Abstract: One of the most promising approaches for replacing conventional power plants during the transition to clean energy is the conversion of existing coalfired power plants (CPPs) into nuclear power plants. This strategy offers numerous ecological and economic advantages. However, integrating a nuclear reactor with a steam turbine originally designed for a coal plant is far from trivial and involves significant technical challenges. The purpose of this work is to analyze and evaluate various options for coupling a HighTemperature GasCooled Small Modular Reactor (HTGR SMR) with a potentially suitable subcritical steam turbine from an existing CPP, thereby creating several repowering configurations. The main difficulties stem from the fact that the turbine was designed to operate with live steam at lower flow rates, temperatures, and pressures than those typically provided by an HTGR SMR. In addition, the feedwater temperature and pressure requirements for the HTGR SMR steam generator differ substantially from those in a CPP, leading at best to additional efficiency losses. Moreover, the overall thermal cycle layouts of the two systems are fundamentally different. Despite these challenges, technically feasible combinations can be achieved. However, determining which option is the most economically viable depends on numerous additional factors, including the specific characteristics of the individual CPP and the regulatory framework of the country in which it operates.

Abstract: Hydrogen enrichment of compression ignition (CI) engines has emerged as a promising strategy to simultaneously enhance thermal efficiency and reduce carbon-based emissions. This study numerically investigates how hydrogen enrichment affects engine performance and emissions in methanol-diesel dual-fuel CI engines, a combustion mode gaining increasing attention for replacing fossil diesel with sustainable fuels, particularly in hard-to-abate sectors such as maritime transport. The simulations are based on the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, incorporating the RNG k–ε turbulence model, the Eddy Dissipation Concept (EDC) for turbulence–chemistry interaction, and the G-equation for turbulent premixed flame propagation. The numerical model is validated against experimental data for in-cylinder pressure and heat release rate at 45% methanol substitution ratio (by energy). The results indicate that increasing the hydrogen enrichment ratio (HER, defined on an energy basis) from 5% to 20% raises the Sauter Mean Diameter (SMD) of the diesel fuel from 20.2 µm to 28.0 µm (+38%), driven by the reduction in gas-phase density and weakened Weber-number-controlled droplet breakup efficiency as hydrogen displaces charge oxygen. Furthermore, hydrogen's elevated adiabatic flame temperature and superior mass diffusivity intensify combustion, raising peak in-cylinder pressure from 75.2 to 79.1 bar (+5.2%), amplifying the peak heat release rate from 129 to 211 J/°CA (+63.6%), and elevating maximum in-cylinder temperature from 1542 to 1735 K (+193 K). These thermodynamic gains translate directly into a 6% improvement in indicated thermal efficiency and a 14% reduction in indicated specific fuel consumption (accounting for hydrogen, methanol, and diesel) at HER 20%. On the emissions front, CO₂ declines by 24% in direct proportion to the carbon-containing fuel mass displaced by hydrogen substitution, while NOₓ surges 3.52-fold through intensified Zeldovich thermal pathways. These findings establish hydrogen–enriched methanol–diesel dual-fuel combustion as a viable pathway toward high-efficiency, low-carbon CI engine operation, provided that targeted NOₓ mitigation strategies, such as exhaust gas recirculation (EGR) or optimized injection timing, are concurrently applied.

Abstract: After several critical reports by the Federal Audit Office on the implementation of the energy transition followed a cost estimation of €13.3 trillion by the Scientific Service of the German Bundestag. Political “lack of alternatives” and needed CO2 reduction led to an uncoordinated activism. The resulting promotion of state-funded NGOs and their lobbying pressure ultimately prevented a clear and stringent program management. The lack of scientific understanding regarding the importance of thermodynamic potential for demand-oriented supply failed to recognize a needed energy storage. Nature’s carbon cycle and the potential generation from CO2 and H2O to hydrocarbons were ignored, however this is the solution. The foreseeable financial collapse of the entire concept lies in the thermodynamically superfluous mandatory energy savings and the infrastructure rebuilding to avoid CO2 emissions. However, the use of renewable gaseous hydrocarbons as energy storage enables the continued use of the existing natural gas infrastructure and only requires recirculation of the raw material CO2 without further investments. The results already available today for the renewable feed-in proof the advantages of a solar-powered carbon cycle economy by the rising high costs of thCost Efficient e extensive regulatory measures for stabilizing the European grid. Finally the carbon cycle economy could save around €10 trillion and operating costs comparable to LNG.

