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 clinical motivation behind the study is stark. Acute ischemic stroke accounts for roughly 70 percent of all strokes and remains a leading cause of death and long-term disability worldwide. The dynamic behavior of cerebral blood flow directly reflects disturbances in cerebral energy metabolism and the extent of neuronal injury, making it a key indicator of stroke progression. Accurate, real-time assessment of blood flow to the brain allows clinicians to track the onset and evolution of ischemia, optimize treatment decisions, and improve prognostic evaluation. Yet the tools currently available for this task are poorly suited to continuous bedside monitoring.

Computed tomography, magnetic resonance imaging, and positron emission tomography provide high spatial resolution and quantitative perfusion information, but their high cost, limited availability, and long acquisition cycles make them impractical for real-time or continuous monitoring. Transcranial Doppler ultrasound offers dynamic measurement but is largely restricted to flow velocity in major cerebral arteries, while near-infrared spectroscopy is constrained by optical attenuation and struggles to reflect deep or whole-brain perfusion. Near-field coupling technology has recently attracted attention as an alternative: it is noninvasive, noncontact, and capable of continuous real-time monitoring. Previous studies have demonstrated its feasibility for detecting intracranial conditions such as cerebral hemorrhage, edema, and ischemic stroke.

The research group behind the new framework had previously developed a wearable NFC-based cerebral blood flow monitoring device capable of simultaneously capturing perfusion changes in both large and small intracranial vessels, validated in healthy volunteers and in patients with cerebrovascular diseases including subarachnoid hemorrhage. Their earlier tilt-based work showed that NFC could characterize dynamic cerebral autoregulation under controlled postural perturbation. But a persistent challenge emerged: under the same tilt paradigm, NFC responses exhibited two distinct trends across individuals, consistent with the substantial inter-individual variability in cerebral perfusion reported across different populations and physiological states. Such variability threatened to undermine the generalizability of any assessment based on cerebral blood flow signals alone.

The solution the team proposed is physiologically elegant. Acute ischemic stroke not only compromises cerebral perfusion but may also involve impaired cerebral autoregulation, and it can precipitate cardiac arrhythmias, reduced heart rate variability, and other forms of cardiac dysfunction. In early ischemic stroke, altered heart rate variability and baroreflex sensitivity have been linked to impaired dynamic cerebral autoregulation and larger infarct volumes. Recent work on brain-heart interactions in stroke emphasizes that cerebrovascular impairment and cardiovascular dysregulation should be considered interdependent processes rather than isolated events. Cardio-cerebral coupling is mediated through interacting hemodynamic, neural, and humoral pathways: postural or pathological changes in venous return, cardiac output, and arterial pressure activate baroreflex-mediated adjustments in heart rate and rhythm, while cerebral autoregulation modifies cerebrovascular tone through myogenic, neurogenic, metabolic, and endothelial mechanisms. Humoral factors including catecholamines, the renin-angiotensin system, and vasopressin further regulate vascular tone and circulating volume over longer time scales.

By incorporating the cardiac regulatory context that the electrocardiogram provides, the researchers aimed to resolve the ambiguity introduced by inter-individual variability in cerebral blood flow responses. The resulting framework consists of two collaborative modules: a Multi-Scale Feature Extractor, or MSFE, and a Depthwise-Separable Cross-Attention Fusion module, or DSCAF. The MSFE employs modality-specific multi-scale convolutional branches with kernel sizes tailored to the temporal characteristics of each signal. The ECG branch uses short kernels of 32 and long kernels of 256 to capture QRS morphologies and RR-interval rhythms, while the NFC-CBF branch uses kernels of 8 and 32 to model impedance peak-valley details and inter-beat fluctuations. Feature channels progressively increase from 128 to 256 to 512 as the network deepens, and Dropout is incorporated to mitigate overfitting. This dual-branch design functions as a physiological filter bank, addressing the limitation of single-scale convolutions that tend to overlook critical temporal structures.

The DSCAF module then integrates depthwise separable convolution with a dual-branch cross-attention mechanism to achieve deep semantic interaction and complementary fusion. Unlike a standard Cross-Transformer that relies on full attention operations over high-dimensional token representations, DSCAF constructs two cross-attention pathways using depthwise-separable projections, a shared bimodal query, and modality-specific key-value representations. This design reduces computational redundancy while preserving bidirectional interactions between the ECG and NFC-CBF streams. Depthwise separable convolution independently extracts local temporal features before integrating them across channels via pointwise convolution, significantly lowering computational complexity for high-dimensional feature maps. Element-wise weighted reconstruction and cross-modal residual connections within each branch produce a unified bimodal representation that explicitly models cross-modal dependencies.

