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Arun Kumar

Publications and source records attributed to Arun Kumar.

At least 19 recordsLinked to original sources
arXiv2026

Probing Gravitational Wave Signatures from Periodic Orbits of Regular Black Holes in Asymptotically Safe Gravity

We investigate bound and periodic timelike geodesics and their associated gravitational-wave (GW) signatures in the spacetime of a regular black hole arising in asymptotically safe gravity (ASG). The geometry incorporates quantum corrections via a running gravitational coupling, encoded in a dimensional scaling parameter $ξ$, that modifies the near-horizon structure while preserving asymptotic flatness. We derive the effective potential for massive test particles and determine the conditions for stable circular and bound motion as functions of $ξ$, including the shift in the innermost stable circular orbit (ISCO). The three topological integers $(z,w,v)$, which represent the number of zooms, whirls, and vertices per radial cycle, are used to categorize the test particles' periodic orbits using Levin's zoom -- whirl taxonomy. Moreover, we employ the rational frequency ratio $q = \frac{ω_ϕ}{ω_r} - 1$ to find closed orbits, where $ω_ϕ$ and $ω_r$ stand for the azimuthal and radial frequencies, respectively. We examine how the orbital frequency spectrum is altered, whirl behaviour is enhanced, and deviations from the Schwarzschild limit are produced by the quantum parameter $ξ$. The GW forms for extreme mass-ratio inspirals (EMRIs) are calculated within the quadrupole approximation. We find that as $ξ$ increases, the signals that are released exhibit detectable amplitude modulations and phase shifts. The corresponding typical strain spectra fall within the anticipated sensitivity limits of space-based detectors such as LISA, Taiji, and TianQin, as they peak in the millihertz frequency band. Peak strain increases monotonically with $ξ$, indicating that observational restrictions on quantum-gravity-induced deviations from classical general relativity in the strong-field domain can be obtained from precise measurements of zoom -- whirl dynamics in EMRIs.

gr-qc↗
arXiv2026

Binder chemistry sets the interfacial balance constant in CsPbBr$_3$ nanocrystal supercapacitor electrodes

Previous work on lead-free tin halide perovskites showed that the binder instead sets the electrolyte concentration at which capacitance is maximised, following the relationship $100 \times [\mathrm{Li}^+]_{\mathrm{opt}} + \mathrm{PVDF}_{\mathrm{wt}\%} = ξ_{\mathrm{Int}}$ with $ξ_{\mathrm{Int}} = 25 \pm 2.5$, established by varying the loading of a single polymer. Whether $ξ_{\mathrm{Int}}$ is universal or specific to that polymer has not been tested. Here, four binders spanning fluorinated (PVDF), carboxylic (PAA), cellulosic (CMC) and sulfonic (PEDOT:PSS) chemistry are compared on $\mathrm{CsPbBr_3}$ nanocrystal electrodes at four LiTFSI concentrations in acetonitrile, with binder loading fixed at 15 wt% and all other formulation variables held constant. The relationship applies to $\mathrm{CsPbBr_3}$: PVDF at 15 wt% gives an optimum at 0.10--0.15 M and 112 F g$^{-1}$, against 126 F g$^{-1}$ reported for $\mathrm{CsSnCl_3}$ under the same conditions, extending the result to a different B-site cation, halide and crystal system. PVDF and CMC optimise at 0.10 M, giving $ξ_{\mathrm{Int}} = 25$, while PAA and PEDOT:PSS optimise at 0.15 M, giving $ξ_{\mathrm{Int}} = 30$, with maximum values of 188 F g$^{-1}$ for PAA and 146 mF cm$^{-2}$ for PEDOT:PSS. The two binders showing the shift carry ionisable acid groups at high density, indicating that $ξ_{\mathrm{Int}}$ expressed in weight percent requires a binder-specific value. In all four electrodes, $\mathrm{CsPbBr_3}$ converts to $\mathrm{PbBr_2}$ and CsBr during electrochemical characterisation, and the surface lead content of the PEDOT:PSS electrode decreases.

cond-mat.mtrl-sci↗
arXiv2026

Sodium Incorporation in CsPbBr$_{3-x}$I$_x$ Nanocrystal Electrodes: Lattice Contraction and the Suppression of Field-Driven Iodine Expulsion

