Physical Sciences

Abstract: Standard pushers for the relativistic equations of motion of a charged particle in an electromagnetic field—Boris, Vay, Higuera–Cary—do not, in general, preserve the full symplectic structure of the underlying Hamiltonian system, while high-order non-symplectic schemes such as Runge–Kutta accumulate secular error over long times. We propose a symmetry-preserving physics-informed neural network framework (SP-PINN) for the 3+1-dimensional relativistic dynamics of a charged particle in a prescribed field, including a focused Gaussian laser pulse. The method is two-stage: an unsupervised physics-informed neural network learns a surrogate relativistic Hamiltonian from the covariant equations of motion using a Lorentz-invariant loss that enforces the mass-shell constraint H=mc²γ; the surrogate is then advanced with an explicit symplectic map built on Tao’s extended phase space, valid for the non-separable relativistic Hamiltonian. We benchmark against the Boris pusher and Runge–Kutta on three test problems. The magnetic-field test illustrates the contrast between bounded and secular error growth: Runge–Kutta drifts secularly, the Boris pusher conserves the invariants to machine precision as a volume-preserving gyro-integrator, and the symplectic map keeps the error bounded for all time; on a non-integrable magnetic trap, where no exact volume-preserving rotation exists, the symplectic map alone keeps the energy error bounded. The learned surrogate is the current accuracy bottleneck; for the demanding laser case a vector-potential light-cone reformulation reduces its error to (3.0±0.1)×10⁻⁴ (three seeds) and yields learned trajectories that remain phase-coherent over essentially the whole interaction. The framework targets laser–plasma acceleration, synchrotron-radiation modeling, and particle tracking.

Abstract: The Haber-Bosch process dominates industrial ammonia synthesis but incurs massive energy consumption and carbon emissions. Here, we demonstrate a catalyst-free approach for direct ammonia synthesis from atmospheric nitrogen and water under ambient temperature and pressure, leveraging ultra-fast laser-induced plasma at the gas-liquid interface. By optimizing irradiation parameters (irradiation time, pulse energy, number of beams) and implementing a concentric laser scanning strategy, we achieved a maximum ammonia concentration of 0.624 μmol/20mL. This method bypasses the need for high temperature/pressure or catalysts, offering a sustainable path for distributed ammonia production. Our work underscores the potential of strong optical fields in activating inert molecules like N₂ and H₂O, with implications for decarbonizing chemical synthesis.

Abstract: Early hidden cracks in circular magnetic encoder rings induce only slight magnetic perturbations at the incipient stage, yet they may evolve into missing-pole, demagnetization, or severe waveform-distortion faults that degrade angular-displacement measurement and closed-loop control. This study establishes a physical mapping between crack-induced magnetization nonuniformity, pole-pitch deviation, and the amplitude-modulated and frequency-modulated components of the measured magnetic signal, and models the encoder output as a compound frequency-modulated–amplitude-modulated waveform. A precision-controlled experimental platform equipped with a self-developed tunnel magnetoresistance read head, a precision rotary stage, multi-axis positioning stages, and laser displacement sensing was built to suppress eccentricity-related disturbances and disturbances related to sensor lift-off distance. An analysis workflow combining fast Fourier transform-based band-pass filtering, Hilbert demodulation, sixth-order Butterworth low-pass filtering, and coefficient-of-variation analysis was used to extract the instantaneous amplitude and instantaneous angular frequency. Experiments on intact, hidden-crack, and visible-crack states show that the proposed normalized indicators sensitively capture weak crack-related fluctuations, reduce sensitivity to sensor lift-off distance after normalization, and increase monotonically with damage severity. The method does not require a high-accuracy external reference and shows promise for online monitoring of circular magnetic encoder rings and related electromagnetic sensing elements.

