Advanced extraction of PV parameters' models based on electric field impacts on semiconductor conductivity using QIO algorithm.

This article presents a novel approach for parameters estimation of photovoltaic cells/modules using a recent optimization algorithm called quadratic interpolation optimization algorithm (QIOA). The proposed formula is dependent on variable voltage resistances (VVR) implementation of the series and shunt resistances. The variable resistances reduced from the effect of the electric field on the semiconductor conductivity should be included to get more accurate representation. Minimizing the mean root square error (MRSE) between the measured (I-V) dataset and the extracted (V-I) curve from the proposed electrical model is the main goal of the current optimization problem. The unknown parameters of the proposed PV models under the considered operating conditions are identified and optimally extracted using the proposed QIOA. Two distinct PV types are employed with normal and low radiation conditions. The VVR TDM is proposed for (R.T.C. France) silicon PV operating at normal radiation, and eleven unknown parameters are optimized. Additionally, twelve unknown parameters are optimized for a Q6-1380 multi-crystalline silicon (MCS) (area 7.7 cm2) operating under low radiation. The efficacy of the QIOA is demonstrated through comparison with four established optimizers: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA). The proposed QIO method achieves the lowest absolute current error values in both cases, highlighting its superiority and efficiency in extracting optimal parameters for both Single-Crystalline Silicon (SCS) and MCS cells under varying irradiance levels. Furthermore, simulation results emphasize the effectiveness of QIO compared to other algorithms in terms of convergence speed and robustness, making it a promising tool for accurate and efficient PV parameter estimation.

Acceleration of Wound Healing in Diabetic Rats through a Novel 3D Organo-Hydrogel Nanocomposite of Polydopamine/TiO2 Nanoparticles and Cu (PDA-TiO2@Cu).

BACKGROUND: Diabetic wound represents a serious issue with a substantial impact and an exceptionally complex pathology affecting patients' mental health and quality of life. So, we have developed a novel 3D organo-hydrogel nanocomposite of polydopamine/TiO2 nanoparticles and cu (PDA-TiO2@Cu) and examined its efficacy in diabetic wound healing. METHODS: Forty-five adult male albino rats were divided into normal control rats (non-diabetic rats with non-treated skin wounds), diabetic control rats (diabetic rats with non-treated skin wounds), and organo-hydrogel-treated rats (diabetic wounds treated with topically applied organo- hydrogel once daily). Macroscopic changes of the wound were observed on days 0, 3, 5, 7, and 10 to measure wound diameters. Skin specimens from the wound tissue were taken on days 3, 7, and 10, respectively, and examined histologically and immunohistochemically. Also, the gene expressions of collagen I, Matrix Metalloproteinase-9 (MMP-9), and Epidermal Growth Factor (EGF), and levels of Interleukin 6 (IL-6) and Superoxide Dismutase (SOD) were assessed. RESULTS: Our observed results indicated that the developed patch significantly accelerated the healing time compared to the normal control and diabetic control groups. Moreover, the patchloaded group revealed complete re-epithelization and a highly significant increase in the mean area % of CD31 immunostaining on day 7. The organo-hydrogel-loaded group displayed a significant decrease in gene expression of MMP-9 and a significant increase in gene expression of EGF and collagen I. Additionally, the organo-hydrogel-loaded group exhibited a significant decrease in levels of IL-6 and a significant increase in levels of SOD, compared to the normal diabetic control groups. CONCLUSION: The organo-hydrogel can be used for treating and decreasing the healing period of diabetic wounds.

Synthesis of Cross-Linked and Sterilized Water-Soluble Electrospun Nanofiber Biomatrix of Streptomycin-Imbedded PVA/CHN/ß-CD for Wound Healing.

The diagnosis and prognosis of chronic wounds are demanding and require objective assessment. Because of their potential medicinal applications, the syntheses of biopolymeric chitosan (CHN) structure and PVA-based mixed electrospun nanofibers with biomimetic features were thoroughly investigated. This study created different formulas, including a guest molecule and capping agent, using supporting PVA as a vehicle. CHN was used as a biomodifier, and beta-cyclodextrin (ß-CD) as a smoother and more efficiently entraps streptomycin (STP) compared with the silver sheet wound dressing. The relevant analyses showed that the size distribution increased with the incorporation of PVA, CHN, and ß-CD to 120.3, 161.9, and 192.02 nm. The webs boosted particle size and released content stability to 96.4% without compromising the nanofiber structure. Examining the synergistic effects of the PVA/CHN/STP/ß-CD nanoformulation against pathogenic strains of S. aureus, P. aeruginosa, and Aspergillus niger, clean zones were 47 ± 3.4, 45 ± 3.0, and 49 ± 3.7 mm were produced. PVA/CHN/STP/ß-CD formula exhibited a 98.9 ± 0.6% cell viability and wound closure of 100% at 72 h. The results reveal that the PVA/CHN/STP/ß-CD formula is promising for medical applications, especially in wound healing, compared with the silver sheet.