Florida Institute for Sustainable Energy

Electrocatalytic water-splitting is a cost-effective and environmentally friendly method for producing high-purity hydrogen using renewable energy sources. Developing efficient and economical catalysts for the hydrogen evolution reaction (HER) is deemed essential for minimizing energy loss in water splitting. We propose that the NiSe2/MoSe2@rGO nanocomposite performs as an exceptional electrocatalyst for the HER and oxygen evolution reaction (OER) in an alkaline seawater electrolyte, exhibiting great activity and stability. The NiSe2/MoSe2@rGO nanocomposite-coated nickel foam (NF) electrode demonstrates an exceptionally low overpotential of 254 mV to reach a current density of 10 mA/cm2, with the Tafel slope value of merely 225 mV/dec for the HER in alkaline seawater electrolyte. Similarly, it exhibits an overpotential of 350 mV at the 10 mA/cm2 current density, with a Tafel slope of only 94 mV/dec for the OER, both of which are inferior to those of NiSe2, MoSe2, and GO catalysts. Furthermore, the very flexible material exhibits no significant catalytic degradation after 12 and 24 h of amperometric i-t curves at current densities of 20 and 40 mA/cm2, thereby affirming its exceptional flexibility and durability under extreme conditions. The NiSe2/MoSe2@rGO nanocomposite demonstrated a remarkable capacitance of 41.8C/g, surpassing the performance of NiSe2, MoSe2, and GO catalysts. Additionally, the GCD stability over 4000 cycles indicated exceptional stability at a current density of 10 A/g when utilizing the optimally performing NiSe2/MoSe2@rGO nanocomposite coated NF electrode. Consequently, the synthesized material shows promise as an effective electrode material for high-efficiency energy generation and storage applications.

Solar desalination has become a viable way to address the world's water shortage using renewable energy. This review comprehensively analyzes solar desalination systems to examine the several modified designs that can improve their efficiency. Incorporation of reflectors, wick materials, different types of fins, absorbers, solar collectors, and heat localization materials into the solar desalination system has been studied in detail. The incorporation of solar collectors with a desalination system provides the dual advantage, i.e., enhancing the water productivity and simultaneously producing the hot water which can be utilized for different household chores. A comparison of the efficiencies for different developed solar desalination systems with and without reflectors was also carried out. The use of nano-materials/biomaterials to enhance the performance of solar desalination systems and their impact on improving evaporation rates and overall system efficiency was also reviewed. To achieve the sustainable development goals through solar desalination, and comprehensive search for the bibliometric analysis has been carried out through the Scopus database. Furthermore, solar desalination potential for wastewater treatment is discussed, highlighting its feasibility for industrial and domestic applications. The review also explores the integration of artificial intelligence in optimizing system performance, predictive modelling, and process automation. This study provides a comprehensive evaluation of solar desalination systems, offering insights into scalable, cost-effective solutions and future direction for the sustainable management of water resources.

Doping copper iodide (CuI) is a promising strategy to enhance its performance as a hole transport layer in perovskite solar cells. In this study, lanthanum was introduced as a rare-earth dopant to preserve the wide band gap of CuI, 3.1 eV, while significantly improving its morphological, structural, optical and electrical properties. The incorporation of La3+ dopants induces a grain-boundary pinning effect that restricts excessive grain growth, yielding dense films with reduced porosity and defect states. This structural refinement correlates with a 14 % increase in conductivity and reduced surface resistivity. X-ray photoelectron spectroscopy confirms the successful substitutional La3+ incorporation at the Cu vacancies without altering the oxidation states of Cu and I, preserving the intrinsic electronic structure of CuI. The device-level validation using SCAPS-1D simulations demonstrates that while undoped CuI produces a modest power conversion efficiency of 18.04 %, La-doped CuI enables a substantial improvement to 31.46 %, owing to improved interfacial band alignment in a standard PSC structure. These results highlight La doping as an effective strategy for optimizing CuI and position La-doped CuI as a highly promising HTL for high-efficiency PSCs.

Iraq's persistent power outages necessitate innovative energy management solutions, a gap often overlooked in existing smart grid research focusing on more developed regions. This research proposes and evaluates an IoTbased Energy Management System (EMS) tailored for Iraq, addressing both microgrid and large-scale applications. The core algorithm, Prioritized Proportional Distribution with Time-Based Adjustment (PPD-TBA), utilizes proportional allocation, excess redistribution, and fairness-driven time adjustments. The methodology combines simulation of a six-house microgrid, assessing PPD-TBA performance under a 4.5 kW power deficit, with practical experimentation on a large-scale load mimicking a government building (350 kWh daily threshold) experiencing blackouts. Simulation results demonstrated the EMS's ability to mitigate microgrid power outages with superior fairness (Curtailed Energy Index sigma = 0.05), outperforming traditional methods. Practical implementation showed effective management of large-scale energy consumption within the set 350 kWh threshold, significantly reducing peak demand charges and stabilizing supply despite blackouts. The study confirms the potential of the proposed IoT-based EMS to enhance energy management in Iraq. Its adaptability and effectiveness in constrained environments highlight its viability for improving energy access and resilience. Future work should focus on integrating renewables, advanced prediction, and scalability for wider deployment.

Building-integrated photovoltaics (BIPV) are central to energy-efficient buildings, yet forecasting irradiance and power with operational practicality remains challenging. This review analyzes 70 studies on machine learning approaches for BIPV forecasting and organizes the literature by task and deployment needs. For irradiance forecasting, the survey catalogs common contexts (fa & ccedil;ade/roof, sky/season, urban geometry), input modalities (telemetry, weather, imagery), model families, and frequently reported error metrics. For power forecasting, horizons from intra-hour to seasonal are compared, distinguishing direct power modeling from approaches that incorporate irradiance estimates. Beyond accuracy, the review examines feasibility and robustness, which determine field viability-namely, latency and memory budgets for edge versus cloud execution, sensitivity to outdoor variability, and risks to generalization across sites and seasons. The review concludes with recommended directions focused on implementable next steps, including shared data/metadata conventions for comparability, methods that can be transferred across buildings and seasons, interpretable operations for building-level decision workflows, and pipelines designed for edge execution. The resulting map of models, inputs, and constraints enables readers to match techniques to forecasting tasks and operational contexts.