Abstract: Aqueous pyrolysis liquid (APL) is the water‑rich by‑product of pyrolysis with high organic content; however, it is too dilute for economical fuel upgrading, so practical applications remain limited. This study investigates the feasibility of co-digesting hydrolyzed sewage sludge filtrate with APL in high-rate anaerobic reactors to enhance energy recovery and as a treatment step for APL. Two lab-scale reactors were operated for >500 days: one fed with filtrate and APL, and a control fed with filtrate only. Results show that APL can be degraded at loadings up to 0.3 g COD_APL/L/d (≈2.1% w/w) without severe inhibition, achieving 93% CODAPL removal and stable methane production. Compared to previous studies, this work demonstrates substantially higher APL degradation under co-digestion conditions. Higher loadings (≥0.44 g CODAPL/L/d) caused inhibition despite recovery attempts. Microbial analysis revealed a shift from acetoclastic to hydrogenotrophic methanogenesis, dominated by Methanobacterium and Methanosarcina, alongside enrichment of taxa linked to resistance to a toxic environment. Measurements of pollutant concentration in effluent indicated partial degradation of low-molecular-weight polycyclic aromatic hydrocarbons (PAHs) and transformation of some polyfluoroalkyl substances (PFAS), though most pollutants persisted in effluent. These findings highlight the potential of high-rate reactors for APL treatment and underscore the need for strategies such as inoculum acclimation, pretreatment, or additives to achieve industrial-scale loadings. The study provides insights into microbial resilience, pollutant fate, and operational considerations for integrated anaerobic digestion–pyrolysis systems.

Abstract: Regional electricity interconnections are increasingly recognised as enablers of cost-effective power system expansion and deep decarbonisation in emerging economies. In East Africa, Kenya and neighbouring countries — Tanzania, Ethiopia, and Uganda — operate relatively low-carbon electricity systems; however, rapidly growing electricity de-mand and expanding thermal generation are placing upward pressure on grid emissions intensity, particularly in Kenya. This study examines whether planned cross-border in-terconnections can mitigate this trajectory using OSeMOSYS Global, an open-source least-cost capacity expansion model, comparing stand-alone national power systems against an interconnected regional grid over 2022–2050. Results show that interconnec-tion enables access to low-cost renewable electricity and facilitates surplus generation ex-ports, maintaining system-wide carbon intensity within climate-finance eligibility thresh-olds of 100 gCO2/kWh. Outcomes are heterogeneous: Ethiopia and Kenya incur cost in-creases (+USD 481 million and +USD 568 million respectively) attributable to transmis-sion capital expenditure, whereas Tanzania and Uganda achieve net cost savings (−USD 590 million and −USD 891 million) alongside substantial emissions intensity reductions of 141.9 and 280.5 gCO2/kWh respectively. Regional emissions equity is preserved, with modest intensity increases in Ethiopia and Kenya offset by large reductions elsewhere. These findings strengthen the case for climate-financed regional transmission as a scala-ble and equitable mitigation strategy in East Africa.

Abstract: Improving the energy efficiency of existing residential buildings is a key challenge for reducing energy consumption in cold climate regions. A considerable proportion of the housing stock in Kazakhstan consists of Soviet-era multi-apartment buildings characterized by high heat losses and low thermal performance. The aim of this study is to assess heat losses and evaluate the energy-saving potential of a typical multi-apartment residential building located in a cold climate. A comprehensive energy audit was conducted, including an analysis of the thermal performance of building envelopes, calculation of heat losses through walls, windows, roof, and heating system pipelines, thermographic inspection, and air infiltration measurements using blower door testing. The results show that the largest share of heat losses occurs through external walls, windows, and uninsulated heating pipelines. The implementation of thermal modernization measures, such as wall insulation, window replacement, and pipeline insulation, can significantly reduce the building’s heat consumption and improve overall energy performance. The findings of this study demonstrate the high potential for improving the energy efficiency of existing residential buildings and may be useful for developing renovation strategies for similar buildings in cold climate regions.