To test the framework, the team built a synchronized monitoring platform combining an NFC-CBF module with an ECG module. The NFC-CBF module used a dual-layer planar spiral electromagnetic coupling sensor, a capacitive digital sensor to convert NFC sensing outputs into a resonant-frequency signal, and a microcontroller that acquired the signal at an effective output rate of 18 Hz before wireless transmission to a host PC. The ECG module recorded signals at a sampling rate of 144 Hz. Data were collected from 20 healthy male volunteers, divided into a young cohort aged 18 to 25 years and a middle-aged cohort aged 35 to 55 years, under two postural conditions: supine and 30-degree head-down tilt. Participants rested for one minute before recording, then simultaneous ECG and NFC-CBF signals were recorded for three minutes supine and four minutes tilted. All participants had no history of cardiovascular, cerebrovascular, or neurological disorders and refrained from caffeine and medication for 24 hours before the experiment.

Preprocessing pipelines suppressed baseline drift and noise in both modalities. For ECG, Butterworth filtering combined with a moving-average kernel removed baseline fluctuations while preserving QRS complex and T wave morphology. For NFC-CBF, wavelet threshold denoising using a hybrid soft- and hard-threshold strategy retained the primary peak and dicrotic wave components with maximal noise suppression. The preprocessed recordings were segmented into non-overlapping 10-second windows, yielding 840 samples for deep learning. Subject-level five-fold cross-validation ensured that all segments from the same participant remained within the same fold, with Z-score parameters calculated from training data only. Two classification tasks were designed: a binary task distinguishing supine from tilted states, and a four-class task jointly distinguishing age group and posture.

The results were striking. In the binary task, the proposed framework achieved an accuracy of 95.95 percent, outperforming ResNet-SE-LSTM at 93.81 percent, the Cross-Transformer Network at 81.07 percent, and Bimodal Feature Fusion at 66.19 percent. The corresponding F1-score and recall were 0.95 and 0.96 respectively, with an area under the ROC curve of 0.973. In the four-class task, the framework reached 79.29 percent accuracy, outperforming the strongest baseline by approximately 3.4 percentage points, with class-wise AUC values ranging from 0.867 to 0.922 and a macro-average of 0.890. Fold-wise paired statistical comparisons confirmed that all improvements over baseline models were statistically significant after Holm correction. Ablation experiments revealed that NFC-CBF alone achieved 92.74 percent accuracy in the binary task while ECG alone reached only 64.76 percent, yet adding the ECG stream improved accuracy over NFC-CBF alone by 3.2 percentage points in the binary task and 11.9 percentage points in the four-class task, supporting the complementary value of cardiac-rhythm information, particularly when posture and age had to be represented simultaneously.

Integrated Gradients attribution analysis complemented these findings at the individual-prediction level. In a representative binary example, NFC-CBF accounted for 68.8 percent of total absolute attribution while ECG accounted for 31.2 percent, consistent with its role as the primary source of cerebral perfusion-related information. In the four-class example, attribution was more evenly distributed at 43.1 percent for ECG and 56.9 percent for NFC-CBF. Simultaneously masking the time points with the highest 10 percent absolute attribution within both modalities reduced the true-class probability by 0.752 and 0.675 in the binary and four-class examples respectively, confirming that temporally localized information from both signals contributed to the predictions. The DSCAF module itself outperformed conventional Add, Multiply, and Concat fusion strategies, achieving improvements of approximately 1.9 and 4.4 percentage points over the best conventional strategy in the binary and four-class tasks respectively, with almost no change in parameter count or model size and an increase of less than 5 percent in training time per epoch.

The physiological interpretation of these results is compelling. Heart rate variability metrics derived from ECG showed a consistent pattern across age groups, with an initial increase after tilting followed by a gradual decline, indicating that tilt-induced cardiac modulation follows a reproducible regulatory trajectory independent of age. In contrast, NFC-CBF-derived parameters such as peak-to-valley distance and systolic slope exhibited substantial inter-individual variability, with some participants showing increases and others decreases after tilt. Younger participants, with greater vascular compliance, showed larger NFC-CBF amplitude variations and stronger phase synchrony between NFC-CBF waveforms and the ECG QRS complex than middle-aged participants. These heterogeneous responses are physiologically consistent with the multifactorial and individually variable regulation of cerebral blood flow during postural perturbation, and they reinforce why relying solely on cerebral blood flow signals may be insufficient for robust assessment across individuals.

