Quantum key distribution (QKD) enables secure communication using the principles of quantum physics. The BB84 protocol is not only effective for secure key sharing but also for detecting eavesdropping. This study simulates a computational model of QKD and analyzes error rates under varying numbers of bits. Using Python-based simulations with n = 10, n = 100, and n = 1000 bits, and a fixed noise probability of 0.02, we evaluated scenarios both with and without eavesdropping over 1000 trials. The results show an average error rate of 2% without eavesdropping and over 26% with eavesdropping. Standard deviation increases with lower n, indicating higher variability. This study validates BB84’s robustness under noise and demonstrates its sensitivity to third-party interference.

Spectral shift functions (SSFs) provide a powerful framework for understanding how the spectrum of a self-adjoint operator changes under perturbation, and they play a central role in trace formulas that generalize the classical results of Krein and Koplienko. While higher-order SSFs have been extensively developed in abstract settings—particularly under Schatten class assumptions or within noncommutative frameworks with τ-compact resolvents—their explicit computation remains challenging, especially for differential operators arising in quantum mechanics. In this paper, we define and compute the SSF of order k for a one-dimensional Schrödinger operator perturbed by a constant potential, and rigorously verify the associated trace formula. Our approach, grounded in classical Hilbert–Schmidt theory and Fourier analysis, bypasses abstract machinery while capturing physically meaningful scenarios. These results also serve as a foundation for future work on more singular perturbations, such as delta and square-well potentials.

Dusty plasmas are plasmas that contain charged dust particles, in addition to the usual ions and electrons, and are found in space, industry, and laboratory experiments. Due to their omnipresence, studying them helps us understand natural phenomena such as planetary rings, comet tails, and interstellar clouds, as well as improve plasma-based technologies on Earth. In this work, we investigate the critical Mach number Mcr and small-amplitude solitary wave structures in an unmagnetized dust-acoustic waves (DAWs) dusty plasma with superthermal kappa-distributed electrons κe and ions κi . Using the Sagdeev pseudopotential approach, we analyze the effects of superthermal species, the ratio of dust to electron temperature σd , and the concentration of ion density δi . The results show that the critical Mach number is affected by superthermal species and the ratio of dust to electron temperature. The amplitude and width of small-amplitude solitary waves are also affected by the Mach number M and the concentration of the ion density. These findings are relevant for understanding nonlinear wave dynamics in space and astrophysical dusty plasma environments.

The objective of this work is to develop a theoretical model, to study the effect of quantum species, activation potential, current density, and temperature on the performance of Pt/C catalysts in Proton Exchange Membrane Fuel Cells (PEMFCs). For this we modified Butler-Volmer equations and analyzing I-V characteristics, the observation shows lower activation potential of 40 mV yields better performance compared to 55 mV. The effect of temperature was observed showing that increased in temperatures can mitigate carbon support corrosion and decline the performance of PEMFCs. Also increasing the electron flow per reaction cycle decrease the performance of PEMFCs by screening the flow of electron by quantum species formed around anode of PEMFCs. This result bring negative voltage and power, highlighting the complex interplay between these factors. The results underscore the importance of optimizing activation potential and managing temperature to enhance PEMFCs performance and longevity.

Learning is inherently a social process as outlined by the theory of constructivism. Learners construct knowledge via active participation and experience. Meaningful learning requires multiple means of engagement, representation of materials, and assessment. The current challenges in physics teaching-learning are understanding of the learning mechanism, designing of proper instructional materials, and use of student-engagement strategies that help them in long-term retention of the concepts. Pedagogical research shows that the teaching-learning process should be learner-centered where the learners will take ownership of their learning. This paper provides a review of various Research-Based Instructional Strategies (RBIS) designed to enhance physics education through learner-centered and authentic teaching practices. The literature shows that the use of RBIS fosters a more engaging learning environment, students’ deeper understanding of the material, and higher retention rates. This paper also explores the limitations of traditional lecture-based pedagogy and emphasizes the need for active learning approaches. Furthermore, it addresses challenges in implementing RBIS, such as large class sizes, time constraints, instructional resource availability, and so on. Finally, we summarize our effort to disseminate the RBIS within the Nepali physicists’ community.