Abstract: To achieve real-time and accurate detections of residual oil distribution during water or CO₂ flooding, this study utilizes the high-frequency Ground Penetrating Radar (GPR) for monitoring of the flooding process in real time. The U-Net neural networks are trained to invert for the subsurface dielectric constants and conductivity distributions. The study first utilizes the gprMax forward tool to simulate the dynamic response changes of rock electrical parameters during flooding and constructs a high-resolution training dataset of 100,000 samples. Each sample contains the relationships between a subsurface electrical parameter model and its corresponding multi-transmitter, multi-receiver GPR responses. A deep learning inversion network based on the U-Net architecture is trained to extract multi-scale features through an encoder-decoder structure, achieving an end-to-end mapping from GPR echo signals to subsurface electrical parameters. Numerical and physical core experimental results show that the method accurately inverts the electrical parameter distributions of the oil, water, and gas in the sandstone model, successfully capturing the position and morphology changes of the displacement front. The average relative error of dielectric constant inversion is controlled within 5%, with the error mainly concentrated in high-conductivity water regions for conductivity inversion results. Compared to traditional full waveform inversion methods, the proposed approach offers a fast inversion solution and is less affected by the initial model and noise. The results reveal the feasibility and superiority of the neural network based deep learning method in GPR electromagnetic inversion, providing a new method for real-time flooding monitoring and intelligent reservoir development during oil and gas flooding.

Abstract: Medium-to-long-term underground heat recovery systems often exhibit cumulative thermal imbalance that is not adequately described by classical local diffusion equations. This study develops a first-principles framework that links thermal engineering with non-equilibrium statistical physics. We derive a hybrid evolutionary generator that unifies local Gaussian diffusion and non-local fractional Lévy-type transport, enabling representation of cross-cycle memory and long-range correlation. Within the Onsager variational and Martin-Siggia-Rose (MSR) formalisms, cyclic thermal evolution is formulated as a gradient-flow process coupled with a thermodynamic conjugate information field. We further show that gradient phase change materials (PCMs) can modulate generator parameters toward a near scale-invariant regime associated with improved long-term stability. Based on this field structure, a path-integral adjoint optimal-control framework is established for periodic external heat-source operation. The proposed framework provides a physically consistent explanation for long-term thermal fading and a practical theoretical basis for sustainable underground heat recovery.

Abstract: Nationwide electrification and consistent power supply remain a forlorn dream in Nigeria, over a decade after the privatisation of the electricity sector. Most studies attribute it to corruption and weak institutions; however, these are just proximate causes. The real causes are underlying variables influencing the slowed metering penetration and poor revenue collection rates. This study examines all 11 DSICOS using monthly data of key performance indicators to assess each DISCOS’ post-privatisation metering and revenue performance. The study adopts an analytical framework comprising descriptive statistics for foundational analysis of key performance indicators (KPIs), Linear and Panel Fixed Effects (FE) regression, and Extension models to accurately quantify the conditional associations between key variables. The R2 values for the clustered robust standard errors (SEs) ranged from 0.715 to 0.879. The metering ratio is a strong positive and statistically significant determinant of revenue collected and collection rate. However, the metering impact trajectories are heterogeneous, with negative metering-collection efficiencies in Benin, Yola, Kaduna, and Enugu. The following structural archetypes were observed: Leaders (Ikeja, Eko, and Abuja); Mid-performers (Port Harcourt, Ibadan, and Kano); Laggers (Benin, Yola, Jos, Enugu, and Kaduna). Also, increasing the total customer base will not yield a proportional increase in energy billed or revenue growth, given the sector’s current overreliance on estimated billing. The value of this research lies in its sector-wide assessment of the DISCOS financial and metering profiles. It buttresses the argument for DISOC-specific frameworks and the dissolution of a uniform regulatory and operational strategy.

Abstract: The article discusses issues related to the possibility of using 10% hydrogen peroxide in ZS engine fuel to reduce exhaust emissions. The additive proposed by the authors of the article is a 12% hy-drogen peroxide solution. The article presents the results of engine and analytical tests carried out by the authors. The article mainly concerns the emission of toxic substances into the atmosphere in the case of an engine fuelled with standard and modified fuel. Laboratory tests showed that the cetane number increased by two units for the modified fuel. The results of engine tests showed that the fuel additive reduces the concentration of nitrogen oxides and hydrocarbons, whilst the soot content increased slightly.

