Thorium-based compounds are considered promising fuel materials for advanced nuclear reactors. The aim of this work is to study the structural, electronic, phonon, bonding, mechanical, thermophysical, and radiation damage properties of ThX (X = C, N, P, As, Sb, O, S, Se) compounds and to find the most suitable compound for high-temperature nuclear applications. The optimized lattice parameters agree well with available experimental and theoretical data, and the negative formation energies show that all compounds are thermodynamically stable. Electronic band structure and density of states analyses reveal metallic behavior across the series. The phonon dispersion curves confirm the dynamical stability of ThC, ThN, ThP, ThO, and ThS. Furthermore, bond population and charge density analyses indicate mixed ionic–covalent bonding, with covalent character most pronounced in ThC. Elastic constants satisfy the Born stability criteria, with all compounds except ThC (borderline) exhibiting ductile behavior. For the first time, the lattice thermal conductivity (using Slack’s equation) and diffusion thermal conductivity of the compounds have been evaluated. Thermophysical assessments identify ThN as the most promising candidate for high-temperature energy applications, exhibiting the highest Debye temperature (389.5 K), melting point (~2919 K), and lattice thermal conductivity (18.38 W m⁻¹ K⁻¹ at 300 K), alongside minimal thermal expansion. IRAD-based radiation damage analysis revealed that ThC and ThN show the deepest hydrogen penetration (~0.055 cm), with a maximum hydrogen concentration of ~ 0.015% and maximum damage of ~0.0015 dpa, and their defects are more uniformly distributed. Based on these results, ThN is found to be the most suitable compound among the studied ThX series for nuclear fuel applications, followed by ThC.
Hydraulic stimulation is widely viewed as a key enabling technology for superhot enhanced geothermal systems (EGS), yet its effectiveness in rocks approaching the brittle-to-ductile transition (BDT) remains uncertain. In this regime, stimulation is not simply the creation of fractures in a brittle medium; permeability development is governed by the coupled effects of high temperature, evolving deformation mechanisms, fluid state, silica chemistry, and cooling-induced stress redistribution. This review synthesizes laboratory, numerical, theoretical, and field evidence to evaluate what hydraulic stimulation can create in superhot BDT rocks, why stimulation-induced permeability may degrade during operation, and what long-term feedbacks may develop over reservoir lifetimes. Existing evidence indicates that permeability may form through cloud-fracture networks, hydroshearing, distributed semi-ductile damage, and thermal microfracturing, but these processes do not necessarily produce durable transmissivity. Hydrothermal alteration, pressure solution, silica precipitation, nanoparticle clogging, aperture closure, and cooling-front migration can modify or reduce permeability over timescales from hours to decades. Cooling plays a complex role: it may improve near-wellbore injectivity through thermoelastic fracture opening, while promoting delayed fault reactivation at greater distances and longer times. Field evidence remains limited because drilling, completion, and wellbore-integrity challenges have often prevented full reservoir-scale validation. Overall, hydraulic stimulation should not be regarded as a standalone solution to the BDT challenge. Rather, BDT-controlled rock behavior determines whether stimulation can generate permeability that is useful, sustainable, and manageable. Future research should prioritize field-scale validation of permeability-stabilization criteria, scale-up of laboratory-observed fracture architectures, and long-term forecasting of cooling-driven mechanical feedbacks.
The proliferation of autonomous pricing algorithms in digital markets has sparked intense debate regarding their potential to facilitate collusive outcomes, yet their behavior under realistic demand uncertainty remains underexplored. This study comprehensively analyzes four pricing algorithms-Q-Learning, Particle Swarm Optimization, Double Deep Q-Network, and Deep Deterministic Policy Gradient-across three canonical duopoly market structures (Logit, Hotelling, Linear) under autoregressive demand shocks. Through profit- and price-based collusion indices, we systematically disentangle the interactive effects of algorithmic architecture, market structure, and demand uncertainty. Our findings reveal that supra-competitive pricing emerges as a joint property of algorithm-market pairings rather than algorithm class alone, with DDPG exhibiting the most pronounced collusive tendencies. Critically, none of the algorithms meaningfully adapt to unobservable persistent shocks; observed changes in collusion indices under volatility primarily reflect benchmark displacement rather than altered pricing conduct. Demand shocks produce divergent market-level responses: Logit markets experience significant performance deterioration, Hotelling markets remain resilient, and Linear markets exhibit shock-induced profit inflation without corresponding price changes. Heterogeneous competition reveals persistent asymmetries that widen under volatility. These results underscore the primacy of market structure in governing algorithmic competition under uncertainty within the calibrated settings examined in this study, and carry important implications for the regulation of autonomous pricing systems.
Advanced oxidation processes (AOPs), particularly photocatalysis, have proven to be highly effective process for phenol degradation. In this study, a novel TiO 2 -based photocatalyst was developed for phenol degradation from aqueous solution under UV light (365 nm). The catalyst was synthesized via the sol-gel method, and the composite contained 5 wt% of NiFe 2 O 4 and 95 wt% of TiO 2 -SiO 2 . Characterization techniques including XRD, FESEM with EDX, FTIR, UV-Vis spectroscopy, and VSM were performed to reveal crystallinity, surface morphology, surface chemistry, bandgap and magnetic properties. XRD peaks confirmed the successful integration of spinel NiFe 2 O 4 (a ≈ 8.35 Å) into the anatase TiO 2 -SiO 2 phase (a ≈ 3.79 Å, c ≈ 9.51 Å) with an average crystallite size of 5.55 nm. FESEM revealed a rough, spherical and partially agglomerated morphology, while FTIR verified composite formation through characteristic Ti-O-Si, Ti-O-Ti, Ni-O, and Fe-O vibrational bands. Under the catalyst comparison condition (pH 7), the NiFe 2 O 4 /TiO 2 -SiO 2 nanocomposite achieved 97.6% phenol removal, which was further increased to complete removal (100%) after optimization of solution pH to 5. It also showed a narrow band gap of 2.40 eV, whereas TiO 2 showed 3.2 eV, displaying a redshift. Furthermore, VSM analysis revealed a saturation magnetization of 1.48 emu/g, indicating that the composite retains a measurable magnetic response. Under the optimized laboratory conditions (0.5 g/L catalyst dose, pH 5, and 10 ppm initial phenol concentration), complete phenol removal was achieved under UV irradiation within 240 min. Kinetic study revealed that phenol degradation follows a pseudo first order kinetic model as the best fit. These findings demonstrate the potential of NiFe 2 O 4 /TiO 2 -SiO 2 as a magnetic photocatalytic material for phenol removal under the investigated laboratory conditions. Further studies on catalyst reusability, real wastewater performance, mineralization, and environmental impact assessment are required to evaluate practical applicability.