Mixed-halide perovskite nanocrystal electrodes fail in supercapacitors through field-driven halide segregation. In undoped $\mathrm{CsPbBr_2I}$, this appears as a capacitance that climbs to 218% of its first-cycle value by cycle 1378 and then collapses to 39% by cycle 2500, with complete loss of the iodine signal from the cycled electrode. This work tests whether sodium incorporation suppresses that failure mode. Na-doped $\mathrm{CsPbBr_{3-x}I_x}$ ($x = 0, 1, 2$) nanocrystals were prepared by ligand-assisted reprecipitation at a Na/Pb precursor ratio of 1.25:1.00 and compared with undoped analogues in 0.1 M tetrabutylammonium tetrafluoroborate in anhydrous dichloromethane. Sodium contracts the pseudocubic lattice parameter of $\mathrm{CsPbBr_3}$ from $5.908 \pm 0.029$ to $5.851 \pm 0.019$ $\mathring{\mathrm{A}}$ after correction for specimen displacement. Specific capacitance at 0.3 A g$^{-1}$ rises for every composition, from 42 to 75, from 63 to 96, and from 56 to 84.5 F g$^{-1}$. Na-$\mathrm{CsPbBr_2I}$ gives the lowest charge-transfer resistance at 175 $Ω$ and the highest ion diffusion coefficient at $1.6 \times 10^{-16}$ m$^2$ s$^{-1}$, and power-law exponents between 0.33 and 0.45 at all potentials examined show that charge storage is limited by ion transport through the pore network rather than by the interfacial process. In the sodium-containing (Na-$\mathrm{CsPbBr_2I}$) electrode, the capacitance rise reaches only 115% at cycle 600, no collapse follows, and 97% is retained at 2500 cycles, the excess above the first-cycle value being reduced by a factor of 7.9. Iodine is retained at unchanged binding energy, and the Br:I ratio measured by elemental mapping is 2.30 after cycling against 2.25 before.

cond-mat.mtrl-sci↗
arXiv2026

An Interfacial Balance Rule Governs Binder-Electrolyte Coupling in Lead-Free Perovskite Energy Storage

Electrode binders are conventionally regarded as inert structural components. Here, we show that in lead-free perovskite supercapacitors, the binder defines the optimal electrolyte composition. Across a factorial matrix of poly(vinylidene fluoride) (PVDF) loadings and LiTFSI concentrations in CsSnCl$_3$ electrodes, the capacitance optimum shifts systematically with binder content along a single linear relationship, described by the Interfacial Balance Rule ($λ+θ=1$), where $λ$ and $θ$ are the normalized lithium-supply and polymer contributions at the optimized interfacial state. The same relationship holds for hybrid MASnCl$_3$, showing that the optimum is governed by the polymer-electrolyte interface rather than the perovskite lattice chemistry. Simulations using a pre-trained MACE machine-learned interatomic potential show that PVDF adopts a planar configuration on CsSnCl$_3$ and simultaneously interacts with cationic and anionic sites. This configuration homogenizes lithium adsorption energetics, introduces fluorine-mediated coordination, and confines lithium to a two-dimensional interfacial region while preserving lateral mobility. Tuning polymer coverage through surface density and chain length reveals a finite interfacial lithium accommodation capacity that marks the onset of out-of-plane aggregation. The Interfacial Balance Rule provides a macroscopic descriptor of this finite interfacial resource, balancing polymer-mediated lithium stabilization against limited accommodation space. Binder loading is therefore an active design parameter for polymer-regulated energy-storage interfaces.

cond-mat.mtrl-sci↗
arXiv2026

Shape Evolution and Dynamics of Deformable Ring

We numerically investigate the dynamics of a deformable closed ring filled with active particles. The ring is modeled as a flexible boundary made up of passive beads interacting with a harmonic spring force. The interior of the ring is filled with active Brownian particles (ABPs), and their activity is controlled through the rotational diffusion coefficient. We explore how, by systematically varying the activity of ABPs, packing fraction, and size of the ring, we can control the shape deformation and dynamics of the ring. At low packing fractions, low rotational diffusion coefficients, and smaller ring sizes, the ring exhibits highly irregular and strongly deformed shapes due to the uneven spatial arrangement of active particles along the boundary. Increasing the packing fraction, rotational diffusion coefficient, or ring size promotes a more even distribution of active particles within the ring, thereby suppressing shape deformations and fluctuations, driving the ring toward a more circular shape. We further analyze the mean-squared displacement (MSD) of the ring's center of mass and observe a crossover from ballistic to diffusive dynamics, which can be tuned by varying the system parameters. Our results demonstrate that, despite its internal complexity and deformability, the ring exhibits emergent behavior analogous to that of a single effective active particle. This study provides insight into the collective effects of confined active matter and the resulting macroscopic dynamics of deformable systems.