Abstract: Due to high sensitivity of proton exchange membrane fuel cells (PEMFC) to feed gas contamination through balance of plant (BOP) materials, in-situ qualification plays a crucial role to secure performance, durability, and economic viability. To be able to deliver verified and accurate qualification results it is necessary to analyze the test method in detail and to perform repetitions on certain measurements. This work focuses on validation of an in-situ material qualification test method in terms of measuring precision on a previously developed test bench and statistical significance of collected data. As a statistical approach t-test was used to calculate confidence intervals based on a sample size of 15 reference measurements with the same parameters and setup but variable membrane electrode assemblies (MEA). The results show substantial reduction of confidence intervals with growing measurement’s sample size clearly quantifying accuracy of the analyzed methodology. The precision of the test method, as indicated by the calculated confidence intervals of irreversible voltage loss is approx. 1.21 mV, corresponding to a relative deviation of about 0.17 % with respect to the calculated mean value across all steady-state phases (SSPs). This approach also provides an insight into the natural degradation behavior of the tested MEAs. The calculated effects can serve as a basis for design of experiments (DOE) in future test series.

Abstract: The energy transition faces challenges associated with the integration of variable renewa-ble energy sources, the management of uncertainty, and the increasing complexity of en-ergy systems. In this context, artificial intelligence (AI) has gained a significant role, and more recently, generative AI has begun to be explored as a tool for the analysis and mod-eling of these systems. This study was conducted through a Scoping Review following the PRISMA-ScR guidelines, with the aim of mapping the current state of knowledge regard-ing the use of generative AI in the energy transition. The literature search was carried out in the IEEE Xplore, Scopus, Web of Science, and Springer Nature databases, applying pre-viously defined inclusion and exclusion criteria. Peer-reviewed studies published between 2020 and 2026 were included, forming a final corpus of 12 studies analyzed using a qual-itative and descriptive approach. The results show that generative AI is being applied across multiple areas of the energy transition. Key applications include the generation of alternative energy scenarios, the creation of synthetic data for model training and valida-tion, probabilistic uncertainty analysis, and the design of complex energy configurations in systems with high renewable penetration. Additional contributions involve forecasting and optimizing solar and wind resources, analyzing energy consumption patterns, plan-ning integrated and bioenergy systems, predictive maintenance of solar infrastructure, and improving energy storage and operational resilience in smart grids. Three main ap-plication areas were identified: scenario and data generation, optimization and opera-tional processes, and decision-support for intelligent energy systems. However, limita-tions remain, including limited real-world validation, data dependency, computational scalability challenges, and the lack of regulatory frameworks. The protocol was registered in the Open Science Framework (OSF) under code: 10.17605/OSF.IO/BYM7A.

Abstract: The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet a systematic quantitative overview of its scientific development remains limited. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a data-driven overview of research trends that can support future research prioritization and inform policy and strategic planning for integrated mobility-energy systems.

Abstract: In-cylinder pressure is a critical parameter for assessing the combustion process and per-formance of spark ignition internal combustion engines. However, obtaining measured in-cylinder pressure data across the full operating range, particularly at various spark advance angles (SA), is costly and technically demanding, limiting its widespread application in engineering practice. This study proposes two artificial neural network (ANN) based in-cylinder pressure reconstruction methods utilizing in-cylinder pressure and heat release rate data from a three-cylinder motorcycle gasoline engine under varying spark advance angle conditions, aiming to achieve cost-effective, high-precision pressure prediction through machine learning technology. Both pressure reconstruction methods employ crank angle and spark advance angle as input features. Method 1 (ANN-P) directly predicts the in-cylinder pressure curve, achieving a coefficient of determination R² exceeding 0.99 on both training and validation sets with a root mean square error (RMSE) below 0.13 bar, accurately reproducing the pressure evolution throughout the compression, combustion, and expansion processes while achieving high-precision prediction of indicated mean effective pressure (IMEP). Method 2 (ANN-HRR) adopts an indirect strategy of "predicting heat release rate followed by integration to reconstruct pressure." This method first derives the apparent heat release rate (HRR) from measured in-cylinder pressure data, trains a machine learning algorithm with HRR data to predict the target operating condition's HRR curve, then reconstructs in-cylinder pressure through integral inversion based on a single-zone thermodynamic model, thereby avoiding error amplification caused by differential operations. This approach demonstrates superior performance in predicting combustion characteristic points (CA10, CA50). The results demonstrate that both methods accurately capture the influence of spark timing on combustion phasing and peak pressure. Method 1 achieves high accuracy in predicted pressure curves but exhibits lower accuracy in capturing combustion characteristics; Method 2 effectively compensates for the limitations of Method 1 in characterizing combustion features through heat release rate curve prediction. This study provides an economical and efficient technical approach for gasoline engine combustion diagnosis, performance calibration, and control optimization.