The authors are careful to frame the work as proof of concept rather than clinical validation. The sample size was relatively small at 20 participants, all male, and the cohort did not include pathological populations or a broader range of ages. BMI was significantly higher in the middle-aged group, and its potential contribution to the age-group comparison cannot be excluded. Concurrent transcranial Doppler, near-infrared spectroscopy, or arterial blood-pressure measurements were not acquired, so participant-level correspondence between NFC-CBF responses and independent cerebrovascular references could not be evaluated. Acute ischemic stroke represents a more complex and dynamically evolving pathological setting in which cerebral autoregulation, autonomic regulation, vascular compliance, and cardiac function may undergo time-dependent alterations during post-stroke progression, potentially reshaping the temporal coupling between cardiac rhythm and cerebral perfusion. Future studies will need to validate the framework in larger, more diverse, and demographically balanced cohorts including patients at different stages after acute ischemic stroke, incorporate synchronous reference measurements, and pursue hardware-specific evaluation and lightweight optimization before practical deployment on wearable or edge devices.

Even within these constraints, the study establishes a methodological foundation for a new generation of perfusion monitoring. By embedding cardiac rhythm information and cerebral perfusion-related features into a shared learnable representation space, the bimodal framework provides a physiologically grounded functional representation of cardio-cerebral coupling that neither signal could achieve alone. The approach moves beyond handcrafted time-domain, frequency-domain, or time-frequency features that may be insufficient to capture nonlinear inter-modal relationships and complex temporal dependencies in coordinated cardiovascular and cerebrovascular responses. As wearable biosensors continue to advance and deep learning architectures grow more attuned to the heterogeneous temporal characteristics of physiological signals, the prospect of continuous, noninvasive, real-time cerebral perfusion assessment at the bedside moves closer to reality. For a disease that strikes every few seconds somewhere in the world, that prospect carries enormous weight.

Subject of Research: A bimodal deep learning framework for cerebral perfusion assessment using synchronous ECG and near-field coupled CBF signals

Article Title: A bimodal deep learning framework for cerebral perfusion assessment using synchronous ECG and near-field coupled CBF signals

Article References: Wu, S., Wang, J., Wan, C., Li, G., Wang, Z., Li, J., Ning, L., Liu, S., Zhong, J., Luo, J., Yang, S., Jiang, B., Liu, S., Chen, J., Qiao, L., & Chen, Y. (2026). A bimodal deep learning framework for cerebral perfusion assessment using synchronous ECG and near-field coupled CBF signals. Frontiers in Bioengineering and Biotechnology, 14, Article 1912951. https://doi.org/10.3389/fbioe.2026.1912951

Image Credits: AI Generated

DOI: 10.3389/fbioe.2026.1912951

Keywords: bimodal, deep, learning, framework, cerebral, perfusion, assessment, synchronous, near-field, coupled, signals, scientific research

The research team, led by Adnan M. Taha and including Mahdi Ghafourivayghan, Konstantin Burlakov, Sergey Shabunin, and Mohammad Alibakhshikenari, set out to address a persistent bottleneck in computational electromagnetics. Commercial full-wave solvers such as ANSYS HFSS, CST, and FEKO discretize an entire three-dimensional volume into a finite-element mesh, and their cost balloons when reflected waves must be tracked near a boundary. When the antenna operates in its near or intermediate zone, the amplitude and phase of the field cannot be inferred from a simple aperture distribution; every field component must be computed numerically, which dramatically inflates processing time and memory demands. For applications like radar sensors, subsurface radiolocation, and level gauges, where the far-zone condition is simply never met, this is more than an inconvenience.

The core of the new method is the Green’s tensor function, a mathematical object that encodes how an infinitesimal electric or magnetic current source radiates through a specified environment. In unbounded homogeneous space, the Green function takes a comparatively simple form. In a layered medium, however, it must absorb the thickness, number, and electromagnetic parameters of every layer, including permittivity, permeability, and conductivity. The authors build these properties into characteristic functions g(z, z’) and f(z, z’), which solve Sturm-Liouville-type differential equations and automatically satisfy all boundary conditions at each interface. Crucially, the modal conductances are recalculated recursively through the stack using an equivalent transmission-line model, in which spectral components of the field are mapped onto voltages and currents in equivalent circuits. Once this analytical groundwork is laid, the field can be evaluated only where it is actually needed, rather than everywhere in space.

To model a real aperture antenna, the team begins with the Huygens element, a pair of orthogonally crossed electric and magnetic dipoles that together reproduce the radiation of an elementary patch of wavefront. The electric field is obtained by integrating the external electric and magnetic currents over the source region, weighted by the appropriate Green’s tensor components. Because a Huygens element radiates a curved, non-uniform wavefront rather than a plane wave, its reflection from a nearby interface behaves nothing like the textbook plane-wave diffraction problem, and the Green’s-function framework captures this distinction exactly. The formulation also yields closed-form far-zone expressions via the saddle-point method, providing a convenient analytical check: the radial field component vanishes, and the radiation maximum points toward the interface, exactly as physics demands.