cond-mat.soft↗
arXiv2026

Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method

University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Within this context, symptoms of amotivation (i.e. loss of motivational drive) and anhedonia (i.e. diminished interest or pleasure) are particularly debilitating, yet they frequently go undetected. Developing new approaches to identify students with prominent amotivation and anhedonia could enable earlier and more targeted intervention. Machine learning (ML) methods have increasingly been used to classify individuals according to symptom severity. However, these ML models often suffer from class imbalance, where the majority of cases fall in the low-symptom group and relatively few in the high-symptom group. This imbalance can reduce model accuracy and bias predictions. To address this, studies commonly employ the popular oversampling strategy SMOTE. However, SMOTE has a notable limitation: it may generate invalid values for nominal variables. In this paper, we introduce a novel and effective oversampling method that addresses this shortcoming. Our approach leverages a predictive model to generate nominal variables, rather than interpolating them. We validate our method on a large-scale GPS location dataset collected from university students and demonstrate that it is significantly better than existing oversampling approaches in predicting elevated symptoms of amotivation and anhedonia.

cs.LG↗
arXiv2026

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model

Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist. We study whether a deep learning model trained on hip-worn accelerometer data can transfer to wrist-worn accelerometer data for sitting versus non-sitting classification. We use CHAP, a CNN-BiLSTM model originally developed for hip accelerometers, and evaluate its zero-shot performance on wrist data as well as its adaptation through finetuning with varying amounts of labeled wrist data. Experiments are conducted on the iWatch dataset with ground-truth posture labels derived from wearable cameras. The hip-trained model performs strongly on hip data without retraining, but accuracy drops on wrist data due to sensor placement shift. Finetuning CHAP provides consistent advantages over transformer models trained from scratch. These findings suggest that hip-based pretraining provides a useful starting point for wrist deployment, while highlighting the need for wrist-specific adaptation to handle higher signal variability.

cs.LG↗
arXiv2026

A rotating black hole in a Hernquist dark matter halo: horizon geometry, thermodynamics, and quantum emission

We investigate the geometrical, thermodynamic, and quantum emission properties of a rotating black hole immersed in a Hernquist dark matter halo. Starting from a static black hole spacetime surrounded by a Hernquist distribution, we construct its rotating counterpart through the noncomplexification formulation of the Newman-Janis algorithm and analyze the modifications induced by the independent halo parameters $ρ$ and $r_s$ and the rotation parameter $a$. The horizon structure is determined from the roots of the radial function $Δ(r)$, while the stationary limit surfaces and the corresponding ergoregions are obtained from the condition $g_{tt}=0$. We show that the Hernquist contribution displaces the outer event horizon toward larger radii and modifies the size of the ergoregion, whereas rotation controls the oblateness of the horizon and the strength of frame dragging. We further derive the surface gravity, Hawking temperature, Bekenstein-Hawking entropy, and heat capacity. The quantum tunneling rate is obtained from the Hamilton-Jacobi method, leading to the corresponding occupation number and a thermal estimate of the particle creation density. Finally, we estimate the Hawking luminosity and evaporation timescales within a Stefan-Boltzmann approximation. All standard Kerr and Schwarzschild results are recovered in the appropriate limiting cases.

gr-qc↗
arXiv2026

Shadows and lensing signatures of a rotating black hole in a Hernquist dark matter halo

We investigate the optical properties of a rotating black hole immersed in a Hernquist dark matter halo. The spacetime is generated from a static Hernquist black hole through the noncomplexification version of the Newman-Janis procedure, yielding a Kerr-like geometry whose halo contribution is encoded in the radial function $Δ(r)$ \cite{AraujoFilho:2026hernquist}. We derive the null geodesic equations, effective potentials, radial acceleration, and representative three-dimensional photon trajectories around the event horizon and ergoregion. Using the separability of the Hamilton-Jacobi equation, we obtain the critical impact parameters of unstable spherical photon orbits and construct the shadow contours for a distant observer. The rotation parameter mainly shifts and distorts the shadow, whereas the Hernquist halo enlarges the photon capture region and increases the apparent shadow size. Comparing the area-equivalent shadow diameter with the Event Horizon Telescope measurements of Sgr A$^\ast$ and M87$^\ast$, we constrain the dimensionless halo parameter $\hatρ=M^2ρ$. The strongest restriction comes from Sgr A$^\ast$, giving $\hatρ\sim(2.7-3.8)\times10^{-3}$ at $1σ$ and $\hatρ\sim(4.1-5.2)\times10^{-3}$ at $2σ$. We also analyze strong- and weak-field gravitational lensing. In the strong-field regime, the halo shifts the unstable photon orbit and critical impact parameter, controlling the logarithmic deflection angle and the position of relativistic images. In the weak-field regime, the halo contributes already to the leading bending angle and enhances deviations from Kerr as $ρ$ grows. From the Einstein ring of ESO325-G004, we further obtain $0\leq\hatρ\lesssim0.00939$ at $1σ$ and $0\leq\hatρ\lesssim0.01963$ at $2σ$.

