Abstract Spin defects in wide-bandgap semiconductors hold great promise for the implementation of solid-state quantum bits (qubits) within quantum technologies. These defective qubits can be engineered by applying external optical, electrical, or mechanical controls. In this study, we present electrical- and strain-driven approaches for the deliberate design of defect spins in functional silicon carbide that could serve as qubits. By employing advanced ab-initio calculations based on the standard density functional theory, we predict that the divacancy centers in silicon carbide exhibit spin-triplet ground states, and their electronic and spin-related properties can be tuned by applying the electric field and the realistic uniaxial and biaxial strains. These spin defects can be effectively used to realize qubits. The strain-driven strategy demonstrated in this work can be readily extended to a broad range of point defects in other wide-bandgap semiconductors, thus paving the way for manipulating the spin properties of defects in ionic systems, leading to potential advancements in spintronic technologies.
Abstract Red-emitting SrMoO 4 phosphors co-doped with Eu 3+ and Bi 3+ were synthesized using a standard solid-state reaction method. Phase analysis confirms the formation of a pure scheelite-type tetragonal structure without secondary phases. The optimized phosphor, obtained at 1200 °C for 5 hours with 3%Eu 3+ and 4%Bi 3+ , exhibits significantly enhanced luminescence performance. Under blue-light excitation, the phosphor exhibits intense emission in the red spectral region 590 - 710 nm, which arises from the characteristic 5 D 0 → 7 F j (j = 1, 2, 4) transitions of Eu 3+ ions. Notably, Bi 3+ co-doping leads to an approximately 28-fold increase in emission intensity compared with the singly Eu 3+ -doped sample, owing to effective transfer of excitation energy from Bi3+ sensitizers to Eu 3+ activators. These results demonstrate that SrMoO 4 :Eu 3+ /Bi 3+ demonstrates considerable potential as a red phosphor for advanced pc-WLED applications.
Abstract In this research, a simple tip sonication strategy for the formation of ZnO/Al/N nanoflowers for an improved room temperature CO gas sensor device under UV light activation was studied. The morphology and structure properties of ZnO/Al/N nanoflower were studied under diversified analytical methods such as X-ray diffraction, Transmission electron microscopy, and other surface area measurements. The gas sensing was investigated and analyzed carefully at room temperature through a variety of analyses. The ZnO/Al/N nanoflowers display an enhanced response toward CO at room temperature under UV effect. The light effect and its enhanced mechanism toward sensor optimization were studied. The sensor can reach the gas concentration limit at 5 ppm.
Abstract This contribution summarizes a talk given at the “Phenikaa International Physics Conference 2025: Celebrating 100 years of quantum physics” from 13 to 15 October 2025 at the Phenikaa University, Hanoi, Vietnam. It gives a compact overview of the highlights and milestones of particle physics and an outlook on future developments.
Abstract The structural, electronic, magnetic, and optical properties of transition-metal-adsorbed BaO monolayers (TM-BaO, TM = Co, Fe, and Mn) are investigated using density functional theory. The pristine BaO monolayer is a non-magnetic semiconductor with strong dielectric anisotropy and weak optical absorption in the visible and near-infrared regions. Transition-metal adsorption induces strong hybridization between TM 3d and O 2p orbitals, transforming the system into a spin-polarized semimetal with finite magnetic moments. In addition, the optical response is significantly enhanced, with pronounced absorption in the visible and near-infrared regions, while structural stability is preserved. These results highlight TM-BaO monolayers as promising candidates for tunable spintronic and optoelectronic applications.
Abstract Positron emission tomography (PET) is an important imaging technique in nuclear medicine, in which fluorine-18 ( 18 F) plays a key role due to its favorable physical and biological properties. Given the increasing demand for 18 F, optimizing irradiation parameters in cyclotron-based production is essential to maximize yield and cost-efficiency. This study systematically investigates the influence of incident proton energy ( E ) and target thickness ( t ) on the production efficiency of 18 F via the 18 O(p,n) 18 F reaction using the ISOTOPIA simulation framework. Simulations were performed for proton energies ranging from 3 to 30 MeV and target thicknesses from 100 to 1000 mg/cm 2 , under irradiation conditions representative of medical cyclotrons. The results reveal the existence of well-defined optimal energy-thickness windows corresponding to different levels of normalized end-of-irradiation activity R , i.e., from 0.01 to 0.175 Ci/ μ A. Based on these findings, a classification map is developed to support efficient parameter selection while minimizing the consumption of enriched 18 O water. Owing to the use of IAEA-recommended nuclear data libraries, the obtained results exhibit a high level of reliability and can serve as a practical reference for routine medical cyclotron operation.
