Poly-generation with less negative environmental impacts is a key thrust for chemical industrial sustainability. In this paper, a chemical industrial complex with zero harmful emissions for simultaneous production of nitric acid, ammonia, steam, hydrogen, and oxygen has been developed, modeled, and virtually evaluated. The complex (called A3NS) employs air and water as feedstock without fossil fuels and intermediate chemicals, which comprises five subsystems: (i) alkaline water electrolysis process (AWEP), (ii) air separation process (ASP), (iii) ammonia manufacturing process (AMP), (iv) nitric acid process (NAP), and (v) selective catalytic reduction process (SCRP). Through effective mass, heat, and power exchanges among five subsystems, the A3NS complex creates a highly integrated industrial ecosystem that can accomplish poly-generation with zero harmful emissions. Rigorous process simulation and comprehensive economic analysis have been conducted to demonstrate the technological efficacy, environmental benefits, and economical potential of the developed A3NS complex.
Organisational learning is a double-edged sword because it is contextual and subject to interpretation by the top managers. Top managers’ cognitions influence how they interpret and process organisational learning, and how they deploy resources. Drawing on organisational learning and upper-echelon theories, we argue for the joint, complementary effects of a firm’s cross-border acquisition (CBA) experience and the chief executive officer’s (CEO) cognitive orientation, measured by their international experience and tenure at the firm, on the pace of CBAs. The results from analyses of an unbalanced panel of 1583 observations across 369 CBAs from 205 Indian MNEs support our hypothesis. Interestingly, we find negative effects of the firm’s CBA experience and mixed effects of the CEO’s cognitions. However, the joint effect is positive and stronger, as hypothesised. We mainly contribute to the CBA literature by demonstrating the role of ‘experience cognition complementarity’ on the CBA pace.
Generative AI has transformed how we search for and process information in both personal and professional environments. Despite its rapid diffusion, the specific factors driving or hindering individual adoption remain underexplored. To bridge this gap, we conducted a qualitative study focused on ChatGPT and identified a range of technological, personal, organizational, and social factors influencing users' acceptance, resistance, and ambivalence. We also examined emotional, experiential, and ethical responses that explain divergent usage trajectories. Building on these insights, we propose a comprehensive framework that maps the constructs and relationships that shape generative AI adoption, resistance, and ambivalence. Further, we highlight a series of paradoxes (e.g., simplicity vs. complexity, technophobia vs. technophilia, techno-optimism vs. techno-pessimism, overdependence vs. independence, and mandatory vs. volitional use) that collectively complicate human-generative AI interactions. This study extends technology adoption literature by incorporating emergent themes specific to generative AI while offering practical implications for AI designers and policymakers.
LuFeO3 (LFO) is a perovskite oxide with promise for optical and electroceramic applications. In the present study, LFO and Co-substituted compositions (LFO, LuFe0.95Co0.05O3, and LuFe0.90Co0.10O3) were synthesized by a conventional solid-state route and characterized by SEM, Raman spectroscopy, diffuse reflectance, and broadband dielectric/impedance measurements. Co substitution alters the powder microstructure, yielding more irregular agglomerates composed of finer sub-units than undoped LFO. Dielectric spectra showed that the loss tangent (tanδ), the dissipation factor, of investigated samples was below 1 over the studied temperature–frequency window. It was seen that Co substitution decreased dielectric loss in the mid-to-high frequency region but raised low-frequency loss at advanced temperatures. The real part of impedance Z^' declined with both temperature and frequency, and Nyquist plots displayed depressed arcs, indicating non-Debye behavior with distributed grain and grain-boundary contributions. Arrhenius analysis of relaxation maxima yielded activation energies of ∼ 0.32–0.76 eV, consistent with oxygen-vacancy energies. Raman spectra revealed Co-induced lattice perturbations, including mode broadening/attenuation and red-shifts in the 200–600 cm− 1 range, together with a strengthened stretching feature near 600–650 cm− 1. Kubelka–Munk analysis was utilized to determine the band gaps of the studied samples, 2.19 eV (LFO), 2.29 eV (5
Autonomous mobile robots are transforming industries from e-commerce logistics to field exploration, but their effectiveness depends on onboard energy storage. This study addresses the challenge of selecting optimal battery technologies for autonomous mobile robots, balancing performance, energy efficiency, thermal stability, and cost across diverse applications. We focus on lithium-ion, lithium-polymer, and nickel-metal hydride batteries, the most common power solutions, each with distinct advantages and disadvantages in energy density, form factor, thermal stability, and cost. A dynamic modeling and simulation framework in MapleSim evaluated these chemistries under defined and representative operating conditions, tracking state of charge and temperature during charging and discharging. A Particle Swarm Optimization algorithm evaluated 37 battery configurations by thermal stability, energy efficiency, and cost across five use cases. Key results indicate that for logistics and warehousing, lithium nickel manganese cobalt oxide with graphite is optimal; for healthcare, lithium nickel manganese cobalt oxide with lithium titanate oxide excels; for manufacturing, lithium nickel cobalt aluminum oxide with graphite leads; for agricultural robots, lithium manganese oxide with graphite is best; and for exploration and mining, lithium iron phosphate with graphite is most reliable. These results provide a structured basis for battery selection, showing how simulation-driven, multi-criteria decision-making enhances energy management and operational reliability.