Abstract: Namibia’s rural communities continue to experience limited and unreliable electricity access despite the country’s exceptional solar, wind, and biomass renewable energy re-sources potential. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, re-renewable based alternatives. This paper presents a resource-driven design and multi objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), Carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati using high-resolution, site-specific renewable resource datasets and community-level load forecasts. Results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behavior across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Com-pared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia.

Abstract: Cavitation in rotating hydraulic machinery -- such as industrial pumps and hydropower turbines -- can cause blade and casing erosion, excessive vibration, noise and efficiency loss, posing significant operational and economic risks across industrial sectors. Reliable and scalable monitoring strategies are therefore essential, particularly under variable operating conditions in real-world environments. Recent advances in machine learning (ML) and deep learning (DL) have enabled data-driven approaches for cavitation detection based on operational sensor signals, yet a structured synthesis of these developments is lacking. This scoping review systematically analyzes measurement-based ML and DL approaches for cavitation monitoring, with the aim of identifying key trends, challenges and future research directions. Following PRISMA-ScR and JBI guidelines, 52 peer-reviewed studies published between 1996 and 2025 were evaluated, covering laboratory and field investigations across pumps and turbines and a wide range of model architectures. The analysis reveals that most studies are laboratory-based (∼ 80%), focus on pumps (∼ 70%) and rely on single-machine datasets (> 80%), limiting generalization across machines and operating conditions. Classical ML approaches remain relevant due to interpretability and robustness with limited data, while DL enables end-to-end learning from raw or time-frequency transformed signals, frequently achieving diagnostic accuracy above 95%. Hybrid frameworks combining DL-based feature extraction with classical classifiers are increasingly adopted. Key limitations across the literature include domain shifts between laboratory and field data, scarce or inconsistent labeling and a predominant focus on categorical cavitation severity levels.

Abstract: Blast furnace gas (BFG) must be deeply purified when it is as fuel for combined-cycle power generation. To improve collection efficiency of the fine particulate dust in BFG by wet electrostatic precipitators (WESPs), this study implemented measures such as optimizing nozzle atomization performance and spatial distribution of droplets, along with adding chemical agglomeration agents and surfactants, These approaches pro-moted the chemical agglomeration of fine dust and enhanced dust collection efficiency. The results show that under overlapping spray conditions, the 1/8 solid cone nozzle produced the smallest droplets size with the most uniform spatial distribution, exhib-iting a d50 of 141.17 μm. When this nozzle was used in combination with guar gum (GG) as a chemical agglomerant, the d50 of BFG dust increased from 8.46 μm to 14.75 μm. The synergistic application of 5 mg/m³ sesbania gum (SBG) and 5 mg/m³ oc-tylphenol ethoxylate (OP-10) further increased the dust d50 to 19.08 μm. Using the 1/8 solid cone nozzle and with an XTG concentration of 5 mg/m³, resulted in the highest dust collection efficiency of 96.76%, while the synergistic use of SBG/OP-10 achieved an efficiency of 97.69%. This study elucidates the influence of nozzle atomization charac-teristics and spray liquid type on dust agglomeration and collection efficiency, providing both theoretical and practical foundations for the deep purification of blast furnace gas.

