International Journal of Energy Research

The development of energy-dense, thermomechanically stable, and low-viscous phase change emulsions (PCMEs) is proposed as an alternative thermal energy storage solution for building air conditioning. A set of oil-in-water (O/W) nanoemulsions with hexadecane concentration varied between 10, 20, 25, 30, 35, and 40 wt. % is prepared and characterized with respect to their physical, thermal, and rheological properties. The storage characteristics are evaluated in terms of storage density, phase transition behaviour, supercooling, and dynamic viscosity. A systematic comparison in terms of energy density between the PCMEs and water is carried out at different temperature conditions. For this purpose, the storage break-even temperature is proposed as a novel parameter to determine suitable operating temperature ranges and cycling conditions. The cycle stability is evaluated by rheological measurements, applying thermomechanical loads to the samples for a high number of cycles. According to the results, the energy density of the PCMEs is always higher than that of water, when the minimum temperature used for the cycling is below the storage break-even temperature. The emulsion with 30 wt. % hexadecane fraction is considered particularly promising, thanks to its high stability when exposed to thermomechanical stress, relatively low viscosity between 10 and 22 mPa s (0–30°C), and a storage density of 98 MJ/m³ within a cycling temperature range of 12 K.

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

Large-scale development of low-cost, efficient, and stable powder electrocatalysts for the hydrogen evolution reaction (HER) in alkaline solutions is a key step toward commercial hydrogen production. Herein, a hierarchical NiMo-MoO_3-x hollow nanotube (NiMo-MoO_3-x-HNT) with bifunctional groups as an efficient HER electrocatalyst was strategically invented using an MoO₃ nanorod (MoO₃−NR) as the precursor. The synthesis mechanism of the NiMo-MoO_3-x-HNT is established based on in-depth investigations using diffraction, spectral, and microscopy results. Experimental results suggest that the NiMo-MoO_3-x-HNT exhibits excellent electrocatalytic HER performance in 1 M KOH, with a low overpotential of 26.5 [email protected] mA cm^-2 and a small Tafel slope of 32.6 mV dec^–1. These values are comparable to those of cutting-edge electrocatalysts based on platinum-group metals. Moreover, it exhibits an almost unaffected cyclic stability over 50,000 cycles and robust durability over 98 h. This remarkable performance is attributed to its bifunctional groups and the porous hierarchical hollow nanotubular morphology. In summary, this study proposes a novel, efficient, and cost-effective strategy for the development of noble metal-free, high-performance HER electrocatalysts.

In this work, we report a novel 2D/2D InVO₄/BiVO₄ heterojunction nanocomposite constructed through a simple hydrothermal approach. The morphological analysis demonstrated that BiVO₄ nanoflakes to be anchored on the surface of InVO₄ nanosheets. The InVO₄/BiVO₄ heterojunction nanocomposite displayed increased light absorption promoting their visible light harvesting. The photoactivity of the prepared InVO₄/BiVO₄ heterojunction was explored for the degradation of tetracycline (TC) under light treatment. InVO₄/BiVO₄ heterojunction exhibited superior photocatalytic activity than that of bare InVO₄ and BiVO₄. The as-prepared InVO₄/BiVO₄ heterojunction photoanodes achieved a higher photocurrent density of 60 μA/cm² under illumination in 0.5 M Na₂SO₄ electrolyte. The enhanced photoelectrochemical performance is attributed to the synergistic effect and interface formation between InVO₄ and BiVO₄ and can effectively promote the charge separation and transfer of photoinduced carriers in heterojunction.

Developing the ecofriendly and high-fidelity electrocatalysts for the oxygen evolution reaction (OER) is essential to foster effective production of environmentally friendly hydrogen. Herein, we fabricated the highly efficient OER electrocatalysts of the activated carbon-decorated vanadium pentoxide (AC-V₂O₅) nanocomposites using a facile hydrothermal technique. The AC-V₂O₅ nanocomposites displayed an aggregated structure of the AC nano-sheet-anchored orthorhombic V₂O₅ nanorods. When performing the OER process in an alkaline electrolyte at 10 mA/cm², AC-V₂O₅ exhibited the low overpotential (~230 mV), small Tafel slope (~54 mV/dec), and excellent stability. These substantial OER performances of AC-V₂O₅ could be ascribed to the synergistic effects from both the electrochemically active V₂O₅ nanorods and the highly conductive AC nanosheets. The results infer that the AC-V₂O₅ nanocomposites possess a substantial aptitude as a high-performance OER electrocatalyst for production of the future green energy source—hydrogen.

Interwell connectivity assessment in polymer-driven reservoirs is critical for setting appropriate injection rates and improving oil recovery. Traditional deep learning techniques often lack accuracy and reliability when applied to short-term oilfield production data. In response, the A-LSTM algorithm is proposed, which integrates the attention mechanism with a long- and short-term memory network (LSTM). The predictive accuracy of A-LSTM is assessed and juxtaposed with LSTM and support vector regression (SVR) algorithms for short-term single-well daily oil production analysis. The Huber loss function was utilized to quantify the difference between predicted and actual results, resulting in a dynamic production prediction model. An interwell connectivity (IWC) assessment model is then obtained by fusing the dynamic production prediction model with the EFAST method, thus demonstrating the superior prediction accuracy of A-LSTM in oil production prediction and connectivity assessment. Moreover, the credibility of the assessment is further corroborated through numerical simulations and interwell tracer tests. The study results showed that the interwell connectivity evaluation model based on the A-LSTM algorithm and EFAST method is not only capable of accurately predicting the single-well daily oil production using a small sample dataset but also a highly reliable method for interwell connectivity evaluation, and the application of the interwell connectivity assessment model can further guide polymer flooding work in oilfields.