gr-qc↗
arXiv2026

Periodic orbits as probes of charged loop quantum gravity black holes through gravitational waves

Gravitational waves from extreme-mass-ratio inspirals (EMRI) provide a direct probe of the strong-field geometry of black holes. Motivated by this, we study the motion of test particles and the resulting gravitational wave emission in the spacetime of a charged black hole inspired by loop quantum gravity (LQG), where the classical singularity is replaced by a smooth transition surface arising from the LQG polymerization, in which its radius is set by the LQG area gap condition. As a result, the polymerization parameter $δ_b$ is uniquely determined by the mass $M$ and charge parameter $Q$, so that all cases examined in this work contain LQG correction. By constructing the effective potential, the innermost stable circular orbit (ISCO) and the marginally bound orbit (MBO) are determined. Periodic orbits are classified using the Levin-Perez-Giz zoom-whirl taxonomy, showing how the orbit topology shapes the waveform, so that each closed trajectory is labeled by the triple integer $(z, w, v)$ and located through the rational frequency ratio $q = ω_ϕ/ω_r - 1$. Within the quadrupole approximation, the gravitational waveforms for an EMRIs are estimated, and the resulting polarizations are obtained in the time-domain and frequency-domain. The resulting polarizations in the time-domain exhibit a zoom-whirl morphology, with the waveform amplitude and phase dependent on the LQG parameter. The characteristic strain peaks in the millihertz band for all values of the charge parameter $Q$, and they exceed the projected sensitivities of LISA, Taiji, and TianQin, suggesting that future observations could place meaningful constraints on the LQG polymerization parameter in the strong-field regime.

gr-qc↗
arXiv2026

Nano-Clay-Stabilized Water-in-Oil Colloidal Pickering Emulsions as Thixotropic Lubricant

The limitations of conventional mineral oil-based lubricants motivate the development of environmentally benign emulsions capable of providing lubrication and heat dissipation in demanding applications. In this study, nano-organoclay (Garamite 1958)-stabilized thixotropic water-in-oil Pickering emulsions are developed using sunflower oil as the base. The rheological and tribological properties of the emulsion system are systematically examined. Rheological findings reveal a pronounced increase in yield stress, shear thinning and thixotropic behavior on increasing Garamite loading percentage in the emulsion. The tribological performance is assessed against dry, water, and oil-lubricated conditions for a steel-steel interface under high contact pressure. The findings indicate that the tribological performance is significantly influenced by the microstructure and thixotropic behavior of the emulsions. The emulsion with the optimal nano-clay concentration demonstrates approximately 41\% and 84\% lower friction and approximately 80\% and 96\% lower wear than oil and water, respectively. The emulsion exhibits sensitivity to the sliding direction and displays load-responsive friction behavior with a memory effect owing to the reversible structuring of the clay-droplet network. This superior performance is attributed to the combined effects of thixotropy, anisotropic nanoclay morphology, and stable droplet armoring, which form a robust and adaptive interfacial film. This study advances the understanding of Pickering emulsions in metallic tribosystems by correlating the microstructure and rheology with tribological performance, thereby facilitating the design of high-performance, smart, and eco-conscious lubricants for metallic systems.

cond-mat.soft↗
arXiv2026

MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment

Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight age-regression framework that achieves an MAE of 4.65 years on the UTKFace held-out test set while maintaining efficient on-device inference with an average latency of 14.4 ms measured using the AI Benchmark application. The model is built on a pretrained MobileNetV3-Large backbone combined with a compact regression head, enabling real-time prediction on mobile devices. The training and evaluation pipeline is integrated into the NN LEMUR Dataset framework, supporting reproducible experimentation, structured hyperparameter optimization, and consistent evaluation. We employ bounded age regression together with a two-stage fine-tuning strategy to improve training stability and generalization. Experimental results show that MobileAgeNet achieves competitive accuracy with 3.23M parameters, and that the deployment pipeline from PyTorch training through ONNX export to TensorFlow Lite conversion - preserves predictive behavior without measurable degradation under practical on-device conditions. Overall, this work provides a practical, deployment-ready baseline for mobile-oriented facial age estimation.

