Lanthanum substituted Ni-Zn ferrite nanoparticles (Ni0.5Zn0.5LaxFe1-xO4; 0.00 ≤x≤ 1.00) synthesized by sol-gel method were presented. X-ray diffraction patterns reveal the typical single phase spinel cubic ferrite structure, with the traces of secondary phase for lanthanum substituted nanocrystals. In addition, the structural analysis also demonstrates that the average crystallite size varied in the range of 21-25 nm. FTIR spectra present the two prominent absorption bands in the range of 400 to 600 cm-1 which are the fingerprint region of all ferrites. Surface morphology of both substituted and unsubstituted Ni-Zn ferrite nanoparticle samples was studied using FESEM technique and it indicates a significant increase in the size of spherical shaped particles with La3+ substitution. Magnetic properties of all samples were analyzed using vibrating sample magnetometer (VSM). The results revealed that saturation magnetization (Ms) and coercivity (Hc) of La3+ substituted samples has decreased as compared to the Ni-Zn ferrite samples. Hence, the observed results affirm that the lanthanum ion substitution has greatly influenced the structural, morphology and magnetic properties of Ni-Zn ferrite nanoparticles.
The utilization of superparamagnetic iron oxide nanofluids in enhanced oil recovery (EOR) has gained attention due to their ability to alter the interfacial tension (IFT) of reservoir rocks. However, the influence of electromagnetic (EM) waves on these nanofluids, particularly when doped with manganese (Mn), remains underexplored. The interaction mechanisms between EM waves and Mn-doped Fe3O4 nanofluids are not well understood, limiting their application in EOR. This study aimed to investigate the effects of EM waves on Mn-doped superparamagnetic iron oxide nanofluids and to assess the potential for enhancing oil recovery by measuring their IFT. Mn-doped Fe3O4 nanoparticles were synthesized using a co-precipitation method and stabilized with ascorbic acid. Density functional theory (DFT) was employed to study the Mn-dopant site selectivity within the Fe3O4 lattice. Helmholtz coils generated uniform EM fields and interfacial tension (IFT) measurements were conducted under applied EM waves generated under both direct current (DC) and alternating current (AC) conditions. DFT calculations indicated a preference for Mn dopants in specific lattice sites, while the experimental results showed that both DC- and AC-generated sinusoidal EM waves could reduce the IFT of the Mn-doped nanofluids, suggesting their improved EOR potential. These findings provide new insights into the application of EM waves in nanofluid-based EOR.
WSN (wireless sensor network) plays a very important role in the agricultural environment monitoring. Although solar energy and other power supply methods are used to solve the node energy problem, the monitoring equipment works outdoors for a long time, which is easily affected by the environment. The supply is unstable to cause abnormalities in some nodes. So this study proposes a ZIRRA algorithm (ZigBee immune routing repair algorithm) for the rechargeable agricultural WSN. It simulates the working mechanism of the immune system and designs modules such as identification, processing, cloning and storage, which can provide a better repair strategy for abnormal nodes. Then it compares the quality of the backup nodes and replaces the backup nodes with poor quality, so that the optimal paths are maintained between source nodes and middle relay nodes, which increases the optimization ability of the algorithm. The experimental results show that the ZIRRA algorithm shows significant advantages in routing node repair mechanism. Compared with the LFRA, AR-TORA and ICCO algorithms, the average routing energy consumption of the ZIRRA algorithm reduced 35.33%, 58.37% and 45.15% , the data transmission delay reduced by 23.72%, 36.74% and 16.28%, and the average node survival time extended 25.08%, 33.55% and 13.88%. In addition, the maximum communication time and network throughput of the ZIRRA algorithm increased 44.49% and 13.03% at the scale of 1000 to 2000 nodes. These quantitative results show that the ZIRRA algorithm can improve the energy efficiency, transmission reliability and stability. The ZIRRA algorithm draws on the working principle of the immune system and repairs abnormal nodes through identification, processing, cloning and storage modules. Unlike the traditional node repair algorithms, the ZIRRA algorithm has higher efficiency and accuracy in identifying and processing abnormal nodes through the improved clone tracking algorithm. It uses an improved clone tracking algorithm in the learning module, improves the cloning and mutation mechanisms, and generates the optimal antibodies for repairing abnormal nodes. It also integrates an adaptive energy management strategy to cope with fluctuations in energy levels by prioritizing the transmission of critical data and reducing the frequency of non-essential communications, which improves the network stability and data transmission reliability.