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Worldwide population growth and pollution have limited the availability of clean water. Therefore, it is imperative to develop rapid water purification techniques that can eliminate all primary water pollutants, such as organic compounds and synthetic dyes. Pollutant disposal using a photocatalytic technique is efficient and safe for the environment. In this context, the ZnO/MgFe2O4 heterostructure photocatalyst is produced via a sol–gel technique to degrade methylene blue (MB) as a benchmark dye. Synthesized samples are characterized by numerous analytical techniques, including X-ray diffraction analysis, scanning electron microscopy, scanning tunneling electron microscopy, vibrating sample magnetometer, Fourier transform infrared spectroscopy, I-V characteristics curves, and photoluminescence spectroscopy. Photocatalytic degradation efficiency is witnessed at 40.01%, 55.05%, and 71.02% for ZnO, MgFe2O4, and ZnO/MgFe2O4, respectively. Among all the samples, composite heterojunction demonstrates the highest photocatalytic degradation efficiency. The obtained results reveal that the presence of magnetic ferrite in heterojunction promotes the absorption of radiation, reduces electron–hole recombination, and improves charge transfer. High stability over multiple cycles and easy removal character are the added benefits of the tested heterojunction photocatalyst.

Dysprosium doped Ni 0.50 Co 0.50 Dy x Fe 2−x O 4 (x = 0.25, 0.50, 0.75) was synthesized via auto combustion of nitrate citrate gel. The prepared composites were given microwave plasma treatment to improve their dielectric properties for constructing high-frequency devices. X-ray diffraction analysis confirmed the production of Dy-doped cubic spinel NiCo ferrites. The average crystallite size for the untreated magnetic composite was 32.10 nm, while for plasma-treated samples was 25.17 nm. A growth in lattice parameters and decreased porosity was observed in plasma-treated composites. VSM findings revealed a decrease in saturation magnetization from 54.15 to 36.75 emu/g with an increase in Dy 3+ from 0.25 to 0.50%. Saturation magnetization again raised with increasing Dy 3+ content from 0.50 to 0.75%. The lower Dy 3+ concentration resulted in low saturation magnetization, while the high Dy 3+ concentration showed high saturation magnetization of the composite. Temperature and frequency-dependent dielectric characteristics revealed bulbous enrichment in the dielectric constant and significant Q factor for plasma-treated samples. The perceived deviation in dielectric loss with frequency change is credited to the conduction phenomenon, which agrees with Koop’s phenomenological theory. High dielectric constant and low dielectric loss, even at higher temperatures, enable the synthesized ferrites to be strong candidates for high-frequency devices.

This study investigated the production of Cu2+-doped CoFe2O4 nanoparticles (CFO NPs) using a facile sol−gel technique. The impact of Cu2+ doping on the lattice parameters, morphology, optical properties, and electrical properties of CFO NPs was investigated for applications in electrical devices. The XRD analysis revealed the formation of spinel-phased crystalline structures of the specimens with no impurity phases. The average grain size, lattice constant, cell volume, and porosity were measured in the range of 4.55–7.07 nm, 8.1770–8.1097 Å, 546.7414–533.3525 Å3, and 8.77–6.93%, respectively. The SEM analysis revealed a change in morphology of the specimens with a rise in Cu2+ content. The particles started gaining a defined shape and size with a rise in Cu2+ doping. The Cu0.12Co0.88Fe2O4 NPs revealed clear grain boundaries with the least agglomeration. The energy band gap declined from 3.98 eV to 3.21 eV with a shift in Cu2+ concentration from 0.4 to 0.12. The electrical studies showed that doping a trace amount of Cu2+ improved the electrical properties of the CFO NPs without producing any structural distortions. The conductivity of the Cu2+-doped CFO NPs increased from 6.66 × 10−10 to 5.26 × 10−6 ℧ cm−1 with a rise in Cu2+ concentration. The improved structural and electrical characteristics of the prepared Cu2+-doped CFO NPs made them a suitable candidate for electrical devices, diodes, and sensor technology applications.

Spinel ferrites are widely investigated for their widespread applications in high-frequency and energy storage devices. This work focuses on enhancing the magnetic and dielectric properties of Ni0.25Cu0.25Zn0.50 ferrite series through non-thermal microwave plasma exposure under low-pressure conditions. A series of Ni0.25Cu0.25Zn0.50 ferrites was produced using a facile sol–gel auto-ignition approach. The post-synthesis plasma treatment was given in a low-pressure chamber by sustaining oxygen plasma with a microwave source. The structural formation of control and plasma-modified ferrites was investigated through X-ray diffraction analysis, which confirmed the formation of the fcc cubical structure of all samples. The plasma treatment did not affect crystallize size but significantly altered the surface porosity. The surface porosity increased after plasma treatment and average crystallite size was measured as about ~49.13 nm. Morphological studies confirmed changes in surface morphology and reduction in particle size on plasma exposure. The saturation magnetization of plasma-exposed ferrites was roughly 65% higher than the control. The saturation magnetization, remnant magnetization, and coercivity of plasma-exposed ferrites were calculated as 74.46 emu/g, 26.35 emu/g, and 1040 Oe, respectively. Dielectric characteristics revealed a better response of plasma-exposed ferrites to electromagnetic waves than control. These findings suggest that the plasma-exposed ferrites are good candidates for constructing high-frequency devices.

