Abstract Abstract 
 This research highlights a fascinating parallel between the Megaparsec-scale cosmic web in space and a laboratory-created micro-scale particle web in laser plasma. We show that similar filamentary web structures on drastically different scales evolve from nonlinear – far from equilibrium - different physical processes. While the cosmic web is created by the gravitational collapse of dark matter and aggregation into galactic clusters, strings-filaments and voids, a microparticle web is created by the Brownian motion and aggregation of metal-oxide particles. Spatial intermittency - resulting from intermittent velocity and density fields – characterizes multifractal microparticle web as a mimic of the cosmic multifractal web. Based on selfsimilar hierarchical clustering of matter, the underlying processes create both webs with a (non-Gaussian) power law distribution of the particle number density, and of the cosmic matter density. This study reveals that variation of the metal-oxide microparticle density in the strings – resembles the large density variations of massive halos in the galactic filament-strings. Cantor set distribution of metal-oxide microparticle separation-intervals along the strings – mimics the Cantor set distribution of the intervals of mass sticking along the intergalactic filaments that connect galaxy clusters. 

Abstract Photonic-crystal surface-emitting lasers (PCSELs) can deliver watt-level, diffraction-limited emission from millimeter-scale, lens-free systems. However, scaling this performance to high-power red PCSELs remains challenging, as strong intrinsic absorption in III-V materials obstructs the integration in a common platform for embedded air holes and substrate-side emission. Here we introduce a novel red-emission PCSEL based on the air-column architecture that breaks the mirror symmetry of the double-lattice photonic-crystal along y=x diagonal to enhance its output efficiency. Three-dimensional coupled-wave theory (3D-CWT) combined with multi-parameter scanning is employed to quantify the radiative ability, inter-modal crosstalk, and coupling strength in infinite structures, as well as to extract the slope efficiency and threshold margin of finite devices. The optimized double-ellipse lattice with a 14° rotation yields a high slope efficiency of 0.82 W/A while maintaining robust transverse-mode discrimination and exhibiting tolerance to ±0.01 variations in the filling factor. Our work demonstrates that the symmetry deliberately broken by rotation in plane geometry provides an additional degree of freedom for maximizing the radiation efficiency of double-lattice PCSELs and enables a compact high-brightness red laser design for laser display and medical lighting.

Abstract Molecular dynamics simulations were employed to investigate Ar flow through a Cu nanochannel and the transport of Cu nanoparticles (NPs) of varying cylindrical sizes under different inlet velocities. The results revealed laminar flow with velocity and density profiles consistent with classical Poiseuille flow, confirming the validity of continuum assumptions for the nanoscale lengths considered here. Layers of solid-like structure were formed near the channel walls, and the apparent viscosity of the Ar fluid flow was found within expected values. The presence of NPs influenced the local flow behavior. NPs traveled faster than the average fluid velocity when moving near the channel center. Additionally, smaller NPs exhibited translational and rotational fluctuations due to lower inertia and stronger coupling with the fluid flow, whereas larger NPs moved more stably, dominated by inertial effects. Interfacial analysis showed that Ar atoms formed ordered layers on the NP surfaces, with coordination increasing with NP size. Higher inlet velocities disrupted these layers, reducing structural ordering. These findings provide atomistic insight into the interplay between hydrodynamics, NP size, and interfacial structuring, enhancing the understanding of NP transport and fluid–solid interactions in nanoscale channels.

