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Present study employes a picosecond laser (532 nm) for selective P1 laser scribing on the indium tin oxide (ITO) layer and subsequent fine‐tuning of P1 scribing conditions with machine learning (ML) techniques. Initially, the scribing is performed by varying different laser parameters and further evaluate them via an optical microscope and two probe resistivity measurements. The corresponding scribing width and sheet resistance data are used as input databases for ML analysis. The classification and regression tree (CART)‐based ML analysis revealed that median pulse energy 5.7 μJ, APL > 35%, LSO > 46%, and processing speed ≥1250 mm s−1 gives ≥16 μm of scribing width. Further, the decision tree (DT) analysis showed that pulse energy of ≥8.1 μJ, and LSO ≥ 37% are required for electrically isolated lines. The feature importance score suggests that laser fluence and pulse energy determined the scribing width, whereas electrical isolation strongly depends on LSO and processing speed. Finally, the ML achieved conditions experimentally validated and reassessed via scanning electron microscope, and atomic force microscopy aligns well with optical microscope measurements.

Air is an important medium for all living beings and is essential for the well-being of all. Monitoring of air is important to know the quality of air. The air quality monitoring was carried out in Kolhapur City under the National Air Monitoring Program. The present study involves the assessment of PM10 as described in the National Ambient Air Quality Standards (NAAQS). The source apportionment study related to particulate matter was carried out in Kolhapur City. The study also determined the average PM10 concentration in the city as it will be useful for preparing an action plan to reduce PM10 concentration. PM10 concentration was calculated as per the standard method adopted by CPCB. Sampling was carried out for 8 hours in three shifts twice a week at each sampling site for three consecutive years. Mahadwar Road (MR) and Dabholkar Corner (DC) were selected per the surrounding residential area, population density, and traffic conjunction. The third site Shivaji University (SUK), was selected as a control site. The results indicated that the PM10 level has risen above the prescribed standards of NAAQS. The reason for the rise in PM10 may be due to fossil fuel burning, construction activity, vehicles, and unpaved roads. The Analysis of Variance (ANOVA) technique is used to check the equality of the mean concentration of PM10 at these three locations and found a significant difference between mean concentrations of PM10, suggesting increased particulate matter.

Abstract 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.

In the Kesterite family, the Cu2ZnSn(S,Se)4 (CZTSSe) thin-film solar cells (TFSCs) have demonstrated the highest device efficiency with non-stoichiometric cation composition ratios. These composition ratios have a strong influence on the structural, optical, and electrical properties of the CZTSSe absorber layer. So, in this work, a machine learning (ML) approach is employed to evaluate effect composition ratio on the device parameters of CZTSSe TFSCs. In particular, the bi-metallic ratios like Cu/Sn, Zn/Sn, Cu/Zn, and overall Cu/(Zn+Sn) cation composition ratio are investigated. To achieve this, different machine learning algorithms, such as decision trees (DTs) and classification and regression trees (CARTs), are used. In addition, the output performance parameters of CZTSSe TFSCs are predicted by both continuous and categorical approaches. Artificial neural networks (ANN) and XGBoost (XGB) algorithms are employed for the continuous approach. On the other hand, support vector machine and k-nearest neighbor’s algorithms are also used for the categorical approach. Through the analysis, it is observed that the DT and CART algorithms provided a critical composition range well suited for the fabrication of highly efficient CZTSSe TFSCs, while the XGB and ANN showed better prediction accuracy among the tested algorithms. The present work offers valuable guidance towards the integration of the ML approach with experimental studies in the field of TFSCs.

The photocatalytic degradation of environmentally non-benign, toxic organic pollutants such as bisphenol A (BPA), brilliant green (BG), or mixture of dyes have been carried out using g-C3N4-TiO2 (GNT) nanocomposites. The GNT nanocomposites were synthesized by using hydrothermal method with different compositions and these nanocomposites were characterized using the different techniques. X-ray diffraction revealed that the anatase phase of TiO2 has been retained in composites; while characteristic reflection of g-C3N4 at 27.07° (d = 3.22 Å) is not observed due to its lower content in the nanocomposites. Raman spectra confirms the formation of composites between TiO2 with g-C3N4. Furthermore nano-scale dimensions of the bare or composites have been proved by FE-SEM and HR-TEM analysis. X-ray photoelectron spectroscopy (XPS) shows the presence of C, N, Ti and O as a constituents, with peaks due to CC, NCN of g-C3N4. Among the different nanocomposites, g-C3N4-TiO2 catalyst having 30% g-C3N4 and 70% TiO2 in molar proportion (i.e. 30-GNT) is exhibiting the highest efficiency for degradation of the different dyes in correlation to its higher surface area, lower optical band gap as well as more visible-light absorption (i.e., λ > 400 nm) in the electromagnetic spectrum. Keywords: Nanocomposites, TiO2, g-C3N4, Photocatalytic degradation, Bisphenol A, Brilliant green