Two series of non-hydrogenated diamond-like carbon (DLC) films and molybdenum doped diamond-like carbon (Mo-DLC) films were grown on the silicon substrate using direct current magnetron sputtering. The influence of molybdenum doping (between 6.3 and 11.9 at.% of Mo), as well as the deposited temperature (between 185 and 235 °C) on the surface morphology, elemental composition, bonding microstructure, friction force, and nanohardness of the films, were characterized by atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDX), Raman spectroscopy, and a nanoindenter. It was found that the increase in the metal dopant concentration led to a higher metallicity and graphitization of the DLC films. The surface roughness and sp³/sp² ratio were obtained as a function of the Mo concentration and formation temperature. The nanohardness of DLC films was improved by up to 75% with the addition of Mo. Meanwhile, the reduction in the deposition temperature decreased the nanohardness of the DLC films. The friction coefficient of the DLC films was slightly reduced with addition of the molybdenum.

The increasing integration of renewable energy sources increases the variability and uncertainty of power systems, requiring advanced prediction-based control strategies. This paper proposes an integrated AutoML–MPC framework for a hybrid renewable energy system (HRES) combining solar and wind generation, biomass, battery energy storage, and a hydrogen chain (electrolyzer and fuel cell). Short-term load and generation forecasts are made using H2O AutoML models, and the energy flow allocation is optimized using model-based control (MPC) formalized in the form of mixed-integer linear programming (MILP). The objective function minimizes electricity imports from the grid and the associated ${\mathrm{C}\mathrm{O}}_2$ emissions, subject to technological constraints. The results obtained showed a clear distribution of short-term (battery) and long-term (hydrogen) storage functions in time: during periods of excess generation, the electrolyzer operated close to nominal mode, and in the deficit phase, the fuel cell was activated, reducing the need for grid imports. The battery ensured fast short-term balancing, while the hydrogen system compensated for the longer-term energy shortage. The forecast models were characterized by high accuracy ($R^2>0.98$), which allowed for reliable planning of energy flows over the MPC horizon. The proposed methodology allows for effective coordination of storage technologies of different time scales, maximum use of renewable generation and reducing the system’s dependence on the external grid.

Non-hydrogenated diamond-like carbon (DLC) films and molybdenum-doped diamond-like carbon (Mo-DLC) films were deposited by direct current magnetron sputtering. The formation was carried out on Si (100) wafers. The influence of molybdenum concentration and deposition temperature on the surface morphology, chemical composition, type of chemical bonds, friction force at nanoscale, and nanohardness of the DLC coatings were investigated by atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and nanoindenter, respectively. The concentration of molybdenum in the films varies from 1.2 at.% to 10.3 at.%. The increase in molybdenum content promotes the graphitization of DLC films, lowering the sp³ site fraction and increasing the oxygen content, which contributes to the reduction in nanohardness (by 21%) of the DLC films. The decrease in the synthesis temperature from 235 °C to 180 °C enhanced the oxygen amount up to 20.4 at.%. The sp³ site fraction and nanohardness of the Mo-DLC films were enhanced with the reduction in the deposition temperature. The film deposited at a substrate temperature of 235 °C exhibited the lowest friction coefficient (CoF) of 0.03, where its molybdenum concentration was 1.2 at.%. The decline in the synthesis temperature increased the CoF of the Mo-DLC films up to seven times.

This paper presents a forecasting and optimization framework for the control of a hybrid renewable energy system (HRES) integrating solar, wind, and biomass generation with lithium-ion batteries, electrolyzers, and fuel cells. A bidirectional long short-term memory (bi-LSTM) neural network model was applied for renewable generation and load forecasting, while the deep Q-network (DQN) and soft actor–critic (SAC) algorithms were used for real-time supervisory control of energy storage and hydrogen-based components. The HRES was formulated as a Markov decision process (MDP), where the agents optimize battery charging/discharging, electrolyzer activation, and fuel cell operation under dynamically changing operating conditions. Experimental results demonstrated that the SAC agent achieved more stable learning dynamics and superior operational performance compared to the DQN agent, maintaining an HRES energy imbalance below 0.5 MWh while reducing unnecessary component switching and improving overall system stability. The obtained results confirm the potential of deep reinforcement learning for adaptive and low-emission supervisory control of complex hybrid renewable energy systems.