Mohammad K. Al-Sadoon’s research while affiliated with King Saud University and other places

As an alternative to synthetic chemicals, natural herbs can greatly improve fish's health and growth potential. This investigation aimed to explore the possibilities of oregano (Oregano vulgare L.) extract as a phytogenic feed additive in improving the growth, blood profile, carcass composition, whole-body mineralization, serum biochemistry and antioxidant activity of Catla catla (average initial weight = 9.26±0.03 g/fish). Triplicate groups of fish were fed diets having various percentages of oregano extract (0 %, 0.5 %, 1 %, 1.5 %, 2 %, 2.5 % and 3 % per kg diet) twice daily for 90 days. The findings found that parameters of growth such as weight gain, weight gain%, feed conversion ratio, survival rate, protein efficiency ratio, and specific growth rate increased substantially (p<0.05), in fish fed diets with 1 %-1.5 % oregano extract supplementation relative to the other treatments. Carcass composition and hematological parameters were improved significantly (p<0.05) as oregano extract concentrations increased from 1 % to 1.5 % per kg compared to control and other groups. Results also indicated that minerals in the body of fish improved significantly (p<0.05) when extract concentrations increased from 1 %-1.5 % in the diet. Furthermore, immune parameters and hepatic enzymes activities in fish improved considerably (p<0.05) at these concentrations, indicating optimal antioxidant protection and disease resistance. In contrast, all fish parameters were decreased notably (p<0.05) in control and groups based on 2.5 % and 3 % oregano extract. In conclusion, the supplementation of 1 %-1.5 % oregano extract in the diets of C. catla could significantly (p<0.05) improve the overall performance of fish.

Discoveries in nanotechnologies are placing a great deal of attention on greener strategies that use harmless substances and moderated reactions to promote healthy development. This work used a straightforward, innovative, and cost‐effective sustainable approach to produce bio‐augmented TiO 2 nanometer‐sized particles (NMSP) by applying a water‐based extract of the star fruit as a stabilization and reduction agent. A variety of techniques, comprising UV–Vis, XRD, FT‐IR, FE‐SEM with EDAX, and TEM, have been employed to investigate the formed TiO 2 NMSP. The germicidal properties of formed TiO 2 NMSP towards germs have been investigated by implementing an agar‐based pore plate technique. Congo red and methylene blue dyes have been applied to assess photodecomposition activity. The TiO 2 NMSP exhibited significant germicidal efficacy versus many pathogenic microbes. The maximum degradation percentages of Congo red and methylene blue were 89.2% and 83.7%, achieved after 60 and 70 min, respectively. Consequently, it is determined that the selected NMSP composition enhanced germicidal and photodecomposition capabilities. The combined effort may serve as an effective method for eliminating color degradation concerning effluent and could potentially be employed in the field of medicine to address antibiotic resistance.

This work presents a fluorescence sensor based on nitrogen‐doped carbon dots (N‐CDs) utilized for sequential detection of Hg ²⁺ and TNT. The N‐CDs were produced by a simple and efficient hydrothermal process involving the combination of black bean extract with citric acid (CA) and ethylenediamine (EDA). The resulting N‐CDs exhibit a steady blue fluorescence with a significant quantum yield of 12%. We conducted a thorough investigation into the mechanism by which the fluorescence of NCDs is reduced in the presence of Hg ²⁺ . Our analyses, which included Stern–Volmer quenching tests, confirmed the development of a stable complex between N‐CDs and Hg ²⁺ . When the NCDs‐Hg ²⁺ complex was exposed to TNT, the fluorescence was selectively restored. This sequential “on–off–on” sensing capacity allows for efficient monitoring of both Hg ²⁺ and TNT, demonstrating good sensitivity and selectivity. The sensor has a low detection limit (LOD) of 3.1 and 46 nM for Hg ²⁺ ions and TNT in a linear range of 0–40 and 0–30 μM, respectively. This study emphasizes the potential application of N‐CDs for detecting heavy metals and explosives at the same time. It highlights their usefulness in sophisticated environmental sensing technologies that are suitable for important applications.