Abstract This study introduces, a novel deep learning-assisted multiband terahertz metamaterial absorber (TMA)-based biosensor designed for precise identification of aggressive brain cancer. The metamaterial unit cell comprises four partially merged cross-shaped structures, each formed by sequentially arranging four horn-shaped patches at 90° intervals. The proposed TMA exhibits four distinct resonance peaks at 2.764 THz, 3.1 THz, 3.368 THz, and 3.763 THz, each achieving an absorption rate exceeding 99% with high Q-factors of 67.4, 59.6, 108.6, and 43.2, respectively. The unit cell’s symmetric configuration renders the TMA completely polarization-insensitive, representing a noteworthy technological enhancement. The biosensor effectively detects minute variations in the refractive index (RI) of the adjacent environment, demonstrating sensitivities of 0.57 THz RIU −1 , 0.52 THz RIU −1 , 0.71 THz RIU −1 , and 0.91 THz RIU −1 as the RI varies between 1.3 and 1.5. The results confirm excellent sensing performance across all four resonance peaks. Its capability to precisely differentiating between normal and cancerous brain cells has been thoroughly evaluated by introducing a fixed thickness analyte atop the metamaterial unit cell, representing normal (CSF: cerebrospinal fluid, GM: grey matter, WM: white matter) and cancerous (LGG: low-grade glioma, GBM: glioblastoma, Mets: metastasis) brain tissues using their exact RI values. Furthermore, a dataset of 2012 unique absorption has been generated by varying the polarization angle, incidence angle, and the tissue type. A deep neural network has been proposed that can distinguish these six types of cells. The model has a training accuracy of 99.9%, and validation accuracy of 98.8%. With the unseen test data, the model distinguishes these six cells with an accuracy of 97.03%. These results demonstrate that deep learning along with terahertz absorption spectroscopy can be an effective means for medical diagnostics and can inspire future research in this field.

Abstract To explore potential pathways for performance enhancement, this study investigates the effect of an external electric field on porphyrin-based dye-sensitized solar cells (DSSCs). Focusing on the porphyrin-based Dye 31, we systematically investigated the electronic structure characteristics of the dyes and Dye@TiO 2 system, as well as the regulatory mechanism of the local electric field on some parameters. Comparative analysis revealed that Dye 31 exhibits superior overall performance over Dye 25 across multiple metrics, featuring a smaller energy gap (E g ), enhanced intramolecular charge transfer (ICT), and a longer excited-state lifetime ( τ ). The 31@TiO 2 system exhibits significant charge separation, high electron injection, and better optoelectronic performance. Furthermore, the influence of local electric field strength on energy levels and gaps, spectra and charge transfer was discussed. This work provides insight into the photoelectric performance of chlorophyll derivative dyes and the mechanism of electric field influence.

Abstract 3D surface reconstruction from point clouds aims to generate continuous surface representations of physical objects from discrete spatial samples. This problem is of fundamental importance across diverse domains, including robotics, industrial design, and cultural heritage preservation. Among various surface representation schemes, Partial Differential equation (PDE)-based surfaces have emerged as a promising approach due to their strong mathematical foundations, compact representation, and high-order continuity properties. Despite these advantages, a key challenge lies in the analytical solution of PDEs used for surface modelling. Existing approaches often rely on numerical solvers, which are computationally expensive and prone to discretization errors. Although analytical solutions to fourth-order PDEs have been applied to parametric surface reconstruction, several limitations remain: (1) comparisons among different PDE orders (e.g., second-, fourth-, and sixth-order) have not been systematically explored; (2) hyperparameters within PDE solutions are typically tuned manually, which is inefficient and often suboptimal; (3) multiple analytical solutions to the same PDE are rarely compared in terms of their fitting accuracy; and (4) PDEs with non-zero sculpting force are rarely investigated for surface reconstruction. To address these issues, this study conducts a comprehensive analysis of closed-form PDE solutions of varying orders for parametric surface reconstruction from point clouds. The Genetic algorithm (GA) is employed to automatically determine optimal hyperparameter values, thereby improving reconstruction quality. Furthermore, we perform an extensive comparison among different PDE orders and among multiple analytical solutions derived from the same PDE. Lastly, we also investigate the impact of the sculpting force in the PDEs for surface reconstruction. Experimental results across several datasets of varying complexity demonstrate that the proposed framework achieves accurate and smooth surface reconstruction while significantly reducing manual intervention. This work provides new insights into the practical use of PDE-based parametric surfaces and establishes a foundation for future research in data-driven geometric modeling and surface reconstruction.