Abstract: This study reported on the multi-objective optimization of atmospheric plasma spraying parameters for zirconia coatings by incorporating the response surface method (RSM) with the desirability method, while simultaneously enhancing microhardness and reducing wear rates. Experiments were achieved using a L18 orthogonal array in Taguchi-based design. An analysis of variance was used to identify the key process parameters, including accelerating power, stand-off distance, powder feed rate and carrier gas flow rate. By constructing regression models and plotting response surface contour plots, the relationship between processing parameters and coating properties was comparatively examined. Subsequently, the optimal window of parameters that meets the requirements for high hardness and excellent wear resistance was determined using the desirability-overlapped method. The influence of these parameters on the hardness of the coatings and the wear volume was analysed graphically through modelling, both individually and in interaction. Experimental results have shown that the experimental results show that the prediction error for hardness was only 1.84%, while the prediction error for wear volume was 3.27%. The validation results demonstrate a high degree of consistency, as evidenced by the striking similarity between the results, which clearly indicates the reliability of the prediction made by the model. In addition, the microstructure of the optimal coating exhibits complete fusion and fine grain size, with very few pores or cracks, while there was only minor pitting and small spalling, and most of the original sprayed structure was preserved in the worn areas. Clearly, by adopting a desirability-overlapped based on RSM by Taguchi’s design, the multi-response properties of the plasma-sprayed coatings were significantly improved, and these results met the expected values for maximum hardness and minimum wear volume in the coatings.

Abstract: Physical AI systems must respond to real-world events under strict constraints of time, energy and safety. This Perspective clarifies the distinct roles of latency, throughput, bandwidth and world models within a Reflex–Policy architecture and argues that increasingly powerful policy and world models do not eliminate the need for well-designed local reflex layers. On the contrary, reflex layers can dramatically reduce upstream data rates, preserve hard real-time safety, and allow policy models to focus on prediction, planning and rare events. A binary spintronic reflex crossbar has been demonstrated in proof-of-concept form for fast shadow bypass in photovoltaic systems and for battery cell balancing, confirming that non-volatile magnetic rule fabrics can perform sub-millisecond, parallel switching decisions in real physical domains. Multilevel spintronic devices align better with weighted inference on the policy side. Quantitative examples from robotics, automotive and energy systems illustrate how this layered approach can reduce communication bandwidth by 1–3 orders of magnitude while maintaining safety and efficiency. A comparison with existing real-time control technologies, safety microcontrollers, FPGAs and neuromorphic processors, clarifies where spintronic reflexes offer distinct advantages in standby power, non-volatility and inspectable rule logic.

Abstract: Raman spectroscopy combined with machine learning offers a rapid, label-free approach for bacterial identification, but robust translation remains challenged by spectral variability, biological heterogeneity, and limited model interpretability. Here, we present an integrated evaluation of an optimized Spectral Transformer (ST) framework for Raman-based bacterial classification benchmarked against a systematically optimized one-dimensional convolutional neural network (1D-CNN). The comparison was performed using a curated 36-class dataset comprising 15 Gram-negative bacterial entries, 15 Gram-positive bacterial entries, one non-bacterial microorganism, and five background/reference classes, enabling evaluation of both species-level and fine-grained bacterial classification. Under 15 dB noise-augmented evaluation, the ST achieved 80.6% ± 0.3% accuracy and a Matthews correlation coefficient (MCC) of 0.801 ± 0.003, outperforming the 1D-CNN baseline with 72.9% ± 0.3% accuracy andanMCCof0.721±0.003. Integrated Gradients analysis combined with attention map visualization enabled multi-level model interpretation, revealing that the ST’s improved robustness correlates with more bounded attribution patterns during misclassification, whereas the 1D-CNN’s feature attribution becomes scattered under noise perturbation. Importantly, this interpretability-driven analysis identified model-specific failure modes in the baseline architecture, including an over-reliance on non-specific spectral regions under noise, which can inform future data collection strategies and guide refinements to experimental protocols. These results demonstrate that attention-based spectral modeling improves Raman-based bacterial classification under noise-perturbed conditions while enabling multi-level interpretability that bridges model understanding with actionable feedback on experimental design and data quality requirements.