Abstract We combine machine learning and theoretical modelling to predict and analyze the thermal and molecular dynamics properties of metallic glasses. First, we use machine learning models to estimate the glass transition temperature (T g ) from alloy compositions. Our approach uses the most minimal input features to date, simplifying the data processing workflow while still achieving high accuracy. Then, the machine learning predicted T g values are input into the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to calculate the structural relaxation time as a function of temperature. Our theoretical calculations show excellent agreement with previously reported experimental data. Overall, this study provides a quantitative framework in agreement with experiments and prior works. This approach would pave the way for the discovery and design of metallic glass materials with engineered thermal properties.
Abstract In this study, we report the selective enhancement of red upconversion (UC) emission in Er-Yb-Mo tri-doped CeO 2 cubic phosphors synthesized via solid-state reaction, which also exhibit promissing thermal sensing properties. X-ray diffraction and high-resolution transmission electron microscopy confirm the formation of a single-phase cubic CeO 2 with high crystallinity. X-ray photoelectron spectroscopy (XPS) reveals binding energies corresponding to the oxidation states of Ce 4+ and O 2- ions in the host lattice and dopant ions including Er 3+ (4d), Yb 3+ (4d), and Mo 5+ (3d). Under 975 nm excitation, the phosphors exhibit intense green and red UC emission, with a prominent red emission at 658 nm, whose intensity strongly depends on Mo dopant concentration. The sample containing 3 mol% Mo shows a remarkable enhancement in red emission intensity, up to 153 times higher than that of the undoped counterpart. This enhancement is attributed to modifications in the crystal field around Er 3+ ions and the formation of oxygen vacancies, which promote dominant red emission in the presence of Mo 5+ , as supported by the XPS results. Furthermore, the phosphor exhibits a high relative sensitivity of up to 0.845% for green emission at 303 K, indicating its potential for applications in optical thermometry and solid-state lighting.
Abstract Reliable discrimination of volatile organic compounds (VOCs) with similar physicochemical properties, such as ethanol and acetone, remains a key challenge for semiconducting metal-oxide (SMO) sensors. Herein, α-Fe 2 O 3 –ZnO heterojunction nanofibers (HJ-NFs) were fabricated via double-jet electrospinning and calcination, with HJ density tuned by electrospinning duration (30-120 min). The optimized 60 min sample achieved the highest responses of 11.45 (ethanol) and 8.52 (acetone) at 450 °C, ~5× higher than pristine oxides, due to enhanced charge separation and increased active sites at well-defined interfaces. To address overlapping response signatures, PCA and LDA were employed, enabling clear discrimination with a ratio of 8.20. This work demonstrates that integrating HJ engineering with machine learning provides an effective strategy for high-performance, intelligent gas sensing.
Abstract In this study, a dual-emitting phosphor based on Pr 3+ -activated SrMoO 4 (SMO:Pr 3+ ) was successfully synthesized. The influence of calcination temperature as well as dopant concentration on its luminescent behavior was systematically examined. X-ray diffraction analysis verified the formation of a pure SMO phase, suggesting that Pr 3+ ions were effectively incorporated into Sr 2+ lattice sites. Field-emission scanning electron microscopy (FESEM), together with energy-dispersive spectroscopy (EDS), indicated that the material consists of granular particles with an average size of about 1 μm. The obtained phosphor displays a wide excitation band within the blue region and produces a strong, narrow red emission centered at 645 nm and blue emission at 478 nm. The optimal photoluminescence performance was observed for the sample annealed at 800 °C with a Pr 3+ concentration of 1.0 mol%. These findings indicate that SMO:Pr 3+ is a promising phosphor for use in blue-excited white light-emitting diodes (WLEDs).
Abstract We present a pair-centered deep-learning framework for interatomic force prediction in which the local environment is described explicitly through a shielding-effect representation. Rather than treating an atomic pair as an isolated geometric object, the model augments pair distance and elemental information with a descriptor that measures how surrounding atoms screen or modulate the interaction between the two atoms. This environment-aware formulation allows the network to learn not only direct pair interactions but also the local many-body influence encoded through shielding contributions. We evaluate the method for crystalline silicon and silicon hydride, where the inclusion of shielding information consistently improves force prediction relative to primitive pair descriptors. In addition, the learned shielding-aware pair representation can be transferred to total-energy prediction and yields interaction profiles that remain chemically interpretable. These results indicate that integrating shielding effects into pairwise descriptors provides an effective and physically meaningful route for constructing machine-learning force fields.
