Sürdürülebilir Kalkınma Amaçları

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• MakaleYayıncıda açık erişim2026Scopus SJRQ3/r/munzur/1095
  Interfacial Polarization in PS/Sm2O3 Nanocomposite Films for Low-Loss Frequency-Dependent Dielectric Application

  Güner, Melek; Özkan, Betül Çiçek; Gürgenç, Ezgi; Macit, Cevher Kürşat; Aslan, Naim; Gurgenc, Turan

  Rare-earth oxide nanoparticles offer a strategy to tune dielectric films via interfacial polarization while maintaining low-loss insulating behavior. Here, polystyrene/samarium oxide (PS/Sm2O3) nanocomposite films containing 0, 1, 3, 5, and 10 wt.% Sm2O3 were fabricated by solvent casting and evaluated as a low-loss dielectric for frequency-sensitive electronic applications. FTIR, XRD, SEM/EDX, and XPS confirmed successful Sm2O3 incorporation, preservation of the PS structure, formation of PS/Sm2O3 interfacial regions, and Sm3+/Sm–O species. XRD revealed amorphous PS with increasingly intense crystalline Sm2O3 reflections, whereas SEM/EDX showed relatively uniform filler distribution at low and moderate loadings and partial agglomeration at 10 wt.%. Dielectric spectroscopy at room temperature from 1 kHz to 1 MHz showed a frequency-dependent response governed by interfacial polarization and limited charge transport. At 1 kHz, the dielectric constant increased from 8.67241 ± 0.00280 for pure PS to 10.13061 ± 0.00641 for PS-10, while the loss tangent remained low (∼ 0.068). The dielectric loss increased moderately from 0.59535 ± 0.00236–0.69194 ± 0.00710, and AC conductivity rose from (3.31202 ± 0.01314) × 10−10 to (3.84935 ± 0.03948) × 10−10 S cm−1. These results indicate interface-controlled dielectric enhancement without conductive percolation, highlighting PS/Sm2O3 films for capacitor dielectric layers, insulating films, flexible electronics, and frequency-sensitive dielectric technologies.

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1379
  Automated fault classification of asynchronous motor using mobile phone accelerometer and Parallel Residual CNN-GRU

  Ertargin, Merve; Orhan, Ahmet; Yildirim, Ozal; Gurgenc, Turan

  In this study, a fault detection system that classifies motor faults using deep learning without requiring any sensor connections inside or on the motor setup is proposed. The focus is on the electrical and mechanical faults of asynchronous motors, which are widely used in the industry. A mobile application developed using Flutter can collect vibration data using the three-axis accelerometer sensor in smartphones. By placing a smartphone with the application installed on the motor for which fault detection is desired, vibration data on three axes (x, y, z) is collected to train deep models. Subsequently, test data is used to identify the fault status and class of the electric motor. Different conditions and scenarios were created to test the adequacy of smartphone accelerometer sensors in detecting motor faults. Fault classification with vibration data obtained under different motor operating speeds in loaded and unloaded conditions achieved accuracy rates of over 99%.

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  AtıfScopus18·OpenAlex15

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