped SrMoO4 pigments with high NIR reflectance for cool coating applications. Results in Chemistry, 31, Article 103958. https://doi.org/10.1016/j.rechem.2026.103958

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103958

Keywords: cool pigments, near-infrared reflectance, strontium molybdate, iron doping, urban heat island, scheelite structure, hydrothermal synthesis, passive cooling, inorganic pigments, band gap engineering, thermal management, PVDF coatings

The analysis, a narrative review synthesizing peer-reviewed literature, technical reports, and industry roadmaps, builds a coupled techno-economic model called H2ET that links aircraft design parameters such as specific impulse, lift-to-drag ratio, and mass fractions with hydrogen infrastructure economics, including production cost, liquefaction energy, transport logistics, and airport storage. Liquid hydrogen emerges as the indispensable enabler. Its gravimetric energy density of 120 to 142 megajoules per kilogram dwarfs the 43 to 44 MJ/kg of kerosene, and its unique thermodynamic properties, including a boiling point of 20.3 kelvin and a liquid heat capacity of 14.3 kJ per kilogram per kelvin, make it the only realistic coolant for air-breathing hypersonic propulsion. Methane, ammonia, and synthetic kerosene all fall short on specific impulse or combust at speeds and temperatures incompatible with scramjet operation.

The reference vehicle at the heart of the study is a notional 100-passenger transport cruising at Mach 8 over 10,000 kilometers, with a takeoff mass of 200 tons, 60 tons of which is liquid hydrogen. Because liquid hydrogen’s density is only about 71 kilograms per cubic meter, that fuel requires roughly 847 cubic meters of tank volume, approximately the volume of two Boeing 747 fuselages. Fuel is loaded at 30 percent of takeoff mass, and each mission burns about 55 tons when the cooling penalty is included. A fleet of 100 such aircraft flying one daily transpacific mission would consume 1.65 million tons of liquid hydrogen per year, a figure that anchors all of the study’s infrastructure estimates.

Propulsion remains a formidable technical challenge even if it is not the binding one. The study identifies turbine-based combined cycle propulsion, which integrates a turbojet, a ramjet, and a scramjet into a single engine sharing common inlet and nozzle, as the most realistic architecture for civil hypersonic flight. Rocket-based alternatives would need four to five times more fuel, and pure scramjets cannot generate takeoff thrust. The critical difficulty is mode transition between Mach 3 and Mach 5, when neither engine cycle is optimal. Specific impulse drops into a valley of 800 to 1,200 seconds during this handover, inflating total mission fuel consumption by 15 to 25 percent relative to an ideal engine. No integrated TBCC flight test has ever been completed, and the technology stands at a readiness level of roughly 4 to 5.

Thermal management adds a second layer of coupling between the vehicle and the fuel supply. At Mach 8 cruise, stagnation-point gas temperatures reach roughly 4,000 to 4,500 degrees Celsius, and the total heat load on the airframe can approach 50 to 100 megawatts. Hydrogen flowing at 0.5 to 2.0 kilograms per second through microchannels and regenerative cooling circuits absorbs this heat, but hydrogen used as coolant is not always available for propulsion, creating a mismatch that can force overboard venting during transition phases. The cooling penalty alone adds 10 to 30 percent to mission hydrogen demand, and the study warns that airports that size storage on propulsion figures alone will under-design liquefaction capacity by a similar margin. Recent materials research, including thermal armor structures that suppress the Leidenfrost effect and have withstood simulated heating above 3,000 degrees Celsius, offers promising complements to conventional regenerative cooling.

Adaptive morphing aerodynamics constitutes the third technical pillar, and the analysis treats it as an energy-efficiency technology rather than a refinement. A vehicle must be short-spanned, highly swept, and sharp-edged for hypersonic cruise yet broad-winged and flap-equipped for subsonic takeoff and landing. Telescoping wings, variable sweep mechanisms, shape-memory alloys, and ceramic matrix composites could allow a single airframe to optimize lift-to-drag ratio across the entire envelope. Because hydrogen fuel represents 70 to 90 percent of direct operating costs, aerodynamic improvements translate almost directly into lower infrastructure demand. The study cautions, however, that a lift-to-drag ratio of 6 at hypersonic cruise is near the practical limit for volume-constrained designs, and that second-order effects reduce realistic fuel savings from a theoretical 33 percent to roughly 10 to 15 percent.

The binding constraint, the study concludes, lies on the ground. Delivering green hydrogen at the airport hydrant for 2 to 3 euros per kilogram by 2035 to 2040 is the threshold at which the levelized cost of travel falls below 0.60 euros per passenger-kilometer. Today green hydrogen costs 4 to 8 euros per kilogram, and at 6 euros per kilogram the levelized cost of a 10,000-kilometer hypersonic trip would be about 0.75 euros per passenger-kilometer, nearly four times the 0.20 euros of subsonic business class on kerosene. Supporting a 100-aircraft fleet requires 15 to 20 gigawatts of electrolysis capacity, 10 to 20 billion euros in electrolyzer capital expenditure, liquefaction plants consuming roughly 22 terawatt-hours per year, and 20 hub airports equipped with cryogenic storage, hydrant systems, and hydrogen safety infrastructure. In a striking inversion of traditional aerospace economics, total infrastructure investment of 12 to 27 billion euros rivals or exceeds the cost of developing and buying the aircraft themselves.

