Aisha Jilani

Lithium–oxygen (Li–O₂) batteries offer exceptionally high theoretical energy density, but their practical development remains limited by sluggish oxygen redox kinetics, insulating discharge products, parasitic reactions, transport losses, and poor electrode durability. This review develops an engineering framework linking four coupled cathode-design strategies: conventional electrocatalysis, single-atom catalysis, carbon-free or carbon-minimized hosts, and multiscale structural and morphological engineering. A structured narrative assessment of the literature through 2026 is used to compare how these strategies influence ORR/OER kinetics, electronic and oxygen transport, Li₂O₂ nucleation and decomposition, interfacial stability, and electrode-level manufacturability. Particular emphasis is placed on the trade-offs that arise when catalytic activity, conductivity, porosity, chemical stability, mass loading, and fabrication complexity are optimized simultaneously. The review identifies engineering design principles for integrated cathodes and evaluates remaining barriers associated with thick-electrode transport, lean-electrolyte operation, air contaminants, scalable fabrication, standardized testing, and long-term cycling. The resulting framework provides an engineering-oriented roadmap for translating high-performing Li–O₂ cathode concepts from laboratory demonstrations toward more realistic energy-storage devices.

High-temperature proton-exchange membrane fuel cells (HT-PEMFCs) generate useful thermal energy that can be recovered for additional power production. This study investigates an integrated HT-PEMFC–organic Rankine cycle (ORC) system by combining response surface methodology (RSM) with thermodynamic energy analysis. A 17-run response-surface design was used to quantify the effects of pressure, temperature, and current density on polarization voltage. Power density was derived directly from the RSM-predicted voltage using P_d = iE to preserve physical consistency. The electrochemical model was benchmarked against published phosphoric-acid-doped polybenzimidazole HT-PEMFC polarization data under comparable conditions. The constrained optimization identified an operating condition of 400 kPa, 443 K, and approximately 1.198 A cm⁻², giving a predicted voltage of 0.5395 V and a power density of approximately 0.6462 W cm⁻². This represents a 12.9% increase in power density relative to the adopted reference condition. Separately, the reference thermodynamic case produced 13.08 kW of gross HT-PEMFC stack electrical power and 15.45 kW of thermal output assumed available to the ORC. The available legacy R409A reference case was evaluated at an evaporator pressure of 2 MPa, yielding approximately 1.24 kW of ORC net power and a net thermal efficiency of about 8.02%. The resulting combined modeled electrical output was approximately 14.32 kW before unmodeled balance-of-plant auxiliary power consumption, with the ORC contribution corresponding to about 9.5% of the gross HT-PEMFC stack output. The results demonstrate the complementary potential of physically consistent HT-PEMFC operating-condition optimization and waste-heat recovery, while the ORC results remain specific to the retained R409A reference dataset.

Flash point is a critical safety parameter indicating the lowest temperature at which a flammable liquid mixture can ignite. Accurate flash point estimation is essential for hazard prevention in chemical processing and fuel handling, yet experimental determination is time-consuming, costly, and hazardous. This study presents a combined thermodynamic and machine learning methodology to predict flash points of binary organic mixtures. A Liaw–UNIFAC thermodynamic model was used to generate vapor pressure and activity coefficient inputs, which were then used to train an Artificial Neural Network (ANN) for flash point prediction. The ANN model, configured with four hidden layers (10-20-10-5 neurons), captures complex non-linear relationships between mixture composition, molecular properties, and flash point. Model evaluation against literature data for eight diverse binary mixtures (including alcohols, alkanes, aromatics, and ketones) demonstrates high accuracy: the ANN’s flash point predictions show mean squared errors (MSE) below 0.1 and R2 above 0.99 in most cases, closely matching both experimental results and the Liaw–UNIFAC model. The ANN approach offers comparable reliability to the mechanistic Liaw model while significantly improving computational efficiency and adaptability. These findings highlight the potential of hybrid thermodynamic ANN modeling to enhance process safety by enabling rapid, accurate flash point estimation for complex mixtures without exhaustive physical testing.