Aqueous Al–air batteries (AABs) are considered promising electrochemical energy devices due to their high-energy density, high-capacity density, and stable discharge voltage. However, the self-corrosion, passivation, and parasitic hydrogen precipitation side reactions in the aqueous electrolyte degrade the performance of these batteries, limiting their development. To overcome the problems related to the use of AABs, we introduce ethylenediaminetetraacetic acid disodium salt (EDTA-2Na) as an additive to the alkaline electrolyte. EDTA-2Na adsorbs strongly to the Al anode interface creating a protective layer capable of inhibiting water-induced parasitic reactions. In fact, in the presence of the additive, the hydrogen evolution tests have shown that the hydrogen evolution rate decreased from 0.70 to 0.30 mL cm⁻² min⁻¹. In addition, the electrochemical tests indicated an inhibition efficiency of 55%, the full-cell discharge tests suggested an increase in the specific capacity density of the battery from 943.6 to 2381.7 mA h g⁻¹ and the anode utilization increased from 31.6% to 80.9%, greatly improving the performance of the battery. Surface characterization of the Al alloy surface was also carried out to investigate the adsorption of EDTA-2Na on it. This electrolyte modification strategy provides a promising option for modulating the anode/electrolyte interface chemistry to achieve high-performance AAB.

DFT approach was employed to examine the mechanical and optoelectronic properties of CdCe2X4 (X = S, Se) for investigating their fundamental attributes leading to the FM semiconducting capabilities. In this letter, we computed the precise spin-polarized electrical characteristics using mBJ potential and evaluated the physical and mechanical features via PBEsol-GGA functional. The materials' brittleness has been disclosed by the obtained elastic parameters and related components. According to the analysis of band structure configuration and density of states plots, the aforementioned composites are accounted to be the most durable. In the FM phase, these compounds’ durability is because of rare earth Ce ions’ exchange splitting within the crystal structure, which is prompted by p-d hybridization. Band exchange splitting has been significantly affected by the participation among impurity cations and resident anions as well as by their spin, charge, and magnetism. In addition, the present study entailed a thorough analysis of the dielectric parameter, which in turn gained insight into the compound's spectral behavior. FM semiconducting features played vital role in scientific improvements of photovoltaic appliances. The parameters estimated in the current investigation might help scientists to explore modifications in the functionality of CdCe2X4 (X = S, Se).

This study takes a detailed look at the corrosion inhibition capabilities of two benzodiazepine‐derived organic compounds, 3,3‐dimethyl‐11‐(4‐nitrophenyl)‐2,3,4,5,10,11‐hexahydro‐1H‐dibenzo[b,e][1,4]diazepin‐1‐one (PNO) and 3,3‐dimethyl‐11‐(2‐nitrophenyl)‐2,3,4,5,10,11‐hexahydro‐1H‐dibenzo[b,e][1,4]diazepin‐1‐one (ONO), in a 1.0‐M hydrochloric acid environment using a variety of analytical methods, including electrochemical approaches — electrochemical impedance spectroscopy (EIS) and potentio‐dynamic polarization (PDP). The results show that the concentration‐dependent inhibitory efficacy of PNO and ONO increases with increasing concentration. Both inhibitors exhibit mixed‐type behaviour, which is confirmed by the polarization results. At the optimum concentration, the inhibition efficiencies of PNO and ONO are 92.9% (PNO) and 87.6% (ONO), respectively. The effective adsorption of these inhibitors on the metal surface was also confirmed by X‐ray photoelectron spectroscopy (XPS) analysis. The existence of a barrier layer surrounding the mild steel was demonstrated using scanning electron microscopy (SEM) and energy‐dispersive X‐ray analysis (EDX), all of which were used to study the surface characterization. The most important interactions with the iron surface are achieved by inhibitors with electron‐accepting properties, according to density functional theory results and molecular dynamic simulation (MDS). With encouraging prospects for industry and metal preservation, these results pave the way for promising applications for effective corrosion protection in a 1.0‐M HCl environment.

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.