Majid Ali

TiFe‐based alloys are attractive solid‐state hydrogen storage materials but their practical application is restricted by the formation of a stable surface oxide layer, which necessitates high‐temperature activation before the first hydrogenation. In this study, the synergistic effects of Mn and Ni substitution with activated carbon (AC) on the activation behavior and hydrogen storage performance of TiFe‐based alloys are systematically investigated. The mechanically milled TiFe‐based alloys are further characterized using X‐ray diffraction (XRD), scanning electron microscopy coupled with energy‐dispersive spectroscopy (SEM/EDS), and differential scanning calorimetry (DSC). First‐cycle hydrogen absorption behavior of metal alloys is evaluated by Volumetric pressure–composition–temperature (PCT) measurements at 25°C and 5 MPa using Isorb HP‐1. Results showed that TiFe‐based alloys exhibited direct first‐cycle hydrogen absorption without the conventional high‐temperature activation treatment, revealing the effectiveness of mechanical activation in overcoming the surface oxide barrier. DSC analysis showed that Mn‐, Ni‐, and AC‐modified alloys reduced the hydride decomposition temperature from ≈400°C to 320°C. Among the investigated compositions, TiFe _0.8 Ni _0.2 + AC _0.4 exhibited the highest first‐cycle hydrogen uptake of 1.7 wt.% at 25°C and 5 MPa. These findings demonstrate that the combined effects of mechanical activation, transition‐metal substitution, and AC effectively eliminate the need for conventional activation while enhancing the first‐cycle hydrogen storage performance of TiFe‐based alloys, providing a practical strategy for room‐temperature solid‐state hydrogen storage.

Activated coolant in light water reactors generates significant radiation fields impacting occupational safety and plant design. This study presents a high-fidelity, dynamic model quantifying the activated coolant source term in the 330 MWth System-integrated Modular Advanced Reactor (SMART), an integral small modular reactor (SMR). A comprehensive isotopic inventory of key activation products 16N, 17N, 19O, 14C, 3H, 24Na, 38Cl, and 32P is established under normal operating conditions. Analysis reveals that 16N dominates short-lived activity, reaching 7.59×1013 Bq in the primary coolant, accounting for 96.8% of nitrogen-based activation. Tritium (3H) production exhibits pronounced operational mode dependence on chemical shim operation, yielding 3.49×1012 Bq, which is 18% higher than mechanical shim operation (2.96×1012 Bq) after a complete fuel cycle. Fission process contributes approximately 85% to the total 3H inventory. A comprehensive static and dynamic sensitivity analysis, perturbing parameters by 1.0%, identifies coolant flow rate, removal efficiency, and reactor power as the most influential parameters governing 3H behavior, with sensitivity coefficients of -0.969, -0.969, and +0.855, respectively. Dynamic sensitivity reveals that parameters related to 10B exhibit 72-88% temporal variability, necessitating explicit time-dependent treatment. Novel Shannon entropy analysis of 14 tritium-bearing state variables quantifies pathway-specific complexity. Chemical shim variables maintain constant dynamic entropy (11.34 bits), mechanical shim variables show monotonically increasing entropy (11.05 → 11.50 bits), and fission-to-gap pathways exhibit the highest variability (10.70 → 11.50 bits). Total system entropy stabilizes at 158.66 ± 0.31 bits within 10 days, demonstrating rapid equilibrium despite 3H pathway complexity.

The healthcare sector is a significant contributor to global energy consumption, particularly within the heating, ventilation, and air conditioning (HVAC) market, due to stringent requirements for maintaining indoor thermal comfort for patients, staff, and visitors in hospital wards, rooms, and intensive care units. This study presents a novel approach employing a Machine Learning-based Linear Regression Algorithm to predict indoor adaptive thermal comfort within the inpatient medical wards. The methodology establishes a robust correlation between key indoor environmental parameters including air temperature, relative humidity, and air velocity, and thermal comfort indices, including Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD). Real-time measurements of indoor environmental conditions were conducted in selected hospitals in Islamabad, Pakistan, utilizing calibrated sensors to capture ambient temperature, wet bulb globe temperature, relative humidity, air velocity, light intensity, and CO₂ levels. This empirical data was integrated with responses from thermal comfort questionnaires, assessing the perceptions of patients, medical staff, and visitors regarding thermal sensation, acceptability, preference, and overall comfort. The adaptive ML-based, predictive analysis identified optimal thermal comfort ranges for hospital wards, recommending indoor air temperatures between 22.0°C and 23.0°C, relative humidity levels between 50% and 55%, and air velocities between 0.1 and 0.2 m/s. The findings revealed a significant impact of overcooling and undercooling on PMV and PPD levels, emphasizing the need for precise HVAC system control to enhance both energy efficiency and occupant comfort. This research contributes to advancing adaptive thermal comfort modeling in healthcare facilities, offering insights for sustainable HVAC management and improved patient outcomes.