Kumar, Jitendra

Doping ZnFe2O4 with Magnesium (Mg) and Manganese (Mn) promotes beneficial defects by tuning the bandgap and enhances the electronic conductivity, along with improvement in the electrochemical performance. Incorporating these dopants optimizes the crystal structure and provides additional active sites, leading to a superior rate capability and improved charge storage capacity compared to pristine ZnFe2O4 for the supercapacitors (SCs). Zn(1−x)MgxFe(2−x)MnxO4 (ZMFMO), with x = 0.00, 0.05, and 0.10, was synthesized using the sol–gel autocombustion technique and confirmed with the physical characterizations, including the detailed X-ray diffraction (XRD), Raman and UV–visible spectroscopy, field emission scanning electron microscopy (FESEM), and energy dispersive X-ray spectroscopy (EDX) analysis. Brunauer–Emmett–Teller (BET) isotherms were analyzed, which show the impact of doping on the surface area. The electrochemical performance of the prepared symmetric coin cell and three-electrode system was explored using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charging and discharging (GCD) studies. The three-electrode characterization suggests high specific capacitance (Csp) of 121.5, 130.2, and 162.5 F g−1 for ZMFMO_00, ZMFMO_05, and ZMFMO_10, respectively, while the Csp was found to be 34.8, 36.4, and 47.3 F g−1 at 1 mV s−1 for ZMFMO_00, ZMFMO_05, and ZMFMO_10 in symmetric coin cells, respectively. It also shows excellent charge–discharge characteristics with a 23.9, 28.6, and 43.6 s discharging time for ZMFMO_00, ZMFMO_05, and ZMFMO_10, respectively, at 392 mA g−1. It illustrates the energy and power density 5.01, 5.08, 8.19 Wh kg−1 and 29.52, 28.09, 28.11 W kg−1 at 39 mA g−1 for ZMFMO_00, ZMFMO_05, and ZMFMO_10 symmetric coin cell devices, respectively. Ex situ XRD analysis was performed to check the electrode stability after long-term cycling. The results indicated that the developed doped nanomaterial exhibits excellent charge storage characteristics, followed by an LED light demonstration. © 2025 John Wiley & Sons Ltd.

The increasing scale of deep learning models in high-energy physics research has posed significant challenges for their deployment on low-power, latency-sensitive platforms, such as FPGAs and ASICs used in trigger systems, as well as CPUs and GPUs employed in offline data reconstruction and processing pipelines. In this context, top-quark tagging, a key task in precision measurements and searches for new physics at the Large Hadron Collider (LHC), provides a representative benchmark due to its stringent latency and accuracy requirements under extremely large data volumes. In this work, we introduce BitParT, a 1-bit Transformer-based architecture designed specifically for top-quark tagging. Building upon recent advances in ultra-low-bit large language models, we extend these ideas to the collider-physics domain by developing a binary-weight variant of the Particle Transformer (ParT) model. Our results indicate that this approach enables a substantial reduction in model size and computational complexity while maintaining high tagging performance. We benchmark BitParT on the public Top Quark Tagging Reference Dataset and show that it achieves performance competitive with its full-precision counterpart. This work demonstrates that extreme quantization can provide a practical route toward real-time inference in collider experiments with minimal and optimized resource usage. © The Author(s) 2026.