Engineering and Technology Journal

Investigating the role of solvent extraction in altering pH levels for efficient Neodymium extraction from magnet scrap
https://doi.org/10.30684/etj.2025.159446.1948
Abstract
Recycling rare earth elements, specifically neodymium from magnet scrap, is crucial for advancing sustainable technologies across various industries. In this research, neodymium was recovered by solvent extraction using a stock solution prepared in 200 mL by dissolving 1 g of neodymium (III) nitrate hexahydrate, Nd(NO3)3.6H2O, in 0.5 M nitric acid. Then, it was mixed with 1 M di-(2-ethylhexyl) phosphoric acid (D2EHPA) in Isopar-L, as the organic phase, and 3 M H2SO4, as the aqueous phase, with an organic/aqueous volume ratio of (1:1). Specifically, the study examined the pH variation of the aqueous phase (from 0.5 to 2). The pH of the solution was measured using a pen-type pH meter, where the stripping agent was HNO3 at a concentration of 6 M. The experiments were conducted using a magnetic stirrer at ambient temperature with an agitation speed of 300 rpm for 24 hours. The concentration of metals was measured using an EDX. The extraction efficiency of neodymium increased from 51.7% at a pH of 0.5 to 82.02% at a pH of 1.5, with a slight decrease observed at pH values of 1.7 and 2. Moreover, samples of Mg-Nd alloy were manufactured with various extractant Nd contents of (1, 3, 4, and 5%). The microstructure of the alloys, both before and after corrosion in 3.5% NaCl, was examined using a scanning electron microscope (SEM). The results indicated that the alloys consisted primarily of the α-Mg phase and the Mg12Nd phase, and that the corrosion resistance increased with the increasing amount of neodymium.
Highlights:
- At pH ≤ 2, the concentration of rare earth elements and metal ions increased, enhancing the extraction potential.
- D2EHPA acted as an efficient cation exchanger, releasing H⁺ ions during metal cation uptake.
- In solvent extraction, precipitated particles promoted rare earth attachment to extractant compounds.
- The highest neodymium extraction efficiency was achieved at a pH of 1.5.
- The addition of neodymium to magnesium improved its corrosion resistance.
Keywords:
- Rare earth
- Neodymium
- Magnet
- Solvent extraction
- Corrosion
Journal: https://lnkd.in/dgnvtdte
Issue: https://etj.uotechnology.edu.iq/issue_15129_15442.html
Article: https://etj.uotechnology.edu.iq/article_189917.html
ETJ LinkedIn: https://lnkd.in/d_8SPqAt

Impact of surface-modified silica and magnesium oxide nanoparticles on the flow behaviour of East Baghdad crude oil and emulsion
https://doi.org/10.30684/etj.2024.147425.1710
Abstract
The transportation of crude oil naturally, including emulsion from the wellhead to the processing facility, presents a challenge in the oil industry, particularly as wells age and the production of associated water increases. To improve the flowability of the emulsified oil, traditional methods for reducing the viscosity, such as dilution and heating, are costly and energy-intensive. However, nanotechnology offers a potential solution to improve flowability and crude oil behavior. This paper examines how adding 3% wt of surface-modified silicon dioxide (SiO2) and magnesium oxide (MgO) nanoparticles impacts the flow properties of an emulsion containing East Baghdad crude oil. The investigation is conducted across different water cut levels (5%, 35%, 50%, and 75% v/v) within a horizontal pipe 0.0145 m inner diameter and 13m in length. The effect of these nanoparticles on emulsion stability, rheology, viscosity, pressure drop, and energy consumption was studied. The rheology study found that the best results were achieved by adding surface-modified nano silica at 3%, which significantly reduced viscosity with shear thinning behavior. Adding 3% nano-silica obtained a highly stable emulsion and a higher reduction of 69% in power consumption for pumping the fluid. In comparison, a 25% increase in power consumption was achieved by adding the same concentration of MgO.
Highlights:
- As oil wells age, water content rises, creating stable emulsions at 50% water cut.
- 3% modified Nanosilica enhances flow and reduces viscosity.
- 3% MgO addition significantly alters fluid behavior at high water cuts.
- Power consumption decreases with SiO2-added emulsions.
Keywords:
- Emulsion
- Rheology
- Pressure drop
- Low API crude oil
- Nanoparticles
Journal: https://lnkd.in/dgnvtdte
Issue: https://etj.uotechnology.edu.iq/issue_14819_15035.html
Article: https://etj.uotechnology.edu.iq/article_183781.html
ETJ LinkedIn: https://lnkd.in/d_8SPqAt

AI-driven attacks on database security: taxonomy and defense strategies
https://doi.org/10.30684/etj.2025.163946.2003
Abstract
Artificial intelligence has introduced both unprecedented capabilities and novel vulnerabilities into database environments, enabling highly adaptive attacks that can evade traditional defenses. This review surveys recent research published between 2022 and 2025 on AI-assisted database security threats and synthesizes the literature to develop a comprehensive taxonomy of emerging attack approaches. We identified five primary classes of AI-based attacks: intelligent SQL injection attacks, adversarial machine learning strategies targeting database security systems, data poisoning attacks on AI-based databases, automated reconnaissance exploits, and sociotechnical manipulations aimed at database administrators. We systematically reviewed publications on cyber defense stored in IEEE Xplore, ACM Digital Library, Science Direct, and Scopus databases. Boolean search terms were used on the databases specific to cyber defense. Findings indicate that automated SQL injection attacks can escalate the bypass rate of security systems to over 85% effectiveness. The effectiveness of rule-based defense systems degrades by 32% when pitted against sophisticated AI-adapted adversarial attacks. Conversely, machine learning-based defenses maintain a detection rate of 85 to 95%. To combat advancing techniques, a multilayer approach that includes adversarial training, anomaly-based intrusion detection, and automated user behavior analysis and reporting technology should be employed. This approach utilizes anomaly-based defenses through a monitoring model. Analysis shows that conventional database defense techniques need to be upgraded with real-time analytics, dynamic response mechanisms, and zero-day vulnerability protection to keep pace with the increasingly sophisticated nature of AI adversarial attacks on database systems.
Highlights:
- A novel taxonomy of five AI-powered database attacks bypassed traditional defenses with 85% success.
- Analysis of 23 recent studies revealed critical gaps in AI-driven database security frameworks.
- A layered defense model with adversarial training and behavioral monitoring was proposed.
- Statistical results showed a 32% decline in rule-based systems against advanced AI attack variants.
- A multi-disciplinary approach addressed technical, organizational, and human factors in AI threats.
Keywords:
- AI driven attacks
- Database security
- Adversarial machine learning
- SQL injection
- Data poisoning
- Federated learning security
- GAN based attacks
Journal: https://lnkd.in/dgnvtdte
Issue: https://etj.uotechnology.edu.iq/issue_15129_15442.html
Article: https://etj.uotechnology.edu.iq/article_189916.html
ETJ LinkedIn: https://lnkd.in/d_8SPqAt