This study reports the structural, electronic, and magnetic properties of undoped and halogen (F, Cl, and Br) doped ZnO monolayers (3 × 3 × 1) by replacing one Zn-atom. Using spin polarized Density Functional Theory (DFT) in VASP code and a projected augmented wave basis set, we employed GGA-PBE and PBE+U exchange correlation functionals. The band gap of pristine ZnO was measured to be 1.67 eV and 2.61 eV for PBE and PBE+U, respectively, whereas band gap decreased significantly upon addition of halogen atom. Likewise, doped ZnO shows semimetallic behavior, whereas undoped ZnO exhibits semiconducting behaviour. Further, the magnetic moments of about 1 µB for Cl and Br-doped ZnO, and 1.02 µB & 2.98 µB for F-ZnO utilizing PBE and PBE+U functionals, respectively were observed. These findings suggest that halogen-doped ZnO might carry huge potential for next-generation spintronic nanodevices.

Large Language Models (LLMs) have grabbed significant attention from diverse technical fields due to their impressive performance on a variety of Natural Language Processing (NLP) tasks. Although these models excel in various generative tasks, they lack the robust reasoning ability required to solve complex mathematics and physics problems. Despite their inherent limitations, Generative Artificial Intelligence (AI) based chatbots, powered by these large language models, are being rapidly adopted by students in physics and other technical fields. In this project, we assessed the ability of various generative AI-based models to solve Physics problems. We asked currently popular AI models to solve Physics questions from a final board exam of class 12 of the Higher Secondary Education Board (HSEB) of Nepal. We then evaluated the AI-written solutions by the subject matter experts. We found that the gpt-4o model by OpenAI performed the best, securing 90% among the models studied. In this paper, we provide a brief overview of these models and compare their performance as evaluated by a University Physics professor. We will also discuss the risks and benefits of their use in higher education.

Non-thermal plasma (NTP) is a rapidly advancing multidisciplinary field, gaining recognition for its environmentally friendly and chemical-free attributes. Over the past decade, NTP has drawn considerable interest due to its extensive potential applications, particularly in the realm of water treatment. When applied to water, NTP creates an acidic environment, which significantly modifies several key properties, including pH, electrical conductivity, and turbidity. Additionally, the process generates reactive oxygen and nitrogen species (RONS), which induce notable changes in the chemical composition of the water. These unique characteristics of plasma-treated water present a promising alternative for microbial disinfection. In our research, NTP is generated at atmospheric pressure using a high-voltage power supply operating at 12 kV and 50 Hz. The plasma discharge produced under these conditions is thoroughly examined through electrical and optical diagnostic methods to ensure accurate characterization. Various water samples, including those collected from river, tap, and well, are treated using a non-thermal plasma source. After treatment, the physicochemical parameters of the water samples are systematically analyzed to assess the impact of plasma treatment and to highlight its potential applications in water purification and disinfection.

The perovskite compound LaMnO3 has gained interest due to its role as a fundamental building block in some heterostructures and its fascinating magnetic phase diagram. In Bulk stoichiometric form, LMO displays A-type antiferromagnetic order. However, thin films exhibit ferromagnetic properties and spontaneous magnetization reversal for zero field cooled (ZFC) magnetization cycles. Our study examined the field-dependent variation of blocking (TB ), freezing (Tf ), and compensation (Tcomp ) temperature associated with the ZFC cycle. The blocking temperature follows the relation TB (H) = TB (0)(1 − H/HK )2 , typical of magnetic nanoparticles indicating nanoclusters of spins in the thin film. The freezing and compensation temperatures exhibited exponential decay with increasing field. We examined the temperature dependence of coercivity and exchange bias. The observed trend in coercivity, combined with the absence of exchange bias, supports the presence of a mixture of weakly interacting superparamagnetic and antiferromagnetic phases. Below the blocking temperature coercivity follows the relationship HC (T ) = HC0 [1 − (T /TB )1/2 ].

Basmati rice, renowned for its exceptional aroma, distinct flavor, and long grains, is a staple in global cuisines. Understanding and enhancing the germination and growth of Basmati rice seeds carry significant implications for both agricultural practices and culinary traditions. This study investigates how plasma-activated water, produced through atmospheric pressure air gliding arc discharge, affects Basmati rice seeds’ germination and growth parameters. The physiochemical analysis reveals a progressive increase in water acidity, electrical conductivity, oxidation-reduction potential, total dissolved solids, and nitrate/nitrite concentrations as treatment time extends from 0 to 20 minutes. The results highlight a significant impact of plasma-activated water on Basmati rice germination and seedling growth. The seed imbibition rate rises with longer plasma activation, reaching a peak at 10 minutes, leading to maximum seed germination, extended seedling shoots, and increased plant weight compared to the control group. However, prolonged exposure (20 minutes) shows adverse effects on seed germination and growth. These findings contribute valuable insights into the potential applications and limitations of plasma-activated water in Basmati rice.