Abstract: Physical AI requires machines to sense, decide and act under tight constraints of energy, latency, safety and robustness. A fly escaping an approaching hand captures the core principle: a fast sensor-action reflex acts before full deliberation, while higher neural resources remain available for richer behavior. This Perspective proposes a layered Reflex-Policy architecture in which reflex layers execute fast, local, ADC-light actions near sensors and actuators, while policy layers perform slower learning, planning, optimization and rule updates. The two are mutually protective collaborators: policy layers define safe operating envelopes, while reflex layers shield policy processors from high-frequency events and avoidable data floods. We position binary spintronic MTJ crossbars as a plausible technology path for low reflex layers, while distinguishing today's MRAM/eMRAM technologies from the proposed reflex-layer crossbar module, which remains an architectural research target. The contribution is a system-integration framework: intelligence is distributed along the sensing, energy, storage and action chain, and each event is assigned to the lowest sufficient layer. We formalize Energy Returned on Invested Energy for Embodied Intelligence (EROIE), compare the framework with related architectures, and state its limitations and validation needs.

Abstract: This paper employs the Lie group method of invariants to re-investigate the domino toppling problem. By defining an anisotropic scaling group distinguishing horizontal propagation from vertical gravitational fall, we rigorously derive the universal scaling law \( v=\lambda \sqrt{\frac{g}{h}} f(\frac{\delta}{\lambda}) \) through both finite group transformation and infinitesimal generator approaches. Curve fitting yields the approximate power law \( v\sim \sqrt{\frac{\delta \lambda }{h}g} \). The Lie group decoupling reveals that the speed is governed by an effective dynamical length \( L_{eff} = \delta\lambda/h \) and is independent of domino width. Furthermore, we clarify that the power-law exponent \( \alpha \approx 1/2 \) corresponds to a complete scaling symmetry in the ideal frictionless limit. The introduction of friction breaks this symmetry, causing \( \alpha \) to fluctuate around \( 1/2 \), which is interpreted from the perspective of symmetry breaking.

Abstract: In this work, we describe a new dynamic rotational Thomson effect induced on rotating conductors exposed to chopped laser beam which generalizes analogue magneto transverse Thomson effects recently observed. We assume the existence of an out of-equilibrium self induced Barnett magnetic field which depends on helical thermal fields propagating on rotating conductors and it is associated to thermoelectric vortices . We deduce, assuming validity of Faraday law on the rotating out of equilibrium conductors, a time dependent rotational Thomson voltage, showing that is detectable on rotating ferromagnetic samples. We prove then the existence of dynamic tunable local magnetic phase transitions on rotating conductors associated to time dependent Curie temperature fluctuations proportional to the dynamic Thomson voltage. Finally we outline the relevance of this new time dependent magneto transverse Thomson effect either for dynamic thermal management that for dynamic tunable local insulator-metal transitions on rotating nano disks exploiting metamaterials.

Abstract: Wireless Internet-of-Things (IoT) data acquisition is an emerging instrumentation paradigm for nuclear and particle physics facilities, offering a flexible complement to established wired architectures based on VME, CAMAC, and OPC-UA. Despite growing deployment activity, the evidence base remains fragmented across conference proceedings, technical notes, and journal publications in instrumentation, nuclear science, and telecommunications. This scoping review systematically maps the evidence on wireless IoT DAQ in this context, following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) framework. A structured search of IEEE Xplore, Scopus, Web of Science, and the CERN Document Server, covering publications from 2015 to 2025, identified 47 papers meeting eligibility criteria after screening. LoRaWAN® dominates current deployments, appearing in 72% of identified systems, driven by its infrastructure independence and sub-GHz propagation characteristics suited to shielded environments. Radiation monitoring at large-hadron-collider-scale facilities is the most evidence-rich application domain; cyclotron equipment health monitoring is the most active non-CERN domain. Five priority evidence gaps are identified: empirical RF propagation data for African geological formations, long-term total-ionising-dose degradation data from deployed nodes, standardised wired-to-wireless DAQ integration interfaces, sub-millisecond wireless synchronisation, and documentation of Global South facility deployments. The review is grounded in direct operational experience across ATLAS/CERN detector instrumentation, the Dolosse DAQ framework at NRF–iThemba LABS, and the proposed Paarl Africa Underground Laboratory (PAUL).

