Yıl 2018, Cilt 22, Sayı 4, Sayfalar 1086 - 1094 2018-08-01

A new tool for prediction of phase transitions in liquid crystals
Sıvı kristallerde faz geçişlerinin tahmini için yeni bir araç

murat beken [1]

129 239

In this article, fundamental analysis of C37H59NO2, C37H59NO3, and C41H67NO2 from among liquid crystals is conducted via Differential Thermal Analysis (DTA) device in high pressure environment. Phase transition temperature, entalphy, and entrophy of these liquid crystals are observed. In addition, an Artificial Neural Network (ANN), which is a method of Artificial Intelligence, is modeled. Then, output values of ANN model and DTA device are compared, and correlation between them is demonstrated. For the values which are not measured with DTA device, outputs are produced by ANN model. In this article, three layered feed-forward back propagation ANN model is used. With this approach, it is proved that, ANN is a resourceful method for prediction in studies conducted about phase transition.

Lo Bu araştırmada Sıvı Kristallerden C37H59NO2, C37H59NO3 ve C41H67NO2 nin yüksek basınç ortamında Diferansiyel Termal Analiz (DTA) gereci ile Termal Analizi yapılmıştır. İncelenen Sıvı Kristallerin Faz geçiş sıcaklıkları, Entalpi ve Entropi değerleri araştırılmıştır. Ayrıca yapay zeka yöntemlerinden Yapay Sinir Ağlarını (YSA) kullanarak, yapay sinir ağlarından alınan sonuçlar ile belirli aralıklarda cihaz ile yapılan ölçümlerden elde edilen sonuçların uyumu gösterilmiştir. Aralık dışında kalan değerler için de yapay sinir ağlarından elde edilen değerler üretilmiştir. Bu çalışmada üç katmanlı ileri beslemeli geriye yayınımlı YSA modeli kullanıldı. Bu yaklaşım ile, YSA’nın faz geçişleri üzerinde yapılan çalışmaların tahmini için yararlı bir araç olduğunu kanıtlamıştır.

  • [1] N. Clayton, N. Musolino, E. Giannini, V. Garnier, and R. Flükiger, «New Apparatus for DTA at 2000 bar: Thermodynamic Studies on Au, Ag, Al and HTSC Oxides”, Supercond. Sci. Technology,» Supercond. Sci. Technology, cilt 17, no. 3, pp. 395-406, 2004.
  • [2] A. Langier-Kuzniarowa, «Standardization in Thermal Analysis,» J. Therm. Anal. Calorimetry, cilt 24, p. 913, 1984.
  • [3] S. Yariv, «The Role of Charcoal on DTA Curves of Organo-Clay Complexes: an Overview,» Applied Clay Science, cilt 29, pp. 225-236, 2004.
  • [4] Alain F Plante, José M Fernández, J Leifeld, «Application of Thermal Analysis Techniques in Soil Science,» Geoderma, cilt 153, no. 1-2, pp. 1-10, 2009.
  • [5] G. Tammann, Z.S. Phys. Chem, cilt 20, p. 743, 1912.
  • [6] P.W.Bridgman, Phys.Review3, p. 126, 1914.
  • [7] R. L. Stone, «Differential Thermal Analysis by The Dynamic Gas Technique,» Anal. Chem., cilt 32, p. 1582, 1960.
  • [8] L. Kaufman, Material Science and Engineering Series, New York: McGraw-Hill, 1963.
  • [9] M. Beken, «The Neural Network and Multivariate Linear Regression Approach for Observing Phase Transitions of Polymers With The Differential Thermal Analysis Method,» J. Therm. Anal. Calorimetry, cilt 101, no. 1, pp. 339-347, 2010.
  • [10] S. Agatonovic-Kustrin and R. Beresford, «Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research,» Jour. Of Pharm. Biol. Anal. , cilt 22, pp. 717-727, 2000.
  • [11] M. Culloch and W. Pitts, «A Logical Calculus Of The Idea Immanent In Nervous Activity,» Bulletin of Math. W. S. Biophys., cilt 5, pp. 115-133, 1943.
  • [12] O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, Springer, 2005.
  • [13] Kotfica, E. Tomaszewicz and M., «Application of Neural Networks in Analysis of Thermal Decomposition of CoSO4·7H2O,» J. Therm. Anal. Calorimetry, cilt 74, p. 583, 2003.
  • [14] G. Zhang, B.E. Patuwo, M.Y. Hu, «Forecasting with Artificial Neural Networks: The State of the Art,» Inter. Journal of Forecasting, cilt 15, no. 1998, pp. 35-62, 1998.
  • [15] R. C. O. Sebastiao, J. P. Braga, and M. I. Yoshida, «Competition Between Kinetic Models in Thermal Decomposition: Analysis by Artificial Neural network,» Thermochimica Acta, cilt 412, no. 1-2, pp. 107-111, 2004.
  • [16] J. Straszko, A. Biedunkiewicz, and A. Strzelczak, «Application of Artificial Neural Nerworks in Oxidation Kinetic Analysis of Nanocomposites,» Polish Journal of Chemical Technology, cilt 10, no. 3, pp. 21-28, 2008.
  • [17] J. A. Conesa, J. A. Caballero, and J. A. Reyes-Labarta, «Artificial Neural Network for Modelling Termal Decompositions,» J. Anal. Appl. Pyrolysis, cilt 71, pp. 343-352, 2004.
  • [18] M. Beken, Yüksek Basınç Altında Çalışan Diferansiyel Termal Analiz Cihazının Geliştirilmesi, İstanbul: Yıldız Teknik Üniversitesi, 2002.
  • [19] M. Beken, «Artifıcial Neural Network Prediction for Thermal Decomposition of Potassıum Nitrate (KNO3) and Benzoic Acid (C6H5COOH),» Mod. Phys. Lett. B, cilt 24, no. 17, pp. 1855-1868, 2010.
  • [20] R. Erb, «Introduction to Backpropagation Neural Network Computation,» Pharmaceutical Research, cilt 10, no. 2, pp. 165-170, 1993.
  • [21] N. Sbirrazzuolia, D. Brunel, and L. Elegant, «Neural Networks for Kinetic Parameters Determination, Signal Filtering and Deconvolution in Thermal Analysis,» J. Therm. Anal. Calorimetry, cilt 49, no. 3, pp. 1553-1564, 1997.
  • [22] B. Widrow and M. E. Ho, «Adaptive Switching Circuits,» IRE WESCON ConventionRecord, pp. 96-104, 1960.
Konular Bilgisayar Mühendisliği ve Bilişim, Fizik ve Fizik Mühendisliği, Malzeme Bilimi, Temel Bilimler (Genel)
Yayımlanma Tarihi August 2018
Dergi Bölümü Araştırma Makalesi
Yazarlar