The environmental ledger offers cautious optimism with important caveats. Under near-zero-carbon electricity and hydrogen leakage below 2 percent, well-to-wake emissions for green-hydrogen hypersonic travel range from 150 to 350 grams of carbon dioxide equivalent per passenger-kilometer, a 70 to 80 percent reduction compared with subsonic kerosene operations. But combustion at altitudes of 30 to 40 kilometers releases water vapor and nitrogen oxides whose climate effects are poorly understood. Preliminary modeling suggests that a fleet of 500 hypersonic aircraft could increase stratospheric water vapor by 10 to 30 percent, and the study estimates that non-carbon-dioxide effects could erode 20 to 50 percent of the carbon benefit. High-speed rail, at 20 to 40 grams per passenger-kilometer, remains far superior for continental distances, leaving hypersonic travel a niche for intercontinental routes where rail is infeasible.

Lessons from today’s hydrogen aviation programs reinforce the infrastructure argument. Airbus has pushed back its ZEROe timeline, acknowledging that hydrogen production, distribution, and regulatory readiness lag aircraft technology. Universal Hydrogen filed for bankruptcy in 2024 despite a technically viable modular capsule system, undone by airport unreadiness and hydrogen supply costs. H2Fly’s successful 2023 demonstration flight depended on mobile refueling rather than airport infrastructure. If regional hydrogen aviation struggles at these scales, hypersonic aircraft consuming 50 to 100 tons of liquid hydrogen per flight face the same barriers amplified by an order of magnitude, and the coordination failure between fuel producers, airlines, and airports appears to demand government intervention through advance market commitments, public-private partnerships, and green hydrogen mandates.

The study therefore proposes a staged pathway rather than a single leap: small demonstrators between 2030 and 2035, niche overwater services with 10 to 30 aircraft around 2038 to 2042, early commercial operations with 50 to 100 aircraft by 2045 to 2050, and a mature network beyond 2050, with total system investment potentially reaching 100 to 250 billion euros. Sonic boom regulation effectively confines early operations to transoceanic routes connecting roughly 20 to 30 coastal city pairs. The overarching message is that progress must be concurrent, not sequential, across propulsion, cryogenic infrastructure, certification standards, and hydrogen production. Hypersonic travel is technically plausible, the analysis concludes, but only if the hydrogen economy and the aircraft advance in lockstep, with immediate and sustained cross-sector investment treating the endeavor as a single energy-transport system from the outset.

Subject of Research: Techno-economic viability of liquid-hydrogen-powered commercial hypersonic point-to-point air travel

Article Title: A systems-level analysis of energy, infrastructure, and economic viability of commercial hypersonic point-to-point (P2P)

Article References: Boretti, A. (2026). A systems-level analysis of energy, infrastructure, and economic viability of commercial hypersonic point-to-point (P2P). Frontiers in Aerospace Engineering, 5, Article 1946082. https://doi.org/10.3389/fpace.2026.1946082

Image Credits: AI Generated

DOI: 10.3389/fpace.2026.1946082

Keywords: hypersonic flight, green hydrogen, liquid hydrogen, TBCC propulsion, techno-economic analysis, aviation infrastructure, point-to-point travel, lifecycle assessment, morphing aerodynamics, thermal management, sustainable aviation, electrolysis capacity

The study, published in the journal Advanced Composites and Hybrid Materials, describes a porous composite membrane made from hexagonal boron nitride (hBN) nanoplatelets embedded in polydimethylsiloxane (PDMS), the soft silicone elastomer that dominates skin-wearable design. The key innovation lies in how the pores are created. Instead of templating, washing, solvent extraction, or any post-fabrication treatment, the researchers mixed a volatile liquid, perfluorohexane (PFH), directly into the hBN/PDMS formulation. As the membrane was cast and cured, the PFH evaporated on its own, leaving behind a stable network of air voids. The porous architecture emerges during curing itself, which means the process can in principle run as a continuous, one-step casting operation without extra processing stages that add cost and complexity.

What makes the approach more than a convenience is the degree of control it offers. The team found that pore size could be tuned simply by changing the curing temperature, because temperature governs a kinetic race between two competing processes: the solidification of the PDMS matrix and the volatilization of the PFH porogen. When curing outpaces evaporation, the growing polymer locks in a particular pore morphology; when evaporation dominates, the voids evolve differently. By adjusting this balance, the researchers could dial in the porous structure without changing the chemistry of the formulation. Meanwhile, the overall porosity of the membrane rose from roughly 15 percent to about 31 percent as the PFH content in the mixture increased, giving a second, independent handle on the material’s internal architecture.