Abstract: The origin of the mass–force hierarchy remains an open question in fundamental physics, since the Standard Model (SM) does not explain it. We examine whether fundamental constants, analyzed as natural-unit frequency scalars, display an underlying constrained integer-scaling power structure. We use Hermann Minkowski to approximate ratios of logs of constants by simple rational exponents, i/j, on a two-dimensional integer lattice. The first approximations define scaling characterized by parsimonious integer ratios, Diophantine residuals, and a conformal factor frequency vcf, a measure from exact power laws. We apply this to hydrogen, using all as the references to the others, but focus on the Rydberg frequency to compare electromagnetic and gravitational forces. All the exponents are near-perfect partial harmonic fractions in a structured form when the gravitational binding energy of the electron in hydrogen or the proton are the references. The ratio of two constants is encoded by the i, j, vcf, and either constant scalar. Monte Carlo tests demonstrate that the ensembles’ patterns are unlikely to arise in randomized datasets. These results indicate that part of the hierarchy among fundamental constants may be captured by simple rational scaling relations based on resonant nodes, without altering established SM values.

Abstract: In physics, and more broadly across the sciences, energy and entropy play a fundamental role in characterizing the evolution of a system. During such evolution, these two state properties must satisfy the First and Second Laws of Thermodynamics. The First Law states that in any evolutionary process, energy must be conserved. The Second Law states that in any evolutionary process, the entropy of the system can never decrease. In the continuum setting, evolutionary diffusion equations are described through the Laplacian operator, modeling, for example, heat conduction. Such continuum models satisfy both laws of thermodynamics, thereby ensuring physical consistency. In the graph setting, it is also possible to define analogous diffusion equations using the graph Laplacian operator. In this work, we address the problem of formulating thermodynamic laws in the discrete graph framework, in parallel with the continuum case. We prove that energy and entropy measures can be consistently defined, and that in the standard graph diffusion model the two laws of thermodynamics are satisfied. This result provides a guarantee of physical consistency for popular graph diffusion models. As an additional contribution, we also show that in an evolutionary graph diffusion process not only the standard Clausius entropy is non-decreasing in time, but also the equivalent Shannon entropy.

Abstract: Wearable electronic textiles (e-textiles) are increasingly being explored for healthcare, sports, military, and smart wearable applications, creating a growing demand for sustainable and flexible energy harvesting systems. In this study, a cost-effective and ultra-flexible textile-assisted thermoelectric generator (TEG) was developed using recycled electronic and textile waste materials. Discarded copper and aluminum foils recovered from electronic waste were integrated into a recycled woven fabric composed of 70% cotton, 28% polyester, and 2% elastane to fabricate the wearable thermoelectric device. The fabricated system demonstrated a measurable thermoelectric response, producing a maximum output voltage of 180.75 mV under a temperature difference (ΔT) of 5.82 K. The results demonstrate the feasibility of utilizing waste-derived conductive materials and recycled textiles for flexible thermoelectric energy harvesting applications. In addition to its lightweight and wearable structure, the developed device highlights the potential of sustainable smart textile systems for low-power wearable electronics and self-powered sensing applications. This work contributes to the advancement of environmentally sustainable smart textiles by combining waste reutilization, wearable energy harvesting, and flexible electronic integration within a single textile platform. Future research may focus on improving thermal contact efficiency, long-term durability, output stability, and scalable fabrication strategies for practical wearable energy harvesting applications.

