To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R² value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

In present study, NiO:ZnO thin films in molar ratios of 1:0, 0:1, 3:1, 1:1 and 1:3 were formed on p-Si layers with aluminum (Al) bottom contact. Dynamic sol-gel spin coating method was used as coating method. Al top contacts were deposited on thin films and Al/NiO:ZnO/p-Si/Al photodiodes were fabricated. The structural and morphological properties of the photodiodes were determined by X-ray diffraction (XRD), emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray spectroscopy (EDX). The photoresponse and electrical properties of the produced photodiodes were investigated by current–voltage (I–V) and capacitance-voltage (C-V) measurements. Al/NiO:ZnO/p-Si/Al photodiodes were successfully produced. It was determined that the thin films formed were composed of nanostructures. All photodiodes were found to be sensitive to light. It was seen that the photosensitivity of composite photodiodes was higher than the pure photodiodes and photosensitivity decreased as the ZnO ratio increased. It was determined that the most sensitive photodiode to light was the composite photodiode with a NiO:ZnO ratio of 3:1, and the highest photosensitivity was measured as 3.12 x 103 at 100 mW/cm2 light intensity in this photodiode. In all photodiodes, the capacitance values decreased as the frequency increased. The results show that by changing the NiO:ZnO ratio, the photoresponse and electrical parameters of the photodiodes can be controlled and the produced photodiodes can be used as a photosensor in solar tracking systems and optoelectronic applications.

Bu çalışmada, saf ve nadir toprak elementlerinden olan Lantan (La) katkılı kadmiyum oksit (CdO) nanoparçacıkları sol-jel yöntemi ile üretildi. Katkılama farklı mol yüzdelerinde (1%, 3%, 5% ve 7%) gerçekleştirildi. Üretilen nanoparçacıklar FE-SEM, EDX, XRD ve FT-IR analizleri ile karakterize edildi. Farklı oranlardaki La katkılı CdO nanoparçacıkların dielektrik sabiti, dielektrik kayıp faktörü ve ac iletkenlik değerleri oda sıcaklığında frekansın bir fonksiyonu olarak empedans analizör cihazı ile belirlendi. La katkılı nanoparçacıklar sol-jel yöntemi ile başarılı bir şekilde üretilmiştir. Üretilen nanoparçacıklar küre benzeri formdadır ve La katkılı nanoparçacıkların boyutları saf CdO’ ya göre daha yüksektir. Frekansın artması ile birlikte dielektrik özelliklerinde azalma olmuş ve daha yüksek frekanslarda sabit bir değere yaklaşmıştır. %1 La katkısı ile 6.87 olan dielektrik sabiti %7 La katksı ile 28.15’e yükselmiştir. Dielektrik kayıp değerleri La katkısı ile azalma göstermiştir. %1 La katkısı ile 9.799x10-5 S/cm olan iletkenlik değeri %7 La katkısı ile 6.110 x10-7 S/cm olarak ölçülmüştür.

This study tailors titanium nitride (TiN)-reinforced RT70 HC nanocomposite phase change materials (PCMs) for medium-temperature thermal energy storage. TiN nanoparticles were incorporated into commercial RT70 HC at 0.1–2.0 wt.% by a two-stage method combining sodium dodecyl sulfate, magnetic stirring, and ultrasonication, and characterized by FT-IR, XRD, SEM-EDX, elemental mapping, DSC, thermal conductivity, Cp, TGA, and 1000-cycle tests. FT-IR and XRD confirmed the physical integration of TiN into RT70 HC without new chemical bonds or secondary phases, and SEM-EDX showed a concentration-dependent dispersion. The phase change temperatures were largely preserved. The latent heat varied non-monotonically with TiN content, increasing at low loadings (0.1–0.5 wt.%) and decreasing at higher loadings. Because each composition was prepared as a single batch and measured on small specimens, the low-loading latent-heat increase (up to about 9%) is indicative rather than statistically proven and may fall within the subsampling variance. The 0.5 wt.% sample reached the highest values of 306/296 J/g in the first cycle and 286/268 J/g after 1000 cycles. The thermal conductivity increased with TiN content, reaching a maximum enhancement of about 24.09% in the liquid phase (0.1785 to 0.2215 W/(m·K) at 80 °C) and 35.7% in the solid phase at 2.0 wt.%, whereas the specific heat capacity was lower at higher loadings, an indicative trade-off given the single-run measurement uncertainty. TGA showed degradation onset temperatures above 240 °C, a wide safety margin relative to the ~72 °C working range. Overall, the 0.1–0.5 wt.% formulations offered the most balanced thermal performance for medium-temperature applications.