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The negative impact of transport on climate has led to incentives to increase the amount of renewable fuels used in internal combustion engines (ICEs). Oxygenated, liquid biofuels are promising alternatives, as they exhibit similar combustion behaviour to gasoline. In this article, the effect of the different biofuels on engine efficiency, combustion propagation and emissions of a gasoline-optimised direct injected spark ignited (DISI) engine were evaluated through engine experiments. The experiments were performed without any engine hardware modifications. The investigated fuels are gasoline, four alcohols (methanol, ethanol, n-butanol and iso-butanol) and one ether (MTBE). All fuels were tested at two speed sweeps at low and mid load conditions, and a spark timing sweep at low load conditions. The oxygenated biofuels exhibit increased efficiencies, even at non-knock-limited conditions. At lower loads, the oxygenated fuels decrease CO, HC and NOx emissions. However, at mid load conditions, decreased volatility of the alcohols leads to increased emissions due to fuel impingement effects. Methanol exhibited the highest efficiencies and significantly increased burn rates compared to the other fuels. Gasoline exhibited the lowest level of PN and PM emissions. N-butanol and iso-butanol show significantly increased levels of particle emissions compared to the other fuels.

A series of PFDPP copolymers based on fluorene (F) and diketopyrrolopyrrole (DPP) monomers were synthesized via direct arylation polycondensation using Fagnou conditions which involved palladium acetate as catalyst (a gradual catalyst addition of three different percentages were used), potassium carbonate as the base, and neodecanoic acid in N, N-dimethylacetamide. This synthesis provides a low cost compared with traditional methods of transition-metal-catalyzed polymerization. Among the different amounts of catalyst used in the present work, 12% was optimal because it gave the highest reaction yield (81.5%) and one of the highest molecular weights (Mn = 13.8 KDa). Copolymers’ chemical structures, molecular weight distributions, and optical and thermal properties were analyzed. The linear optical properties of PFDPP copolymers resulted very similarly independently to the catalyst amounts used in the synthesis of the PFDPP copolymers: two absorptions bands distinctive of donor–acceptor copolymers, Stokes shifts of 41 nm, a good quantum yield of fluorescence around 47%, and an optical bandgap of 1.7 eV were determined. Electronic nonlinearities were observed in these copolymers with a relatively high two-photon absorption cross-section of 621 GM at 950 nm. The dynamics of excited states and aggregation effects were studied in solutions, nanoparticles, and films of PFDPP. Theoretical calculations modeled the ground-state structures of the (PFDPP)n copolymers with n = 1 to 4 units, determining the charge distribution by the electrostatic potential and modeling the absorption spectra determining the orbital transitions responsible for the experimentally observed leading bands. Experimental and theoretical structure–properties analysis of these donor–acceptor copolymers allowed finding their best synthesis conditions to use them in optoelectronic applications.

A methanol island, powered by solar or wind energy, indirectly captures atmospheric CO2 through the ocean and combines it with hydrogen gas to produce a synthetic fuel. The island components include a carbon dioxide extractor, a desalinator, an electrolyzer, and a carbon dioxide-hydrogen reactor to complete this process. In this study, the optimal locations to place such a device in the Mediterranean Sea were determined, based on three main constraints: power availability, environmental risk, and methanol production capability. The island was numerically simulated with a purpose built python package pyseafuel. Data from 20 years of ocean and atmospheric simulation data were used to “force” the simulated methanol island. The optimal locations were found to strongly depend on the power availability constraint, with most optimal locations providing the most solar and/or wind power, due to the limited effect the ocean surface variability had on the power requirements of methanol island. Within this context, optimal locations were found to be the Alboran, Cretan, and Levantine Sea due to the availability of insolation for the Alboran and Levantine Sea and availability of wind power for the Cretan Sea. These locations were also not co-located with areas with larger maximum significant wave heights, thereby avoiding areas with higher environmental risk. When we simulate the production at these locations, a 10 L s−1 seawater inflow rate produced 494.21, 495.84, and 484.70 mL m−2 of methanol over the course of a year, respectively. Island communities in these regions could benefit from the energy resource diversification and independence these systems could provide. However, the environmental impact of such systems is poorly understood and requires further investigation.

Many kinds of defects are present in the different layers of GaN-on-Si epitaxy. Their study is very important, especially because they play a significant role on the device characteristics. This paper investigates the cause of the temperature dependence of the output and Miller capacitance at three temperatures: 25 °C, 75 °C and 150 °C of GaN-on-Si power transistors. In particular, this study focuses on the temperature dependence of the depletion voltage seen in these characteristics due to the progressive depletion of the two-dimensional electron gas (2DEG) under the device field plates. First, variations of the epitaxial growth are studied, showing that the intrinsic carbon concentration does not play a significant role. Secondly, the deep acceptor trap origin of the temperature dependence is analyzed with a TCAD simulation study. Thirdly, by adjusting TCAD parameters and binding them with experimental concentrations to fit experimental data, trap properties were obtained. The comparison of these properties with the acceptor traps in the literature suggests that the origin is a gallium vacancy tied to oxygen atom(s) on the N site.

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks.

Today, wide bandgap (WBG) GaN semiconductors are considered the future, allowing the improvement of power transistors. The main advantage of GaN is the presence of two-dimensional electron gas (2Deg) typically used as a conduction layer in normally-on and normally-off transistors. Concerning the normally-off family, several solutions are proposed. Among these, one of the most promising is the MIS-Gate technology that features a gate recess architecture allowing the semiconductor to physically cut off the 2Deg and drastically decrease gate–source leakage currents. The Vth relaxation characteristic, after voltage stress, has been investigated. It has been shown that the main impact is due to charges close to the gate dielectric/GaN interface, precisely dwelling within the dielectric or the GaN epitaxy. This work provides an analytical model of the Vth evolution of these MIS-GATE (metal insulator semiconductor gate) transistors fabricated on GaN-silicon substrate. This model allows the extraction of different trap energy levels from a temporary threshold voltage (Vth) shift after 650 V stress. Based on this method, it is possible to identify up to four different trap energy levels. By comparing state of the art methods, we show that these obtained energy levels are well correlated with either magnesium and carbon impurity or Ga and/or N vacancy sites in the GaN epitaxy.

Building energy consumption is still one of the main contributions to global carbon emissions. With the overall digitalization in the building sector, building automation and control systems (BACS) are to play a more important and key role in improving the building sector performance. A well-designed BACS at the building design phase with a high level of control functionalities is not a guarantee for efficient building operation and successful control and management strategies in the operational phase. Thus, a systematic automated initial and retro-commissioning process is key to test the performance of the automation system and the response of the integrated HVAC systems. This is an arduous and time-consuming task susceptible to human errors. As an alternative, the current study proposes a methodological framework to automate step response testing of BACS and to optimize the different steps of this process in a cost-effective way. In addition to newly built buildings, the framework can be applied in existing or retrofitted medium to large-sized buildings that have a building management system capable of receiving actuator commands and responsible to provide updates of several state variables. Based on the proposed framework, a first-of-its kind tool “AUSTRET” for building automated step response testing of BACS is designed and developed. The tool provides the necessary input configuring parameters, building system selection, and output results for each performed test. The framework aims to act upon ventilation, room heating and cooling, and water heating and cooling modules in a building. The implementation and demonstration of the AUSTRET in a medium-sized building case study for two different building systems are presented and evaluated: (1) Ventilation/fan, (2) Room heating. The results show the different dynamic responses on these two systems and how misleading input parameter configuration can invalidate step response tests. The preliminary results highlight the capability of using AUSTRET as a key component in both building initial and retro-commissioning applications.