Abstract: Interpreting interferometric synthetic aperture radar (InSAR) imagery is a critical task in monitoring volcanic and seismic activity, yet the process usually requires expert knowledge and manual analysis. As the volume of satellite observations continues to increase, automated methods capable of describing and interpreting these images become increasingly important in order to assist geophysical monitoring efforts. In this work, we investigate the feasibility of automated image captioning for InSAR data using modern vision-language models. We utilize the Hephaestus dataset which is a large collection of annotated interferograms focused on volcanic deformation, and apply a series of preprocessing steps to curate a balanced dataset of deforming and non-deforming images. Two generative image captioning architectures, the Generative Image-to-Text Transformer (GIT) and Bootstrapping Language-Image Pretraining (BLIP), are fine-tuned to output natural language descriptions of the InSAR images. In addition, we implement a retrieval-based model that aligns image and text representations within a shared embedding space and retrieves the most semantically similar caption. The performance of these approaches is evaluated using standard captioning metrics and qualitative inspection of generated descriptions. Our results suggest that pre-trained vision–language models can adapt to specialized scientific imagery despite being trained primarily on natural image datasets. This study represents an initial step towards automated interpretation systems capable of assisting researchers in large-scale InSAR monitoring applications.

Abstract: Selective energy conversion in confined catalytic nanocavities is examined through a coupled reactive‑photonic framework rather than only as a microscale flame‑stability problem. The experimental basis combines visible/NIR spectral measurements from Pt‑coated anodic porous alumina (APA) nanocavities with a smooth zirconia reference, together with structural information on ordered nanocavity platforms. Within the measured window, the Pt‑coated APA spectrum departs more strongly from the corresponding grey‑body fit than the zirconia reference, providing direct experimental indication that confinement alters radiative behaviour at accessible wavelengths. We interpret this divergence through a photonic‑chemical coupling framework in which high‑aspect‑ratio cavities reduce access to free‑space long‑wavelength radiative escape while increasing wall‑coupled relaxation and catalytic‑interface interaction. A converged FDTD benchmark at the dominant CO₂-4.3 µm band (Purcell factor Fp ≈ 1.26) shows moderate total LDOS enhancement while aperture flux is suppressed by more than six orders of magnitude relative to total radiated power. This indicates that the cavity redirects the emitted energy into wall‑coupled channels rather than allowing free‑space axial emission. The result is not a full mid‑IR device demonstration, but a mechanistically grounded, computationally supported case that confined combustion in Pt‑coated APA can operate as an integrated selective emitter and wall‑coupled heat‑redistribution architecture relevant to thermophotovoltaic and hybrid TPV/TEG energy conversion.

Abstract: In this work, a numerical investigation of an organic light-emitting diode (OLED) based on a bilayer architecture is presented, with particular emphasis on the influence of ZnO nanoparticles (ZNPs) concentration on charge transport, recombination dynamics, exci-ton formation, and luminescence performance. The studied device consists of a hole injec-tion layer combined with an electron transport and emissive layer based on Alq₃ doped with ZNPs. The impact of ZNPs concentration has been explicitly introduced into carrier mobility, dielectric permittivity, Langevin recombination rate, and radiative exciton decay. The simulation results show that increasing ZNPs concentration enhances charge bal-ance, recombination efficiency, exciton density, and luminescence power. Furthermore, the variation of ZNPs concentration from 0% to 10% in Alq₃ polymer layer increases the electron charge density from 0.65 x 10²¹cm^-3 to 1.4 x 10²¹cm^-3, the recombination rate from 1.25 x10²⁵ cm^-3 s^-1 to 12.5 x10²⁵ cm^-3 s^-1, the exciton density from 0.05 x 10¹⁵cm^-3 to 0.75 x 10¹⁵cm^-3 and the power of luminescence from 0.015W/μm² to 0.75W/μm². Since, the per-formance of Alq3-ZNPs-OLED is tenfold higher than of Alq3-OLED pure. These findings demonstrate that the incorporation of ZNPs is a key parameter for ameliorate and opti-mizing OLED performance which can serve many optoelectronic designs.

