Sensörlerde Nano Bor Nitrür ve Karbon Nanotüp: YSA ile Karşılaştırmalı İnceleme

Abstract

Karbon nanotüp (CNT) ve nano boyutlu bor nitrür (BN) malzemeler, uyumlulukları ve yapısal çok yönlülükleri nedeniyle sensör geliştirmede yaygın olarak kullanılan destek elemanlarıdır. Literatürde YSA ile fiziksel parametrelerin bu kadar detaylı bir şekilde karşılaştırılarak incelendiği çalışmalara nadiren rastlanmaktadır. Sunulan çalışmada farklı miktarlarda nanomalzemeler, su, gliserol ve jelatin içerikli sensörler hazırlanmıştır. Oluşturulan sensörlerin parlaklık (20°-60°), renk değişimi (ΔE) ve sertlik parametreleri ölçülmüştür. Ölçüm verileri hem istatistiksel yöntemler hem de Çok Katmanlı Algılayıcı (MLP) yapay sinir ağı modeli kullanılarak değerlendirilmiştir. Transfer fonksiyonu olarak Tansig ve performans fonksiyonu olarak ortalama kare hata kullanılmıştır. Hem eğitim hem de test başarısı için en iyi sınıflandırma %99,9 doğrulukla sertlik parametresinde yapılmış, bunu %94,9 doğrulukla ΔE takip etmiştir. ΔE ve parlaklık, 60° test koşullarında 6 ml su ile, 20°'de parlaklık ve sertlik için de 9 ml su ile yapılan deneylerde en etkili ortak yapısal malzemeler olmuştur. ΔE için 0,12 g BN ve 0,06 g CNT ve 60° parlaklık için hem BN hem de CNT için 0,06 g değişimler üzerinde en büyük etkiyi sağlamıştır. Hem BN hem de CNT için 0,06 g'lık deneysel koşullar da sertlik değişimi için en etkili parametreler olmuştur.

Keywords

• Sensör
• BN
• CNT
• MLP
• ΔE
• parlaklık

Nano Boron Nitride and Carbon Nanotube in Sensors: Comparative Investigation with ANN

Abstract

Carbon nanotube (CNT) and nanosized boron nitride (BN) materials are widely used support elements in sensor development due to their compatibility and structural versatility. Studies examining physical parameters in such a detailed comparison with ANN are rarely encountered in the literature. The presented study prepared sensors with different amounts of nanomaterials, water, glycerol and gelatin content. The created sensors' glossiness (20°-60°), colour change (ΔE) and hardness parameters were measured. The measurement data were evaluated using statistical methods and the Multilayer Perceptron (MLP) artificial neural network model. Tansig was used as the transfer function, and the mean square error was used as the performance function. The best classification for training and test success was made in the hardness parameter with 99.9% accuracy, followed by ΔE with 94.9% accuracy. ΔE and glossiness were the most effective common structural materials for the experiments with 6 ml of water at 60° test conditions and 9 ml of water for glossiness at 20° and hardness. For ΔE, 0.12 g BN and 0.06 g CNT and for 60° gloss, 0.06 g for both BN and CNT provided the most significant effect on the changes. The experimental conditions of 0.06 g for BN and CNT were also the most influential parameters for the hardness change.

Keywords

• Sensor
• BN
• CNT
• MLP
• ΔE
• glossiness

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