Browsing by Author "Kartal, Enise"

Now showing 1 - 4 of 4

Restricted
12 Eylül ve Duymaz ailesi
(Bilkent University, 2018) Aytekin, Arda; Tosyalı, Batuhan; Birlikçi, Bora; Kartal, Enise; Turan, Göksu
Türkiye’nin belki de en gergin dönemi sayılabilecek 1975-1980 dönemine bir son vermek amacıyla Türk Silahlı Kuvvetleri 12 Eylül 1980’de darbe ile yönetime el koymuştur. Bu darbe sokaklardaki terörü büyük ölçüde kontrol altına almayı başarmış olsa da bu kontrol farklı ideolojiye sahip çok sayıda örgüte büyük ölçüde son vermek ve bu örgüt mensuplarını gözaltına almak sureti ile olmuştur. Muzaffer Duymaz, bu süreçte hasar gören örgütlerden birisi olan Türkiye Komünist Partisi’nin Bursa il genel sekreteridir. Aşağıdaki makalede 12 Eylül 1980 darbesi ve Muzaffer Duymaz ile ailesinin bu süreçte yaşadıkları anlatılacaktır.
Open Access
Atmospheric-pressure mass spectrometry by single-mode nanoelectromechanical systems
(American Chemical Society, 2023-09-08) Kaynak, Batuhan Emre; Alkhaled, Mohammed; Kartal, Enise; Yanık, Cenk; Hanay, Mehmet Selim
Weighing particles above the megadalton mass range has been a persistent challenge in commercial mass spectrometry. Recently, nanoelectromechanical systems-based mass spectrometry (NEMS-MS) has shown remarkable performance in this mass range, especially with the advance of performing mass spectrometry under entirely atmospheric conditions. This advance reduces the overall complexity and cost while increasing the limit of detection. However, this technique required the tracking of two mechanical modes and the accurate knowledge of mode shapes that may deviate from their ideal values, especially due to air damping. Here, we used a NEMS architecture with a central platform, which enables the calculation of mass by single-mode measurements. Experiments were conducted using polystyrene and gold nanoparticles to demonstrate the successful acquisition of mass spectra using a single mode with an improved areal capture efficiency. This advance represents a step forward in NEMS-MS, bringing it closer to becoming a practical application for the mass sensing of nanoparticles. © 2023 The Authors. Published by American Chemical Society.
Open Access
Graphene and carbon nanotubes interfaced electrochemical nanobiosensors for the detection of SARS-CoV-2 (COVID-19) and other respiratory viral infections: A review
(Elsevier BV, 2021-10) Özmen, E. N.; Kartal, Enise; Turan, Mehmet Bora; Yazıcıoğlu, A.; Niazi, J. H.; Qureshi, A.
Recent COVID-19 pandemic has claimed millions of lives due to lack of a rapid diagnostic tool. Global scientific community is now making joint efforts on developing rapid and accurate diagnostic tools for early detection of viral infections to preventing future outbreaks. Conventional diagnostic methods for virus detection are expensive and time consuming. There is an immediate requirement for a sensitive, reliable, rapid and easy-to-use Point-of-Care (PoC) diagnostic technology. Electrochemical biosensors have the potential to fulfill these requirements, but they are less sensitive for sensing viruses/viral infections. However, sensitivity and performance of these electrochemical platforms can be improved by integrating carbon nanostructure, such as graphene and carbon nanotubes (CNTs). These nanostructures offer excellent electrical property, biocompatibility, chemical stability, mechanical strength and, large surface area that are most desired in developing PoC diagnostic tools for detecting viral infections with speed, sensitivity, and cost-effectiveness. This review summarizes recent advancements made toward integrating graphene/CNTs nanostructures and their surface modifications useful for developing new generation of electrochemical nanobiosensors for detecting viral infections. The review also provides prospects and considerations for extending the graphene/CNTs based electrochemical transducers into portable and wearable PoC tools that can be useful in preventing future outbreaks and pandemics.
Open Access
Reservoir computing model using a single nonlinear nanoelectromechanical resonator at atmospheric conditions
(2024-07) Kartal, Enise
Reservoir computing is an alternative method to conventional systems using computationally expensive recurrent neural networks (RNNs). In this method, the training is performed only at the final layer of a nonlinear physical system functioning as a black box substituted instead of the hidden layers in RNNs requiring intensive training. This study suggests using a small nanoelectromechanical systems (NEMS) resonator with intrinsic nonlinearities for reservoir computing instead of relying on complicated feedback loops or spatially extended reservoirs as used in the earlier works. The linear classification is made possible by trans-forming the input data into a higher dimensional space, which is accomplished by utilizing the combination of the nonlinearity of the NEMS resonator and its fading memory behavior stemming from its transient response. Compared to reservoir computing using micromechanical resonators, the use of nanoelectromechanical resonators results in faster information processing, enabled by their rapid decay times arising from their small dimensions. Moreover, the implementation of the proposed NEMS reservoir computing architecture is more practical since it can operate at atmospheric conditions and occupies less space than its MEMS counterparts. This study emphasizes the efficient and feasible information processing potential of the suggested approach for a range of applications by the evaluation of its performance with the MNIST handwritten digit recognition task.