Abstract A digital γ − γ coincidence spectrometer has been developed at the Dalat Nuclear Research Reactor (DNRR). The setup utilizes a CAEN DT5730S digitizer to directly process signals from two High-Purity Germanium (HPGe) detectors, which were originally part of an existing analog system. By implementing the coincidence logic entirely within the digital firmware, the system achieves a resolving time of 114.2 ns. Compared to its analog predecessor, the digital system slightly improves energy resolution (4.5 keV vs. 4.7 keV at the 1.33 MeV peak of 60 Co under identical conditions); however, the timing resolution remains inferior to the 14.3 ns achieved by the analog setup. The system’s performance was validated using a mixed 60 Co/ 137 Cs source, as well as prompt γ -rays from the 35 Cl(n th , γ ) 36 Cl and 182 W(n th , γ ) 183 W reactions. The most significant advancement of this digital system is the substantially wider measurable energy range, which now spans from 100 keV up to 12 MeV. This expansion facilitates the detection of critical low-energy γ -ray cascades (below 520 keV) that were previously obstructed by analog hardware thresholds. Despite current limitations in timing resolution, the developed digital system is highly stable, flexible, and provides exceptionally clean data suitable for contemporary nuclear structure studies. Upcoming work will focus on optimizing the timing resolution, as high timing resolution brings the opportunity to more delicate studies, such as the lifetimes of excited states, a topic of significant interest.
Abstract We study the radiative decay Υ(1 S ) → η b γ and the Dalitz decay Υ(1 S ) → η b e + e − within the framework of the Covariant Confined Quark Model. We provide theoretical predictions for the hadronic form factor, the radiative decay constant, and the decay branching fractions. Within the Standard Model we predict Γ(Υ(1 S ) → η b γ ) = 9.3(9) eV and Γ(Υ(1 S ) → η b e + e − ) = 4.8(5) × 10 −2 eV. The contribution of the hypothetical ATOMKI X 17 vector boson to the Dalitz channel is also investigated.
Abstract Ce 3+ -doped Yttrium Aluminum Garnet phosphors (Y 3 Al 5 O 12 :Ce 3+ , YAG:Ce) were synthesized via a solid-state reaction (SSR) to investigate the effects of dopant concentration and calcination temperature on structural and optical properties. A series of YAG:xCe samples (x = 0, 0.02, 0.04, 0.06, 0.08, 0.1) were annealed at temperatures ranging from 1200 to 1700°C. The optimized YAG:0.06Ce sample exhibited a broad yellow emission band (520 – 560 nm) under 450 nm excitation, with dominant peaks assigned to the allowed 5d → 2 F 5/2 and 5d → 2 F 7/2 transitions of Ce 3+ . X-ray diffraction analysis and FESEM observation confirmed the formation of a highly crystalline cubic garnet phase (Ia3d), with grain sizes increasing from several hundred nanometers to a few micrometers at higher temperatures. These results demonstrate that optimizing Ce 3+ concentration and thermal treatment is crucial for enhancing the structural and luminescent performance of YAG:Ce, highlighting its potential as an efficient yellow-emitting phosphor for high-performance white LEDs (WLEDs).
Abstract In this work, we investigate the evolution of in-plane magnetic anisotropy in FeGa thin films as a function of post-deposition annealing temperature. FeGa films were deposited on GaN/Al 2 O 3 (0001) substrates by RF magnetron sputtering and subsequently annealed to tailor their structural ordering and composition. Room-temperature magnetic measurements reveal a strong annealing-dependent modification of anisotropy symmetry and strength, with moderate annealing enhancing directional response and higher temperatures leading to well-defined magnetocrystalline anisotropy. Structural analysis indicates a transition from predominantly L1 2 ordering in as-deposited and low-temperature annealed films to the emergence of the D0 3 phase at higher annealing temperatures, accompanied by lattice parameter variation and compositional redistribution. These results establish a direct correlation between annealing-induced phase transformation and magnetic anisotropy, providing insight into the controlled tuning of FeGa thin films for magnetostrictive and spintronic applications.
Abstract Thermal decomposition temperature is a crucial factor in evaluating the thermal stability and practicality of polymer materials. In this study, we explore data-driven approaches for predicting the thermal decomposition temperature of polymers using both classical machine learning (CML) models and a small language model (SLM). We use experimental polymer datasets from the PolyInfo database to train Random Forest and XGBoost models, which utilize molecular fingerprints as structured input features. In contrast, the SLM-based approach directly uses polymer expressed as Simplified Molecular Input Line Entry System (SMILES) strings in textual form, eliminating the need for feature engineering or explicit preprocessing of molecular descriptors. This presents an alternative modeling framework for predicting polymer properties, where structure-property relationships are learned directly from raw chemical representations. In addition to thermal decomposition temperature, we also apply this framework to predict glass transition temperature using a dataset previously reported in our work, demonstrating its potential applicability to multiple polymer thermal properties. Overall, our results suggest that small language models can serve as a valuable alternative modeling strategy for predicting polymer thermal properties, providing a complementary perspective to traditional methods.