The central surprise of the work is thermal. Porosity normally kills thermal conductivity, since air-filled voids interrupt the paths along which heat travels. Yet the porous hBN/PDMS membrane achieved a thermal conductivity of 0.22 watts per meter-kelvin, essentially matching the dense form of the same composite. The explanation lies in how the hBN nanoplatelets arrange themselves during fabrication. Rather than being distributed randomly through the bulk, the platelets assemble along the walls of the pores, forming continuous heat-conduction pathways that thread through the material. Heat entering the membrane finds an interconnected skeleton of thermally conductive boron nitride to follow, even as the voids between those pathways remain open for water vapor transport. Breathability and heat dissipation, usually opposing goals, are satisfied simultaneously by the same microstructure.

To understand this behavior in more detail, the researchers turned to computational fluid dynamics simulations. The modeling showed that heat is transferred more effectively near the interface between the hBN and the PDMS matrix in the porous membrane than in comparable dense structures. That computational picture matched what the team observed experimentally: the porous membrane accumulated less heat during use. In a skin-interface application, lower heat accumulation translates directly into less thermal stress on the tissue beneath, which matters both for user comfort and for the accuracy of sensors that measure physiological signals through the skin.

Thermal performance alone would not make a material suitable for wear. The team therefore evaluated the membrane in contact with living skin. In vivo analysis showed that the porous membrane kept skin hydration at a stable level of approximately 103 to 107 percent over the testing period, indicating that the material does not trap moisture against the skin or dry it out. Hydration stability is a meaningful benchmark for wearables, because occlusive films that block vapor transport cause the skin to macerate over time, degrading both comfort and measurement quality. A membrane that holds hydration in a narrow, healthy band addresses one of the most common complaints about long-duration skin patches.

Mechanical compliance proved equally convincing. The porous membrane’s mechanical behavior was found to be close to that of bare skin, meaning the material deforms in a way that the body does not register as a stiff foreign object. This is where the choice of PDMS as the matrix pays off: the elastomer is soft, stretchable, and biocompatible, and introducing controlled porosity pushes its effective stiffness even lower. Combined with the hydration results, the mechanical data suggest that the membrane satisfies both the physiological and mechanical criteria for comfortable, extended skin contact, which the authors describe as the foundation for future skin-interface applications.

The significance of the work extends beyond a single material recipe. Hexagonal boron nitride is prized in electronics for its thermal conductivity, electrical insulation, and chemical stability, and pairing it with PDMS is a natural strategy for thermally managed wearables. What has been missing is a fabrication route that preserves those thermal benefits while adding breathability without extra processing. By using a volatile porogen that does its job and disappears during curing, the researchers have effectively turned the manufacturing step itself into the design tool. The pore structure, the porosity, and the platelet alignment all emerge from the interplay of curing kinetics and evaporation, parameters that are easy to adjust on a production line.

The study was a collaboration between the Research and Innovation Center of Amorepacific Corporation in Yongin and the School of Mechanical Engineering at Soongsil University in Seoul, combining industrial materials expertise with mechanical engineering analysis. The work was supported by South Korea’s National Research Foundation through its Bio and Medical Technology Development Program and the G-LAMP Program, as well as by the Ministry of Trade, Industry and Energy through an Industrial Technology Innovation Program grant. The involvement of a major cosmetics and personal care company hints at near-term applications in skin patches, cosmetic films, and health-monitoring wearables, where thermal comfort and moisture management directly affect product performance.

For the wearable industry, the message is that the long-standing trade-off between breathability and thermal management may be an artifact of how porous materials have been made, not a law of physics. If thermally conductive fillers can be guided to line the pore walls of a soft elastomer, air voids no longer have to be thermal dead ends. The one-step PFH porogen method offers a scalable route to that architecture, with tunable pores, no washing or extraction stages, and demonstrated comfort on living skin. As skin-interfaced sensors, electronic skins, and on-body therapeutic devices multiply, materials like this porous hBN/PDMS membrane could determine whether the next generation of wearables is something people actually forget they are wearing.

Subject of Research: One-step fabrication of porous hexagonal boron nitride/PDMS composite membranes for breathable, thermally dissipative skin-interface wearable applications

Article Title: One-step fabrication of a porous hBN/PDMS composite membrane for breathable and thermally dissipative skin-interface applications

Article References: Lee, J. Y., Lee, S. K., Nam, J., & Hong, J. (2026). One-step fabrication of a porous hBN/PDMS composite membrane for breathable and thermally dissipative skin-interface applications. Advanced Composites and Hybrid Materials. https://doi.org/10.1007/s42114-026-02120-y

Image Credits: AI Generated

DOI: 10.1007/s42114-026-02120-y

Keywords: hexagonal boron nitride, PDMS, porous membrane, thermal management, breathability, wearable electronics, skin-interface materials, perfluorohexane porogen, thermal conductivity, skin hydration, mechanical compliance, computational fluid dynamics