Yazar: murat beken
Kurum: BEYKENT UNIV
Ülke: Turkey


Bibtex @araştırma makalesi { saufenbilder290944, journal = {Sakarya University Journal of Science}, issn = {1301-4048}, eissn = {2147-835X}, address = {Sakarya Üniversitesi}, year = {2018}, volume = {22}, pages = {1086 - 1094}, doi = {10.16984/saufenbilder.290944}, title = {A new tool for prediction of phase transitions in liquid crystals}, key = {cite}, author = {beken, murat} }
APA beken, m . (2018). A new tool for prediction of phase transitions in liquid crystals. Sakarya University Journal of Science, 22 (4), 1086-1094. DOI: 10.16984/saufenbilder.290944
MLA beken, m . "A new tool for prediction of phase transitions in liquid crystals". Sakarya University Journal of Science 22 (2018): 1086-1094 <http://www.saujs.sakarya.edu.tr/issue/31264/290944>
Chicago beken, m . "A new tool for prediction of phase transitions in liquid crystals". Sakarya University Journal of Science 22 (2018): 1086-1094
RIS TY - JOUR T1 - A new tool for prediction of phase transitions in liquid crystals AU - murat beken Y1 - 2018 PY - 2018 N1 - doi: 10.16984/saufenbilder.290944 DO - 10.16984/saufenbilder.290944 T2 - Sakarya University Journal of Science JF - Journal JO - JOR SP - 1086 EP - 1094 VL - 22 IS - 4 SN - 1301-4048-2147-835X M3 - doi: 10.16984/saufenbilder.290944 UR - http://dx.doi.org/10.16984/saufenbilder.290944 Y2 - 2017 ER -
EndNote %0 Sakarya University Journal of Science A new tool for prediction of phase transitions in liquid crystals %A murat beken %T A new tool for prediction of phase transitions in liquid crystals %D 2018 %J Sakarya University Journal of Science %P 1301-4048-2147-835X %V 22 %N 4 %R doi: 10.16984/saufenbilder.290944 %U 10.16984/saufenbilder.290944