Spinel ferrites are widely investigated for their widespread applications in high-frequency and energy storage devices. This work focuses on enhancing the magnetic and dielectric properties of Ni[sub.0.25]Cu[sub.0.25]Zn[sub.0.50] ferrite series through non-thermal microwave plasma exposure under low-pressure conditions. A series of Ni[sub.0.25]Cu[sub.0.25]Zn[sub.0.50] ferrites was produced using a facile sol–gel auto-ignition approach. The post-synthesis plasma treatment was given in a low-pressure chamber by sustaining oxygen plasma with a microwave source. The structural formation of control and plasma-modified ferrites was investigated through X-ray diffraction analysis, which confirmed the formation of the fcc cubical structure of all samples. The plasma treatment did not affect crystallize size but significantly altered the surface porosity. The surface porosity increased after plasma treatment and average crystallite size was measured as about ~49.13 nm. Morphological studies confirmed changes in surface morphology and reduction in particle size on plasma exposure. The saturation magnetization of plasma-exposed ferrites was roughly 65% higher than the control. The saturation magnetization, remnant magnetization, and coercivity of plasma-exposed ferrites were calculated as 74.46 emu/g, 26.35 emu/g, and 1040 Oe, respectively. Dielectric characteristics revealed a better response of plasma-exposed ferrites to electromagnetic waves than control. These findings suggest that the plasma-exposed ferrites are good candidates for constructing high-frequency devices.

This study investigated the production of Cu[sup.2+] -doped CoFe[sub.2] O[sub.4] nanoparticles (CFO NPs) using a facile sol?gel technique. The impact of Cu[sup.2+] doping on the lattice parameters, morphology, optical properties, and electrical properties of CFO NPs was investigated for applications in electrical devices. The XRD analysis revealed the formation of spinel-phased crystalline structures of the specimens with no impurity phases. The average grain size, lattice constant, cell volume, and porosity were measured in the range of 4.55?7.07 nm, 8.1770?8.1097 Å, 546.7414?533.3525 Å[sup.3] , and 8.77?6.93%, respectively. The SEM analysis revealed a change in morphology of the specimens with a rise in Cu[sup.2+] content. The particles started gaining a defined shape and size with a rise in Cu[sup.2+] doping. The Cu[sub.0.12] Co[sub.0.88] Fe[sub.2] O[sub.4] NPs revealed clear grain boundaries with the least agglomeration. The energy band gap declined from 3.98 eV to 3.21 eV with a shift in Cu[sup.2+] concentration from 0.4 to 0.12. The electrical studies showed that doping a trace amount of Cu[sup.2+] improved the electrical properties of the CFO NPs without producing any structural distortions. The conductivity of the Cu[sup.2+] -doped CFO NPs increased from 6.66 × 10[sup.?10] to 5.26 × 10[sup.?6] ? cm[sup.?1] with a rise in Cu[sup.2+] concentration. The improved structural and electrical characteristics of the prepared Cu[sup.2+] -doped CFO NPs made them a suitable candidate for electrical devices, diodes, and sensor technology applications.

This study investigated the production of Cu -doped CoFe O nanoparticles (CFO NPs) using a facile sol-gel technique. The impact of Cu doping on the lattice parameters, morphology, optical properties, and electrical properties of CFO NPs was investigated for applications in electrical devices. The XRD analysis revealed the formation of spinel-phased crystalline structures of the specimens with no impurity phases. The average grain size, lattice constant, cell volume, and porosity were measured in the range of 4.55-7.07 nm, 8.1770-8.1097 Å, 546.7414-533.3525 Å , and 8.77-6.93%, respectively. The SEM analysis revealed a change in morphology of the specimens with a rise in Cu content. The particles started gaining a defined shape and size with a rise in Cu doping. The Cu Co Fe O NPs revealed clear grain boundaries with the least agglomeration. The energy band gap declined from 3.98 eV to 3.21 eV with a shift in Cu concentration from 0.4 to 0.12. The electrical studies showed that doping a trace amount of Cu improved the electrical properties of the CFO NPs without producing any structural distortions. The conductivity of the Cu -doped CFO NPs increased from 6.66 × 10 to 5.26 × 10 ℧ cm with a rise in Cu concentration. The improved structural and electrical characteristics of the prepared Cu -doped CFO NPs made them a suitable candidate for electrical devices, diodes, and sensor technology applications.

In this study, we explored the role of biochar (BC) and/or urease inhibitor (UI) in mitigating ammonia (NH 3 ) and nitrous oxide (N 2 O) discharge from urea fertilized wheat cultivated fields in Pakistan (34.01°N, 71.71°E). The experiment included five treatments [control, urea (150 kg N ha −1 ), BC (10 Mg ha −1 ), urea + BC and urea + BC + UI (1 L ton −1 )], which were all repeated four times and were carried out in a randomized complete block design. Urea supplementation along with BC and BC + UI reduced soil NH 3 emissions by 27% and 69%, respectively, compared to sole urea application. Nitrous oxide emissions from urea fertilized plots were also reduced by 24% and 53% applying BC and BC + UI, respectively, compared to urea alone. Application of BC with urea improved the grain yield, shoot biomass, and total N uptake of wheat by 13%, 24%, and 12%, respectively, compared to urea alone. Moreover, UI further promoted biomass and grain yield, and N assimilation in wheat by 38%, 22% and 27%, respectively, over sole urea application. In conclusion, application of BC and/or UI can mitigate NH 3 and N 2 O emissions from urea fertilized soil, improve N use efficiency (NUE) and overall crop productivity.

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.

Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.