Abstract This study evaluates the fundamental kinetic parameters of the Dalat Nuclear Research Reactor (DNRR) fueled with Low Enriched Uranium (LEU). Parameters including the effective delayed neutron fraction ( β eff ), prompt neutron generation time (Λ), and prompt neutron lifetime ( l p ) were quantified using the Serpent 2 Monte Carlo code and the ENDF/B-VIII.0 nuclear data library. To ensure numerical robustness, several built in methods were employed, including the Meulekamp–Spriggs, Chiba, Nauchi–Kameyama, Iterated Fission Probability, and perturbation methods. The computed β eff values ranged from 732.87 to 736.13 pcm, while the Λ and l p varied between 81.08–81.92 μ s and 81.12–81.96 μ s, respectively. The discrepancies among the numerical methods remained within 1.07%, demonstrating a high consistency. Furthermore, comparison with MCNP6.3 calculations showed deviations of less than 1.21%, validating the reliability of the Serpent 2 model. These results provide a robust baseline for further kinetic analysis of the DNRR core.
Abstract Activated carbon derived from agricultural biomass has emerged as a promising adsorbent for heavy metal removal due to its low cost, environmental sustainability, and tunable surface properties. In this study, activated carbon was synthesized from waste coffee grounds via anaerobic pyrolysis at different temperatures (400–650 °C), followed by oxygen plasma surface modification. The physicochemical characteristics of the materials were investigated using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and wettability measurements. Porous structures were more pronounced at lower pyrolysis temperatures (400–450 °C), while XRD patterns revealed the evolution of amorphous carbon with broad diffraction peaks around 20° and 25°. Oxygen plasma treatment significantly improved surface hydrophilicity, reducing the water contact angle from 67.95° to 0° after 1 min treatment. Adsorption experiments showed that the sample pyrolyzed at 450 °C and plasma-treated for 1 min exhibited the best Cu 2+ removal among untreated samples, achieving a removal efficiency of 34.14% and an adsorption capacity of 33.39 mg g −1 within 20 min. Remarkably, chemical activation using KOH followed by secondary pyrolysis and plasma treatment greatly enhanced adsorption performance. The optimized material achieved complete Cu 2+ removal with a high adsorption capacity of ~416.31 mg g −1 within 20 min. These findings demonstrate that the synergistic combination of chemical activation and plasma modification is an effective strategy for developing high-performance biomass-derived carbon adsorbents for rapid heavy metal removal.
Abstract The anharmonic high-order extended X-ray absorption fine structure (EXAFS) cumulants of cadmium metal (Cd) are theoretically investigated, accounting for the combined effects of thermal and structural disorder. The formulation is developed within quantum statistical theory using a first-order perturbation treatment. In this approach, the correlated Einstein model is combined with an anharmonic effective potential to describe interatomic interactions. The resulting thermodynamic EXAFS quantities include both correlation and anharmonic contributions and explicitly account for the influence of nearest-neighbor atoms in the absorber–backscatterer pair. Temperature-dependent analytical expressions are obtained and are valid in both low- and high-temperature regimes. Numerical results for Cd show good agreement with available experimental data and other theoretical models in the temperature range 0–500 K. These results demonstrate that the present framework can effectively analyze anharmonic high-order EXAFS cumulants in thermally disordered metals and structurally distorted systems.
Abstract This study investigates the development of ZnO:graphene (ZnO:Gr) hybrid nanostructures for high-performance gas sensing at room temperature under 365 nm UV illumination. The graphene content was systematically varied to elucidate its synergistic role in modulating both sensing response and selectivity. Structural and spectroscopic analyses (XRD, SEM, and Raman) confirm the formation of a robust 0D/2D heterostructure characterized by strong interfacial coupling and induced lattice strain, while preserving the hexagonal wurtzite ZnO phase. Electrical characterization reveals a percolation-driven transition in charge transport, where the transition from ZnO-dominated to graphene-dominated conduction occurs near the 1.0–2.5 wt% threshold. Intriguingly, the ZnO:Gr hybrids exhibit a unique p-type-like sensing response, characterized by a resistance increase upon exposure to both reducing and oxidizing gases. This anomalous behavior is attributed to an interface-controlled mechanism, where the ZnO nanoparticles act as "molecular gates" that modulate the hole-carrier density and scattering effects within the graphene network. The optimized ZnO:Gr-1.0% sensor demonstrates exceptional performance toward NO gas, achieving a remarkable response of 1120% at 14.3 ppm – a 20-fold enhancement over pristine ZnO – along with an ultra-low detection limit of ∼14 ppb. These findings offer significant insights into interfacial engineering for designing high-sensitivity, low-power, room-temperature gas sensors for advanced environmental monitoring.
