Cascaded Multi-level Inverter Topology Developed from a Modified H-bridge

In this paper, a single-phase cascaded multi-level inverter topology is proposed. It is basically developed from a modified H-bridge module. This topology reduces the switch count, the gate drive requirement, and voltage stress. The significant advantages of the proposed inverter are modular structure, simpler control, and lower number of switches. The Nearest Level Control algorithm is employed to generate the gating signals for the power switches. To verify the performance of the proposed structure, simulation results are carried out by MATLAB (The MathWorks, Natick, Massachusetts, USA)/SIMULINK. Experimental results are presented to validate the simulation results.     

A fault‐tolerant modular multilevel inverter topology

Multilevel inverter topologies have received an increasing importance for their modular structures with better integration of renewable energy sources and reduced filtering requirements. But reliability is compromised in the process of decreasing the number of switches to produce the maximum number of voltage levels. This paper elevates a single‐phase fault‐tolerant inverter topology, which is modular in structure. The proposed inverter is analyzed for open‐ or short‐circuit faults in sources and open‐circuit faults in switches. Sine Pulse Width Modulation with multicarriers technique is used to control the circuit. The carrier signals are reconfigured under fault conditions based on levels to be generated by bypassing the faulted switch or source. The circuit is simulated in MATLAB/SIMULINK, and experimental setup is developed to claim the fault tolerance of proposed inverter.     

Modeling and optimization of process parameters for supercritical CO2 extraction of Argemone mexicana (L.) seed oil

This research article deals with the determination of optimal conditions of extraction parameters (e.g. temperature (60–100?°C), pressure (200–350?bar), particle size (0.5–1.0?mm), flow rate-CO₂ (5–15?g/min), and the % of co-solvent (0.0–10% of flow rate-CO₂) resulting to the optimal cumulative extraction yield during the supercritical fluid extraction of Argemone mexicana (L.) seed oil with and without a modifier (ethanol) using a supercritical carbon dioxide as solvent. A “five-factors-three-levels” Box-Behnken design under the response surface methodology was used to show independent and interactive effects of extraction parameters. A mathematical regression model was expressed properly by a quadratic second-order polynomial equation. The maximum oil yield (42.86%) from A. mexicana seeds was obtained with the optimal conditions (85?°C, 305?bar, 0.75?mm, 11?g/min, and 9% of flow rate-CO₂) of extraction parameters. The fatty acids analysis of the seed oil was done using gas chromatography and found its suitability as bio-fuel.     

A Promising Modified SILAR Sequence for the Synthesis of Photoelectrochemically Active Cu2ZnSnS4 (CZTS) Thin Films

A promising modified SILAR sequence approach has been employed for the synthesis of photoelectrochemically active Cu₂ZnSnS₄ (CZTS) thin films. To study the influence of sulfurization temperatures on the CZTS thin films, the CZTS precursor thin films were annealed at temperatures of 520, 540, 560, and 580 °C for 1 h in an H₂S (5 %)+Ar (95 %) atmosphere. These films were characterized for their structural, morphological, and optical properties using X-ray diffraction, Raman spectroscopy, field emission scanning electron microscopy, and UV-vis spectrophotometer techniques. The film sulfurized at an optimized temperature of 580 °C shows the formation of a prominent CZTS phase with a dense microstructure and optical band gap energy of 1.38 eV. The photoelectrochemical (PEC) device fabricated using optimized CZTS thin films sulfurized at 580 °C exhibits an open circuit voltage (V_oc) of 0.38 V and a short circuit current density (J_sc) of 6.49 mA cm⁻², with a power conversion efficiency (η) of 0.96 %.     

Doubling of photocatalytic H2 evolution from g-C3N4 via its nanocomposite formation with multiwall carbon nanotubes: Electronic and morphological effects

Nanocomposite of graphitic carbon nitride (g-C₃N₄) with optimum concentration of multiwall carbon nanotubes (MWCNTs) is shown to render 100% improvement in its photocatalytic activity for visible-light induced water splitting. Our study reveals that in addition to the charge transfer effects, morphological changes in g-C₃N₄ introduced by mild MWCNT reinforcement play a vital role in the photocatalytic activity. We present results of structural and opto-electronic characterizations in support of these inferences.     

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.     

Touchy feely vectors: A compensatory design approach to support model-based reasoning in developing country classrooms

Educational technology designs in developing countries mostly focus on making knowledge resources widely available, through MOOCs, repositories and computer-based tutoring. The use of digital media for cognitive augmentation, particularly interactive designs that help learners understand modelling topics in STEM, is underexplored. We report a 3-year design study examining this potential in the Indian context, testing two iterations of an interactive system, Touchy-Feely Vectors (TFV). The design supports learning vectors, a modelling topic pre-university students struggle with. Virtual lesson-plans were co-designed with teachers to augment — but not replace — their existing practices, and to address classroom and resource constraints. Pre-post testing of TFV-1 (a computer-based prototype) showed that it helped students develop a more integrated vector concept, and improved their reasoning. Field-implementation of TFV-2 (a textbook-linked touch-screen based design) in 6-classrooms (3-Control, N=135; 3-Experimental, N=131) showed that it fostered cognitive engagement in average-students and geometry-algebra integration (model-based reasoning) in good-students. These results, along with a requirements analysis based on textbooks, indicate that the interaction possibilities supported by different representational media critically shape student reasoning. The study also illustrates a systematic approach to design and test digital media systems that support cognitive augmentation in developing countries.     

Mitigation of harmonics in three-phase ac system using current injection technique for ac-to-dc converter

This paper presents a new approach to eliminate harmonics and to improve power factor of a three phase front-end uncontrolled rectifier. A high-power-factor is achieved by injecting high-frequency triangular current from the output of three-phase inverter. The high frequency (HF) current modulates the rectifier input voltage resulting in conduction of diodes into each switching cycle. The resulting ac input line current is continuous and sinusoidal in shape with significant reduction in current harmonics. All the switches are operated at zero-voltage switching (ZVS). The diodes of the rectifier are also operated with soft switching at turn-on as well as at turn-off. Varying switching frequency with fixed duty ratio regulates the output voltage. The proposed ac-to-dc converter also maintains high-power-factor even for unbalanced input supply voltages. The analysis of the converter is carried out on a single-phase basis, using different operating modes in one switching cycle of injected current. Theoretical results based on analysis are verified initially through digital simulation, and confirmed by using an experimental prototype of 2.5 kW.     

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.