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Abstract Pioneering flexible micro-supercapacitors, designed for exceptional energy and power density, transcend conventional storage limitations. Interdigitated electrodes (IDEs) based on laser-induced graphene (LIG), augmented with metal-oxide modifiers, harness synergies with layered graphene to achieve superior capacitance. This study presents a novel one-step process for sputtered plasma deposition of HfO2, resulting in enhanced supercapacitance performance. Introducing LIG-HfO2 micro-supercapacitor (MSC) devices with varied oxygen flow rates further boosts supercapacitance performance by introducing oxygen functional groups. FESEM investigations demonstrate uniform coating of HfO2 on LIG fibers through sputtering. Specific capacitance measurements reveal 6.4 mF/cm2 at 5 mV/s and 4.5 mF/cm2 at a current density of 0.04 mA/cm2. The LIG-HfO2 devices exhibit outstanding supercapacitor performance, boasting at least a fourfold increase over pristine LIG. Moreover, stability testing indicates a high retention rate of 97% over 5000 cycles, ensuring practical real-time applications. more...

We study unique range sets of meromorphic functions over an angular domain in the light of weighted sharing. One of our main results generalizes and improves a result of Xu et al. (2014). Most importantly, we have pointed out a gap in the proofs of some main results of Rathod (2021) and subsequently rectifying the gap we have conveniently improved the results. more...

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring. more...