Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 12Citation - Scopus: 14Insights Into Interface Treatments in P-Channel Organic Thin-Film Transistors Based on a Novel Molecular Semiconductor(IEEE-Inst Electrical Electronics Engineers Inc, 2017-05) Liguori, Rosalba; Usta, Hakan; Fusco, Sandra; Facchetti, Antonio; Licciardo, Gian Domenico; Di Benedetto, Luigi; Rubino, AlfredoOrganic thin-film transistors (OTFTs) were fabricated using a novel small molecule, C6-NTTN, as the semiconductor layer in several different architectures. The C6-NTTN layer was deposited via both vacuum evaporation at different substrate temperatures and via solution-processing, which yield maximum hole mobilities of 0.16 and 0.05 cm(2)/V . s, respectively. Surface treatments of the substrate, insulator, and metal contacts used for OTFT fabrication employing polymer films and different self-assembled monolayers were investigated. In particular, in bottom-gate devices, the insulator surface hydrophobicity was optimized by the deposition of poly(methyl methacrylate) or hexamethyldisilazane, while in the top-gate geometry, pentafluorobenzenethiol was efficiently used to modify the substrate surface energy and to change the contact work function. Atomic force microscopy analysis was exploited to understand the relationship between the semiconductor thin-film morphology and the device electrical performance. The results shown here indicate an inverse proportionality between the mobility and the interface trap density, with parameters depending especially on semiconductor-insulator interfacial properties, and a correlation between the threshold voltage and the characteristics of the semiconductor-metal interface.Article Comprehensive Optimization of Shot Peening Intensity Using a Hybrid Model With AI-Based Techniques via Almen Tests(Walter de Gruyter Gmbh, 2025-06-03) Karaveli, Kadir Kaan; Bal, BurakShot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model's robustness, four optimization algorithms - differential evolution (DE), simulated annealing (SA), Nelder-Mead (NM), and random search (RS) - were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.