Combustion behavior of KOH desulphurized coals assessed by TGA-DTG

This work presents the results obtained from the experimental study on the effects of KOH treatment and its combustion behavior of high sulfur Indian coal. Coal was treated with 5–20% KOH (v/v) concentration for 6–24 h reaction time to identify the effects of KOH treatment on coal properties. Experimental results showed that upto 36.79% of total sulfur can be removed from coal with 20% KOH concentration and 24 h contact time at atmospheric condition. However, gross calorific value of coal decreased from 6800 to 6084 kcal/kg due to removal of combustibles from coal. Combustion characteristics of treated coal were assessed by Thermo-gravimetric analysis/ Differential thermo-gravimetric (TGA/DTG analysis). Further various combustion kinetic parameters like ignition temperature, peak temperature, burnout temperature, activation energy (E), and pre-exponential factor (A) are estimated. Experimental results show that the ignition temperature of coal decreased from 321°C to 252°C, peak temperature decreased from 459°C to 409°C due to changes in the coal matrix after desulfurization. The activation energy of coal calculated decreased from 79 to 45 kJ/mol due to desulfurization using 20% KOH concentration and 24 h reaction time.     

Detection of latent fingerprints using luminescent Gd0.95Eu0.05PO4 nanorods

Lanthanide ions doped luminescent materials are widely studied for latent fingerprint detection. However, most of these materials are synthesized at very high temperatures and use UV C light for visualization, which is harmful to eye, skin, etc. Herein, the Gd_0.95Eu_0.05PO₄ nanorods synthesized by a simple co-precipitation method at 185 °C were reported for latent fingerprint visualization under 395 nm light. The Gd_0.95Eu_0.05PO₄ nanomaterial has monoclinic crystal structure and shows rod-shaped morphology. Further, these Gd_0.95Eu_0.05PO₄ nanorods exhibit excellent photoluminescence properties and strong fuchsia emission under UV light. These nanorods have been employed for developing latent fingerprints on various porous and non-porous substrates by the powder dusting technique, which exhibits clear and well defined details with high contrast, selectivity and sensitivity under 395 nm UV light. Latent fingerprints developed after 72 h of their deposition also show clear contrast with these nanorods. Therefore, the Gd_0.95Eu_0.05PO₄ nanorods can be used for latent fingerprint visualization applications.     

Synthesis and Characterization of Novel CoCr2O4@GeO2@ZnO Core–Shell Nanostructure: Focus on Electrical Conductivity and Gas Sensing Properties

Journal of Inorganic and Organometallic Polymers and Materials - An effort has been made to develop and synthesize novel CoCr2O4@GeO2@ZnO core–shell nanostructure gas sensor via sol–gel...     

Machine Learning Assisted Analysis,Prediction, and Fabrication of High-Efficiency CZTSSe Thin Film Solar Cells

The Earth-abundant element-based Cu₂ZnSn(S,Se)₄ (CZTSSe) absorber is considered as a promising material for thin-film solar cells (TFSCs). The current record power conversion efficiency (PCE) of CZTSSe TFSCs is ≈13%, and it's still lower than CdTe and CIGS-based TFSCs. A further breakthrough in its PCE mainly relies on deep insights into the various device fabrication conditions; accordingly, the experimental–oriented machine learning (ML) approach can be an effective way to discover key governing factors in improving PCE. The present work aims to identify the key governing factors throughout the device fabrication processes and apply them to break the saturated PCE for CZTSSe TFSCs. For realization, over 25,000 data points were broadly collected by fabricating more than 1300 CZTSSe TFSC devices and analyzed them using various ML techniques. Through extensive ML analysis, the i-ZnO thickness is found to be the first, while Zn/Sn compositional ratio and sulfo-selenization temperature are other key governing factors under thin or thick i-ZnO thickness to achieve over 11% PCE. Based on these key governing factors, the applied random forest ML prediction model for PCE showed Adj. R² = >0.96. Finally, the best-predicted ML conditions considered for experimental validation showed well-matched experimental outcomes with different ML models.