Abstract: This study evaluates the technical feasibility of deploying containerized oxy-combustion power modules with integrated CO₂ capture in remote Ecuadorian Amazon oil fields. Associated petroleum gas is conditioned with a 35 wt.% diethano-lamine (DEA) sweetening stage specifically implemented to remove H₂S and reduce acid-gas loading prior to combustion, improving fuel quality and protecting down-stream equipment while increasing methane mole fraction for combustion. System ef-ficiency is governed by stoichiometric oxygen demand, with methane requiring 2 mol O₂/mol fuel and hexane requiring 11 mol O₂/mol fuel; favoring methane-rich streams reduces ASU energy demand, enhances combustion performance, and lowers separa-tion costs. The combined oxy-combustion cycle attains a thermal efficiency of 33.10% and an exergetic efficiency of 39.98%. Major energy penalties arise from the cryogenic air separation unit and the CCS train, yet operational tuning of CO₂ recirculation and steam flow could raise thermal efficiency by up to 2%. The ASU produces oxygen at 96.67% purity with an energy consumption of 0.385 kWh/kg O₂, while the CCS achieves 99.99% CO₂ capture at 0.41 kWh/kg CO₂. Sourcing gas from three production blocks provides flexibility to accommodate supply variability. The modular 272 MW unit demonstrates viability for off-grid power supply, routine flaring reduction, and scalable acid-gas valorization in frontier oilfields.

Abstract: Two-dimensional electron gases (2DEGs) in complex oxide heterostructures provide a powerful platform for enabling nanoscale energy-conversion mechanisms. In this work, we investigate LaAlO₃/SrTiO₃/LaTiO₃ (LAO/STO/LTO) interfaces as an engi-neered architecture for electricity generation, exploiting the combined effects of LAO-induced polar discontinuity, STO’s high-κ dielectric response, and LTO-driven electronic reconstruction. This trilayer system generates a strongly confined interfacial 2DEG with sheet densities in the 1013–1014 cm−2 range, enabling strong interaction with external electromagnetic fields and efficient charge displacement. To quantitatively capture the physics governing 2DEG formation and energy extraction, we develop a self-consistent analytical model solving the Schrödinger–Poisson equations. The SP module resolves quantum confinement, band bending, and carrier distribution at the LAO/STO/LTO interface, while the analytical engine models electromagnetic excitation (sheet charge density ns), field enhancement, and dynamic charge response across the heterostructure. This approach enables simultaneous evaluation of subband structure, interfacial potential, plasma-resonance behavior, and field-induced current genera-tion. Simulation results demonstrate that the asymmetric LAO/STO/LTO stack pro-duces a deep quantum well on the STO side, promoting strong 2DEG confinement and enhanced sensitivity to THz–IR excitation; under illumination the 2DEG exhibits res-onant carrier modulation, enhanced drift displacement, and energy-transfer pathways conducive to electricity generation. We additionally incorporate temperature depend-ence into the model and find monotonic increases in sheet charge density and conduc-tivity with temperature (measured at zero bias for 10 nm films), with LTO showing metallic-like, strongly temperature-dependent transport, STO exhibiting modest ther-mally activated behavior, and LAO remaining effectively insulating but most sensitive in relative terms—effects that alter subband occupancy, screening, and resonance con-ditions. The model clarifies how layer thickness, dielectric contrast, interface polariza-tion, and temperature jointly govern energy-conversion efficiency. Overall, the vali-dated Schrödinger–Poisson framework provides a predictive tool for optimizing oxide 2DEG power-generating structures and positions LAO/STO/LTO heterointerfaces as promising candidates for tunable, nanoscale energy-harvesting devices.

Abstract: We present a parametrised charging infrastructure model developed to support the design of a hybrid-electric zero-emission vessel with corresponding charging infrastructure for operation along the Norwegian Coastal Express route. The charging model includes functionalities to analyse the required battery storage capacity and power ratings and locations of charging facilities for achieving battery-electric operation. We demonstrate the use of the charging model to analyse different zero-emission scenarios for the Norwegian Coastal Express route. In the presented example scenarios, the model takes as input the estimated energy demand for a new zero-emission vessel design for the Coastal Express in different weather conditions, and includes functionality to consider realistic port stays based on existing timetables and historical data of delays. The analyses show minimal required battery and illustrate a trade-off between charging power and battery capacity, as well as exemplifying the impact of different timetables as well as historic deviations on charging and energy delivered from the battery. The charging model presented is general and can be used for other routes than the Norwegian Coastal Express, as a tool for decision-makers to optimize for battery-electric operation whilst keeping the need for onboard storage capacity and charging infrastructure installations at a minimum.