In cases where a dye-sensitized solar cell (DSSC) is exposed to light, thermal energy accumulates inside the device, reducing the maximum power output. Utilizing this energy via the Seebeck effect can convert thermal energy into electrical current. Similar systems have been designed and built by other researchers, but associated tests were undertaken in laboratory environments using simulated sunlight and not outdoor conditions with methods that belong to conventional data analysis and simulation methods. In this study four machine learning techniques were analyzed: decision tree regression (DTR), random forest regression (RFR), K-nearest neighbors regression (K-NNR), and artificial neural network (ANN). DTR algorithm has the least errors and the most R², indicating it as the most accurate method. The DSSC-TEG hybrid system was extrapolated based on the results of the DTR and taking the worst-case scenario (node-6). The main question is how many thermoelectric generators (TEGs) are needed for an inverter to operate a hydraulic pump to circulate water, and how much area is required for that number of TEGs. Considering the average value of the electric voltage of the TEG belonging to node-6, 60,741 pieces of TEGs would be needed, which means about 98 m² to circulate water.

Nano-enhanced membrane technology and deep eutectic solvents (DESs) have demonstrated effectiveness in addressing emerging environmental pollutants. This research centers on purifying water by removing heavy metals employing membranes enhanced with mesoporous silica and DES. Various DESs, including hexanoic acid, octanoic acid, and decanoic acid, were synthesized using tetrabutylammonium bromide (TBABr) as a base. The study combined a polysulfone-based membrane with mesoporous silica, aiming for efficient indigenous crafting to remove heavy metals. Mesoporous silica was blended with the synthesized DES solution, creating diverse membranes for heavy metal separation. The study characterized these membranes using various techniques such as scanning electron microscopy, atomic force microscopy, energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and contact angle measurements. Surface mapping confirmed the integration of silicon-based DES, reducing the membrane surface roughness from 4 to 1.4 nm. By adjusting the carboxylic acid chain length with TBABr and adding mesoporous silica, leaching ratios were reduced from 4.2 to 2.3%. Silica-grafted DES-based membranes exhibited a progressive increase in flux from 2.6 to 26.67 L/m ² h. The synthesized silicon-based membranes demonstrated outstanding performance, achieving rejection rates exceeding 80% for chromium and arsenic, maintaining an impressive 90% flux recovery ratio even at high flux rates. This study will envision the potential of nano-enhanced membrane technology utilizing DES for sustainable water purification and wastewater treatment applications to achieve the sustainable development goal (SDG) of clean water and sanitation (SDG-6).

The dye-sensitized solar cell (DSSC) is a promising candidate, offering an attractive substitute for conventional silicon-based photovoltaic technologies. The performance advantages of the DSSC have led to a surge in research activity reflected in the number of publications over the years. To deliver data-driven analysis of DSSC performance, machine learning models have been applied. As a first step, a literature-based database has been developed and after the data preprocesses, Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), xgboost (XGB), and Artificial Neural Network (ANN) algorithms were applied with stratified train-test splits. The performance of the models has been assessed via metrics, and the model interpretability relied on SHAP analysis. Based on the employed metrics and the confusion matrix, DT, RF, and KNN are the most accurate models for predicting DSSC efficiency on the developed dataset. Furthermore, it was revealed that synthesis temperature and the thickness of thin film were identified as the dominant drivers, followed by precursor and dye. Mid-tier contributors were morphological structure, electrolyte concentrations, and the absorption maximum. The results suggest that in optimizing the manufacturing process, targeted tuning of the synthesis temperature, the thickness of thin film, the precursor, and the dye are likely to improve the performance of the device. Therefore, experimental effort should concentrate on these factors.

Amongst the several alternative resources, renewable energy source (RES) can provide continuous and clean energy. Furthermore, RESs have received a worldwide attention and an outstanding role is attributed to them. The sun energy can be exploited using light energy conversion applications such as photovoltaics. Dye Sensitized Solar Cell (DSSC) is third-generation photovoltaic device and a forwardlooking, easily producible solar cell. Nevertheless, buying a commercially available DSSC, datasheet and material information are unavailable. Thus, the purchased DSSC is a black box. The main motivation is that to develop a MatLab application which is able to determine the material information and parameters of the Dye Sensitized Solar Cell from some incoming parameters. The goal of the study is to describe the peration of the developed MatLab application which calculated the photogeneration rate in function of the thickness of the cell, the open-circuit voltage, and to illustrate its operation via an example. The photogeneration rate is solved using the numerical finite element method as a numerical procedure. In our model the Beer-Lambert law is implemented. The model takes into consideration the measured parameters of the light such as photoncount-wavelength spectrum, transmissionwavelength spectrum and irradiation intensity-wavelength spectrum. Furthermore, the open-circuit voltage is an elementary electric parameter of a solar cell.