The extension from the elementary Huygens source to a practical pyramidal horn is where the study claims its principal advance over the authors’ earlier work. The horn flares in both principal planes, combining the properties of E-plane and H-plane sectoral horns, and its aperture field carries a quadratic phase variation that reflects the true curvature of the wavefront. The researchers derive a rigorous aperture integration in which the path-length difference across the aperture is expanded binomially, and the resulting integrals are evaluated in terms of cosine and sine Fresnel integrals. This captures interference structure near the reflector that a simple Huygens source fundamentally cannot represent, making the tool genuinely design-ready rather than a purely theoretical exercise.

Numerical stability is handled through a documented three-region spectral truncation strategy. The integration domain in wave-number space is split into an evanescent region of exponentially decaying near-field contributions, a narrow singularity gap around the branch point where split-path integration is applied, and a propagating region of oscillatory far-field radiation. Convergence analysis shows the truncation error decays exponentially with the upper limit, achieving one percent accuracy well within the adopted defaults, while the singularity treatment remains stable for gap sizes down to ten to the minus twenty-fifth. A parametric robustness study across horn heights from half a wavelength to two wavelengths, frequencies from 0.5 to 3 gigahertz, and substrate permittivities from 1 to 10 confirms that the chosen settings sit on a stable plateau and need no geometry-specific retuning.

The validation against ANSYS HFSS is the study’s most persuasive evidence. On identical observation grids, the proposed method required 30.9 seconds of wall-clock time against 88 seconds for HFSS, a roughly threefold speed-up, and used 200 megabytes of memory against 749 megabytes, a nearly fourfold reduction. The speed advantage stems from the region-of-interest strategy: the spectral integral is evaluated only at the user-specified observation points, whereas the finite-element solver must discretize the entire computational volume. Error budgets quantify the agreement precisely. The L2-norm relative error for the electric field magnitude is 2.78 percent, with maximum deviation of 2.29 percent, while the phase component shows an L2 error of 4.50 percent, a figure the authors attribute to the notorious sensitivity of phase near field nulls.

Even more telling is how the error scales with antenna height above the interface. At a separation of half a wavelength, deep in the reactive near field, the L2 error against HFSS is about 2.6 to 2.9 percent. At three-quarters of a wavelength it drops to roughly 0.65 percent, and at one and a half wavelengths it falls below 0.01 percent as the observation domain moves into the Fresnel region. Notably, the error shows only weak dependence on frequency and substrate permittivity, with higher permittivity actually marginally reducing the error at close separations because stronger field confinement diminishes the relative weight of the evanescent spectral tail. For radar-sensor and level-gauge designers, these numbers suggest the method can be trusted across a decade of bandwidth without recalibration.

The comparison also exposed an interesting divergence between the two approaches at a perfect electric conductor boundary, where the Green’s-function method delivered cleaner wave patterns than the finite-element reference. The authors point out that HFSS results depend on mesh symmetry and on the correct selection of the bounding box around the analyzed object; an asymmetric mesh around a structure symmetric with respect to the reference plane can introduce spurious field asymmetry. The analytical method, by contrast, is electrodynamically exact and automatically enforces all boundary conditions, eliminating such numerical artifacts. In benchmark runs over a dielectric boundary, the conventional solver took roughly twice as long at a comparable spatial step.

The authors are careful to delineate the method’s limits. Its computational advantage is specific to planar, stratified reflecting surfaces; for finite-sized, curved, or laterally inhomogeneous reflectors, edge diffraction and geometric scattering fall outside the stratified-media kernel, and full-wave solvers remain the appropriate tools. Substrates containing vias, patches, or etched patterns would require augmenting the transmission-line recursion with periodic or moment-method treatments. Within its domain, however, the framework offers a unified and efficient foundation that extends naturally to printed and slot antennas, transmitarrays, and reflectarrays, whose multiple functional layers are handled natively by stratified-media Green’s functions. As near-field applications multiply, from high-resolution electromagnetic imaging and secure short-range communication to over-the-air testing of 5G and 6G arrays, tools that trade brute-force meshing for analytical rigor are likely to become indispensable to the antenna community.

Subject of Research: Electromagnetic near-field analysis of pyramidal horn antennas above layered reflecting media using dyadic Green's functions

Article Title: Analysis of pyramidal horn antennas near a reflecting surface using Green’s functions

Article References: Taha, A. M., Ghafourivayghan, M., Burlakov, K., Shabunin, S., & Alibakhshikenari, M. (2026). Analysis of pyramidal horn antennas near a reflecting surface using Green’s functions. Results in Engineering, 32, Article 113033. https://doi.org/10.1016/j.rineng.2026.113033

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113033

Keywords: pyramidal horn antenna, Green's functions, near-field, reflecting surface, stratified media, ANSYS HFSS, electromagnetic modeling, radar sensors, spectral integration, computational electromagnetics, 5G, aperture antennas