Pyridine 3,5-dicarboxylate-based metal-organic frameworks as an active electrode material for battery-supercapacitor hybrid energy storage devices.

Efficient energy storage and conversion is crucial for a sustainable society. Battery-supercapacitor hybrid energy storage devices offer a promising solution, bridging the gap between traditional batteries and supercapacitors. In this regard, metal-organic frameworks (MOFs) have emerged as the most versatile functional compounds owing to their captivating structural features, unique properties, and extensive diversity of applications in energy storage. MOF properties are governed by the structure and topological characteristics, which are influenced by the types of ligands and metal nodes. Herein, MOFs based on pyridine 3,5-dicarboxylate (PYDC) ligand in combination with copper and cobalt are electrochemically analyzed. Owing to the promising initial characterization of Cu-PYDC-MOF, a battery supercapacitor hybrid device was fabricated, comprising Cu-PYDC-MOF and activated carbon (AC) electrodes. The device showcased energy and power density of 17 W h kg -1 and 2550 W kg -1, respectively. Dunn's model was employed to gain deeper insights into the capacitive and diffusive contributions of the device. With their performance and versatility, the PYDC-based MOFs stand at the forefront of energy technology, ready to power a brighter future for upcoming generations.

Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction on residential buildings.

In digital era, energy efficient building remains a hot research topic because of increasing concern regarding their environmental impact and energy consumption. Designing a suitable energy efficient building based on their layout namely overall areas, distribution of the glazing areas, orientation, height, and relative compactness. Such components directly impact the heating load (HL) and cooling load (CL) of residential buildings. A precise predicting of load facilitates effective management of energy consumption and improves the quality of life. Lately, several studies have been implemented to predict the CL and HL. The most significant and challenging parts of predictive are defining the effective input parameter and developing a higher accuracy predictive model. The accuracy of predictive model based on machine learning algorithm must be enhanced by hybrid model. With this motivation, this article introduces an Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction (IHHOHDL-HCLP) model on Residential Buildings. The major aim of the IHHOHDL-HCLP model is to determine the CL and HL to accomplish effective energy utilization. To accomplish this, the IHHOHDL-HCLP primarily pre-processes the raw data in two levels namely min-max normalization and polynomial equation. In addition, the HDL model involves convolutional neural network (CNN) along with long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for HL and CL prediction process. Finally, the IHHO technique was applied for fine-tuning the hyperparameters related to the DL model. The IHHOHDL-HCLP model has demonstrated maximum prediction results with low RMSE values of 0.00874 and 0.00821, respectively, when applied to HL and CL, respectively. The experimental result analysis of the IHHOHDL-HCLP model demonstrates the better performance of the IHHOHDL-HCLP model over other DL models.

Light Weight Deep Learning Algorithm for Voice Call Quality of Services (Qos) in Cellular Communication.

In this paper, a deep learning algorithm was proposed to ensure the voice call quality of the cellular communication networks. This proposed model was consecutively monitoring the voice data packets and ensuring the proper message between the transmitter and receiver. The phone sends its unique identification code to the station. The telephone and station maintain a constant radio connection and exchange packets from time to time. The phone can communicate with the station via analog protocol (NMT-450) or digital (DAMPS, GSM). Cellular networks may have base stations of different standards, which allow you to improve network performance and improve its coverage. Cellular networks are different operators connected to each other, as well as a fixed telephone network that allows subscribers of one operator to another to make calls from mobile phones to landlines and from landlines to mobiles. The simulation is conducted in Matlab against different performance metrics, that is, related to the quality of service metric. The results of the simulation show that the proposed method has a higher QoS rate than the existing method over an average of 97.35%.

Forecasting renewable energy for environmental resilience through computational intelligence.

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

Understanding the light soaking effect of ZnMgO buffer in CIGS solar cells.

This study investigated the mechanism underlying the light soaking effect of a ZnMgO buffer in Cu(In,Ga)Se2 (CIGS) solar cells, where the cell efficiency increased with an increase of light soaking time. The ZnMgO buffer layer was deposited by an atomic layer deposition method. With light soaking, the cell efficiency of ZnMgO/CIGS cells increased mainly by the increase of the fill factor and partly by the increase of the open-circuit voltage. With light soaking, the electron carrier concentration of the ZnMgO layer increased and the XPS intensity of the hydroxyl bond in the ZnMgO layer decreased. Based on the above results and the comparison of other buffers in literature, we assumed that the hydrogen atoms broken away from the hydroxyl bond by photon irradiation occupied the interstitial sites of the ZnMgO layer as a donor atom and also passivated the defects at the ZnMgO/CIGS interface. The increase of the fill factor and open circuit voltage was explained based on H doping in the ZnMgO layer and H passivation at the ZnO/CIGS interface.

Design of energy band alignment at the Zn(1-x)Mg(x)O/Cu(In,Ga)Se2 interface for Cd-free Cu(In,Ga)Se2 solar cells.

The electronic band structure at the Zn(1-x)Mg(x)O/Cu(In(0.7)Ga(0.3))Se(2) interface was investigated for its potential application in Cd-free Cu(In,Ga)Se(2) thin film solar cells. Zn(1-x)Mg(x)O thin films with various Mg contents were grown by atomic layer deposition on Cu(In(0.7)Ga(0.3))Se(2) absorbers, which were deposited by the co-evaporation of Cu, In, Ga, and Se elemental sources. The electron emissions from the valence band and core levels were measured by a depth profile technique using X-ray and ultraviolet photoelectron spectroscopy. The valence band maximum positions are around 3.17 eV for both Zn(0.9)Mg(0.1)O and Zn(0.8)Mg(0.2)O films, while the valence band maximum value for CIGS is 0.48 eV. As a result, the valence band offset value between the bulk Zn(1-x)Mg(x)O (x = 0.1 and x = 0.2) region and the bulk CIGS region was 2.69 eV. The valence band offset value at the Zn(1-x)Mg(x)O/CIGS interface was found to be 2.55 eV after considering a small band bending in the interface region. The bandgap energy of Zn(1-x)Mg(x)O films increased from 3.25 to 3.76 eV as the Mg content increased from 0% to 25%. The combination of the valence band offset values and the bandgap energy of Zn(1-x)Mg(x)O films results in the flat (0 eV) and cliff (-0.23 eV) conduction band alignments at the Zn(0.8)Mg(0.2)O/Cu(In(0.7)Ga(0.3))Se(2) and Zn(0.9)Mg(0.1)O/Cu(In(0.7)Ga(0.3))Se(2) interfaces, respectively. The experimental results suggest that the bandgap energy of Zn(1-x)Mg(x)O films is the main factor that determines the conduction band offset at the Zn(1-x)Mg(x)O/Cu(In(0.7)Ga(0.3))Se(2) interface. Based on these results, we conclude that a Zn(1-x)Mg(x)O film with a relatively high bandgap energy is necessary to create a suitable conduction band offset at the Zn(1-x)Mg(x)O/CIGS interface to obtain a robust heterojunction. Also, ALD Zn(1-x)Mg(x)O films can be considered as a promising alternative buffer material to replace the toxic CdS for environmental safety.