Abstract: Vibroacoustic monitoring provides a measurement-based approach for investigating heritage spaces in which architectural morphology, environmental conditions and sound-related practices are physically interrelated. This study applies a portable and non-invasive monitoring protocol to the medieval cave sanctuary of San Michele di Mezzo, located in Fisciano, Southern Italy. The site consists of stratified natural and built spaces, including a lower cave, an upper cave and a later upper church, and rep-resents a relevant case study for assessing the acoustic behaviour of small, irregular and fragile cultural heritage environments. The experimental procedure combined calibrated microphone recordings, time-domain signal inspection, third-octave-band analysis and impulse-response-derived room-acoustic indicators, including reverbera-tion, clarity and definition parameters. The results show that the lower and upper caves are acoustically differentiated, with the lower cave displaying more favourable clarity and definition values in selected low–mid frequency bands relevant to vocal practices. At higher frequencies, the differences become less systematic, indicating that the acoustic distinction between the two spaces is frequency-dependent rather than absolute. Comparative data from other cave and cave-like environments further con-textualize the measured response of San Michele di Mezzo. The findings do not imply intentional acoustic design; rather, they show that the long-lasting devotional central-ity of the lower cave is compatible with measurable acoustic conditions supporting spoken or sung ritual practices. More broadly, the study contributes to applied vi-broacoustics by demonstrating that low-invasive field monitoring can provide repro-ducible acoustic indicators for heritage interpretation, conservation-oriented docu-mentation and the investigation of intangible sound-related dimensions of cultural heritage.

Abstract: Accurate ionization energies are essential for understanding electronic structures of atoms and molecules, benchmarking quantum-chemical methods, and modeling ioni-zation processes in chemical and biological systems. In this work, we report calculated ionization energies of the H, C, N, O, P, and S atoms using a range of quan-tum-chemical approaches, aiming at reproducing the experimental values within the chemical accuracy. The methods include the electron propagator approximations OVGF and P3+, the coupled-cluster methods CCSD(T), CCSDT, and IP-EOM-CCSD, and the composite methods G3 and CBS-QB3. The CCSD(T), CCSDT, G3, and CBS-QB3 methods, together with the DFT method with B2PLYP density functional and several post-Hartree-Fock methods, were used in conjunction with the energy-difference (ΔSCF) approach. The coupled-cluster calculations were combined with the aug-cc-pVXZ-DK, aug-cc-pVXZ, and ANO-RCC basis sets, all-electron correlation, DKH2 scalar relativistic corrections, and extrapolation to the complete basis set (CBS) limit. The OVGF and P3+ methods do not reach chemical accuracy on average, while CCSD(T) and CCSDT combined with the aug-cc-pVXZ-DK basis set and CBS extrapolation achieve chemical accuracy for all atoms. CCSD(T)/aug-cc-pVXZ-DK with CBS extrapolation provides the best compromise between accuracy and computational cost, and can be used as a reference for these atomic ionization energies.

Abstract: This article provides general expressions for the phase velocity and the Doppler shift of the electromagnetic fields radiated from a uniformly moving Hertzian dipole measured by a uniformly moving observer. The results show that the phase velocity of the electromagnetic wave is always equal to when measured exactly in the direction pointing to the birthplace of the field. The expression for the Doppler effect is of the same form of the Newtonian type classical formula, which implies that it might be not proper to consider that the classical formula for the Doppler shift is the low speed approximation of the conventional relativistic formula.

Abstract: The fall of droplets of an aqueous NaCl solution in a vertical channel, filled with heated dry air, is studied. Water from the droplets evaporates quickly, and crystals of a solid salt crust form on their surface. At a later stage of the process, the remaining solution is removed from the droplet using a jet of water vapor that passes through the pores of the polycrystalline crust. It was first observed that some of the drying droplets suddenly shifted to one side under the influence of the reactive force generated by the vapor jet. The resulting salt particles are weakly porous and consist of many crystals. It has been proven that these particles don’t have a central cavity. The use of seawater and the role of salt particles in protecting against thermal radiation from fires are briefly discussed. Calculations based on Mie theory have shown that the contribution of light scattering by hollow sea salt particles formed above the ocean surface during relatively slow evaporation of seawater droplets can be significant in the ocean's heat balance.