cs.CV↗
arXiv2026

Thermodynamics and phase transitions of charged-AdS black holes in dRGT massive gravity with nonlinear electrodynamics

Investigating black holes in modified theories of gravity offers fertile ground for exploring phenomena beyond the scope of general relativity. We investigate a novel class of charged anti-de Sitter (AdS) black holes within the ghost-free de Rham-Gabadadze-Tolley (dRGT) massive gravity, minimally coupled to an exponential form of nonlinear electrodynamics (NED). The NED sector is modelled by an exponential electrodynamics Lagrangian, which leads to singular black hole geometries in contrast to many regular configurations known in other NED models. In turn, we systematically investigate the thermodynamic properties and phase structure of the obtained black holes. The results show that the system has a rich thermodynamic structure. For different values of the magnetic charge $q$, the black hole can exhibit several types of phase transitions. These include van der Waals-like first-order phase transitions, second-order critical behavior, and a reentrant phase transition between small and large black holes without extending the phase space ($Λ=$constant). Our study enhances the understanding of AdS black holes in ghost-free massive gravity, providing further insights into the interplay between graviton mass and NED. The results highlight how the combined effects of graviton mass and electromagnetic nonlinearity can yield a rich and complex thermodynamic phase space, offering further insights relevant to the gauge/gravity duality and the ongoing search for observational signatures of modified gravity.

gr-qc↗
arXiv2026

MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]

Modern data applications increasingly involve heterogeneous data managed in different models and stored across disparate database engines, often deployed as separate installs. Limited research has addressed cross-model query processing in federated environments. This paper takes a step toward bridging this gap by: (1) formally defining a class of cross-model join queries between a graph store and a relational store by proposing a unified algebra; (2) introducing one real-world benchmark and four semi-synthetic benchmarks to evaluate such queries; and (3) proposing a lightweight middleware, MICRO, for efficient query execution. At the core of MICRO is CMLero, a learning-to-rank-based query optimizer that selects efficient execution plans without requiring exact cost estimation. By avoiding the need to materialize or convert all data into a single model, which is often infeasible due to third-party data control or cost, MICRO enables native querying across heterogeneous systems. Experimental results on the benchmark workloads demonstrate that MICRO outperforms the state-of-the-art federated relational system XDB by up to 2.1x in total runtime across the full test set. On the 93 test queries of real-world benchmark, 14 queries achieve over 100 speedup, including 4 queries with more than 100x speedup; however, 4 queries experienced slowdowns of over 5 seconds, highlighting opportunities for future improvement of MICRO. Further comparisons show that CMLero consistently outperforms rule-based and regression-based optimizers, highlighting the advantage of learning-to-rank in complex cross-model optimization.

cs.DB↗
arXiv2026

Knowledge-Centric Metacognitive Learning

Interactions are central to intelligent reasoning and learning abilities, with the interpretation of abstract knowledge guiding meaningful interaction with objects in the environment. While humans readily adapt to novel situations by leveraging abstract knowledge acquired over time, artificial intelligence systems lack principled mechanisms for incorporating abstract knowledge into learning, leading to fundamental challenges in the emergence of intelligent and adaptive behavior. To address this gap, we introduce knowledge-centric metacognitive learning based on three key principles: natural abstractions, knowledge-guided interactions through interpretation, and the composition of interactions for problem solving. Knowledge learning facilitates the acquisition of abstract knowledge and the association of interactions with knowledge, while object interactions guided by abstract knowledge enable the learning of transferable interaction concepts, abstract reasoning, and generalization. This metacognitive mechanism provides a principled approach for integrating knowledge into reinforcement learning and offers a promising pathway toward intelligent and adaptive behavior in artificial intelligence, robotics, and autonomous systems.