Abstract Tandem Solar Cells (TSCs) offer a strategic approach to surpass the efficiency limits of single-junction solar cells by harnessing complementary spectral absorption. This study explores the design and optimization of a novel TSC architecture composed entirely of Transition Metal Dichalcogenide (TMDC) materials, employing a WSe 2 -based top cell and a MoTe 2 -based bottom cell. The top cell leverages the tunable bandgap and superior carrier transport properties of WSe 2 to effectively harvest high-energy photons, while the MoTe 2 bottom cell is optimized to absorb lower-energy photons, enabling broad-spectrum solar utilization. The top cell architecture consists of an ITO/ETL/WSe 2 /HTL configuration, in which three (3) different electron transport layers (ETLs) and four (4) hole transport layers (HTLs) are evaluated to identify the optimal photovoltaic configuration. Key performance-affecting parameters including absorber thickness, doping density, defect density, and metal work function are systematically optimized. Following comprehensive optimization, the top cell with the configuration Al/ITO/ZnO/WSe 2 /CGSe/Ni achieves the highest performance, with a power conversion efficiency (PCE) of 33.50% . The pre-developed MoTe 2 -based bottom cell is tailored for tandem integration using a current-matching technique to align it’s short circuit current density (J sc ) with that of the top cell. The resulting WSe 2 /MoTe 2 tandem device demonstrates a power conversion efficiency of 58.73%, with a V OC of 2.28 V, a J SC of 32.86 mA cm −2 , and an FF of 78.4 % , outperforming the individual sub-cells. These theoretical results highlight the promising potential of all-TMDC tandem configurations for next-generation photovoltaic technologies.

Abstract We present a framework for empirical characterization and prediction of quantum key storage depletion using Markov Chain Monte Carlo simulation in Quantum Key Distribution systems. The framework leverages operational data from QKD systems implementing BB84, T12, and COW protocols measured over various observation periods to establish initial model parameters. We identify three distinct operational regimes of quantum key storage by analysis of measured data. Through comprehensive analysis of depletion rates as a function of storage capacity and key consumption-to-generation ratio, we characterize system-specific operating points exhibiting efficient reduction of depletion rate relative to storage size. The simulation provides predictions across varying system conditions. Model validation establishes practical validity boundaries. This research provides experimentalists with quantitative methods for storage optimization, proactive reliability assessment, and informed planning regarding transitions between quantum, post-quantum, and classical cryptographic systems. The developed framework supports strategic planning for quantum key distribution system deployment, addressing fundamental requirements for maintaining continuous operation.

Abstract Radio-frequency capacitively coupled plasmas (RF-CCPs) are central to microelectronics processing, yet their behavior is highly sensitive to the working gas. We employ a self-developed two-dimensional particle-in-cell/Monte Carlo collision (PIC/MCC) model to investigate He-Ar mixtures in He-rich, Ar-dilute regimes. Introducing a modest Ar fraction into He increases plasma density and improves spatial uniformity via complementary electron-impact processes on the two species. With increasing Ar fraction and pressure, the Ar ion energy distribution function (IEDF) shifts to slightly lower peak energy and broadens,whereas the overall He ions energy spectrum remains largely unchanged but exhibits broadening with partial extension into the high-energy region. These trends are consistent with a modified sheath potential and altered ion acceleration path lengths in a more collisional sheath. The results demonstrate that adjusting the Ar content provides a simple knob to tune IEDFs and uniformity in RF-CCPs without changing the external driving waveform.

Abstract Two-dimensional (2D) materials have gained significant attention for their atomic-scale thickness, extensive surface area, and outstanding mechanical properties, positioning them as promising nanoadditives to improve lubricant performance in diverse industrial fields. In this work, molybdenum disulfide (MoS 2 )/ reduced graphene oxide (RGO) nanocomposites were successfully synthesized using a hydrothermal route with varying graphene oxide (GO) weight percentages relative to ammonium heptamolybdate tetrahydrate (2%, 5%, 10%, and 15%). MoS 2 nanocomposites exhibited coexistence of tetragonal (1 T) and hexagonal (2H) phases with well-aligned nanosheets on the RGO surface, as confirmed by multiple characterization techniques. The tribological properties of SN 500 base oil containing intrinsic MoS 2 and MoS 2 /RGO nanocomposites were evaluated using a four-ball friction tester at 1200 rpm under a load of 200 N. The nanocomposite containing 10 wt% GO demonstrated superior friction reduction compared to MoS 2 and other composites, achieving a significant 32.06% improvement in antifriction performance relative to the base oil. Further studies on the load and temperature effects showed the lowest friction coefficient at ambient temperature under a 200 N load for this sample. These findings highlight the strong potential of MoS 2 /RGO nanocomposites as efficient nanoadditives for enhancing lubrication, indicating their promise for use in advanced high-performance industrial lubricants.