Analysis and Prediction of Hydrothermally Synthesized ZnO-Based Dye-Sensitized Solar Cell Properties Using Statistical and Machine-Learning Techniques

Structural, Optical, and Magnetic Properties of Pure and Ni-Fe-Codoped Zinc Oxide Nanoparticles Synthesized by a Sol-Gel Autocombustion Method

Pure and Ni-Fe-codoped Zn1 - 2xNixFexO (x = 0.01, 0.02, 0.03, and 0.04) nanoparticles were effectively synthesized using a sol-gel autocombustion procedure. The structural, optical, morphological, and magnetic properties were determined by using X-ray diffraction (XRD), ultraviolet-visible (UV-vis), scanning electron microscopy, and vibrating sample magnetometer techniques. The XRD confirmed the purity of the hexagonal wurtzite crystal structure. XRD analysis further indicated that Fe and Ni successfully substituted the lattice site of Zn and generated a single-phase Zn1-2xNixFexO magnetic oxide. In addition, a significant morphological change was observed with an increase in the dopant concentration by using high-resolution scanning electron microscopy. The UV-vis spectroscopy analysis indicated the redshift in the optical band gap with increasing dopant concentration signifying a progressive decrease in the optical band gap. The vibrating sample magnetometer analysis revealed that the doped samples exhibited ferromagnetic properties at room temperature with an increase in the dopant concentration. Dopant concentration was confirmed by using energy-dispersive X-ray spectroscopy. The current results provide a vital method to improve the magnetic properties of ZnO nanoparticles, which may get significant attention from researchers in the field of magnetic semiconductors.

Effects of activation overpotential in photoelectrochemical cells considering electrical and optical configurations

Photoelectrochemical cells (PECs) are a promising option for directly converting solar energy into chemical energy by producing hydrogen (H₂) gas, thus providing a clean alternative to consuming fossil fuels. H₂ as fuel is free from any carbon footprints and negative environmental impacts. Therefore, the H₂ production, especially directly using sunlight in PECs, is critically important for the rapidly growing energy demand of the world. Although promising, PECs are inefficient and must overcome a few inherent losses in producing H₂-the most important being the activation overpotential (ηa) required for splitting water. This work analyzes the impact of ηa on solar-to-fuel efficiency (ηSTF) and H₂ production rate (HPR). This work also discusses choosing appropriate photo-absorbing materials based on their energy bandgaps and suitable electrode pairs to achieve desired ηSTF and HPR for different electrical and optical PEC configurations. Significant changes are observed in ηSTF and HPR when ηa is considered in water splitting.

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques

In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled

The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.

Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers

In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R 2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.