RESUMO
This study investigated the efficacy of magnetic Sn metal-organic frameworks (MSn-MOFs) in removing the insecticide amoxicillin (AMX) from aqueous solutions. Our thorough experimental investigation showed that MSn-MOFs were an incredibly effective adsorbent for removing AMX. Several methods were used to characterize the material. BET investigation of the data displayed a significant surface area of 880 m2 g-1 and a strong magnetic force of 89.26 emu g-1. To identify the point of zero charge, surface characterization was carried out and the value was 7.5. This shows that the adsorbent carries a positive and negative charge below and above this position, respectively. Moreover, the impact of pH on adsorption equilibrium was explored. The results of kinetic models to explore the adsorption of AMX on MSn-MOFs supported the pseudo-second-order, and the adsorption complied well with the Langmuir isotherm. The results revealed that the overall adsorption mechanism may entail chemisorption via an endothermic spontaneous process with MSn-MOFs. The precise modes by which MSn-MOFs and AMX interacted may involve pore filling, H-bonding, π-π interaction, or electrostatic interaction. Determining the nature of this interaction is essential in understanding the adsorption behavior of the MOFs and optimize the adsorbent design for real-world applications. The use of the MSn-MOF adsorbent provides a straightforward yet efficient method for the filtration of water and treatment of industrial effluents. The results showed 2.75 mmol g-1 as the maximum capacity for adsorption at pH = 6. Additional tests were conducted to assess the adsorbent regeneration, and even after more than six cycles, the results demonstrated a high level of efficiency. The adsorption results were enhanced by the application of the Box-Behnken design.
RESUMO
Automated malware detection is a prominent issue in the world of network security because of the rising number and complexity of malware threats. It is time-consuming and resource intensive to manually analyze all malware files in an application using traditional malware detection methods. Polymorphism and code obfuscation were created by malware authors to bypass the standard signature-based detection methods used by antivirus vendors. Malware detection using deep learning (DL) approaches has recently been implemented in an effort to address this problem. This study compares the detection of IoT device malware using three current state-of-the-art CNN models that have been pretrained. Large-scale learning performance using GNB, SVM, DT, LR, K-NN, and ensemble classifiers with CNN models is also included in the results. In light of the findings, a pretrained Inception-v3 CNN-based transfer learned model with fine-tuned strategy is proposed to identify IoT device malware by utilizing color image malware display of android Dalvik Executable File (DEX). Inception-v3 retrieves the malware's most important features. After that, a global max-pooling layer is applied, and a SoftMax classifier is used to classify the features. Finally, gradient-weighted class activation mapping (Grad-CAM) along the t-distributed stochastic neighbor embedding (t-SNE) is used to understand the overall performance of the proposed method. The proposed method achieved an accuracy of 98.5% and 91%, respectively, in the binary and multiclass prediction of malware images from IoT devices, exceeding the comparison methods in different evaluation parameters.