cs.AI↗
arXiv2026

ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control

Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittle process of tuning controllers. We introduce ZEST (Zero-shot Embodied Skill Transfer), a streamlined motion-imitation framework that trains policies via reinforcement learning from diverse sources -- high-fidelity motion capture, noisy monocular video, and non-physics-constrained animation -- and deploys them to hardware zero-shot. ZEST generalizes across behaviors and platforms while avoiding contact labels, reference or observation windows, state estimators, and extensive reward shaping. Its training pipeline combines adaptive sampling, which focuses training on difficult motion segments, and an automatic curriculum using a model-based assistive wrench, together enabling dynamic, long-horizon maneuvers. We further provide a procedure for selecting joint-level gains from approximate analytical armature values for closed-chain actuators, along with a refined model of actuators. Trained entirely in simulation with moderate domain randomization, ZEST demonstrates remarkable generality. On Boston Dynamics' Atlas humanoid, ZEST learns dynamic, multi-contact skills (e.g., army crawl, breakdancing) from motion capture. It transfers expressive dance and scene-interaction skills, such as box-climbing, directly from videos to Atlas and the Unitree G1. Furthermore, it extends across morphologies to the Spot quadruped, enabling acrobatics, such as a continuous backflip, through animation. Together, these results demonstrate robust zero-shot deployment across heterogeneous data sources and embodiments, establishing ZEST as a scalable interface between biological movements and their robotic counterparts.

cs.RO↗
arXiv2026

The Influence of Uniform Magnetic Fields on Strong Field Gravitational Lensing by Kerr Black Holes

We investigate strong gravitational lensing using magnetized Kerr black holes (MKBHs), which are accurate Kerr-Bertotti-Robinson solutions for Kerr black holes in a uniform magnetic field with additional magnetic field strength $B$ apart from mass $M$ and spin $a$. Unlike previous magnetized spacetimes, the MKBH geometry is Petrov type D, devoid of conical singularities, allowing photons to reach asymptotic infinity and making the concept astrophysically feasible. We use the strong deflection limit formalism to calculate the photon sphere radius, critical impact parameter, deflection angle, and lensing observables including the image position $θ_\infty$, angular separation $s$ and relative magnification $r_{\text{mag}}$, as well as their relationships with the parameters $a$ and $B$. Our results reveal that the relativistic image's photon sphere and angular size increase with $B$, whereas lensing observables deviate significantly from the Kerr scenario. For M87*, with $a=0.9$, the angular position of relativistic images increases from $10.8~μ$as (Kerr) to $12.02~μ$as, and the time delay between the first two images increases from $158.5$ h to $176$ h at $B=0.4$. Similarly, for Sgr A*, the image position increases from $14.4~μ$as to $16~μ$as, with time delays enhanced by approximately $0.7$ minutes. The relative magnification $r_{\text{mag}}$ grows with $B$ and deviates by $0.53$ from Kerr black holes at $B=0.4$. Our findings highlight strong gravitational lensing as a powerful tool to probe the presence of magnetic fields around astrophysical black holes, and in particular, we demonstrate that the MKBH spacetime enables constraints on the parameters $a$ and $B$.

gr-qc↗
arXiv2026

Pristine and Doped MoS2 Monolayers as Potential HCN Gas Sensors: A DFT Study

Two-dimensional transition metal dichalcogenides (TMDCs) have been extensively investigated due to their tunable properties. In this work, density functional theory (DFT) is employed to investigate the adsorption behavior and sensing characteristics of HCN on pristine and doped MoS2 monolayers (X-MoS2, where X = P, N, Si, Al, B, Cl). The structural, electronic, and optical characteristics of all systems are examined to study the sensing properties of various doped MoS2 monolayers. In particular, the Al-MoS2 system demonstrates the strongest adsorption characterized by chemisorption, while the remaining systems show interactions of physisorption type. Recovery time and changes in electronic and optical properties reveal that Si-MoS2 possesses an ultrafast response of the order of microseconds, while Al-MoS2 exhibits a significantly longer recovery time, making it unsuitable for reusable sensors. P-MoS2, Si-MoS2, and Al-MoS2 monolayers show pronounced changes in their properties after HCN adsorption. To explore tunability in adsorption strength and recovery behavior, systems with two and three dopant atoms are further studied for P, Si, and Al doping. The results indicate that double doping enhances adsorption strength, whereas triple symmetric doping weakens it. Based on adsorption energy, recovery time, and electronic response, 2P-MoS2 and 3Al-MoS2 are identified as promising candidates for electrochemical and chemiresistive sensing of HCN. Additionally, the observed optical response in the ultraviolet region highlights their potential in UV-range optical sensor design.

cond-mat.mtrl-sci↗