Advertisement

Information Processing in Chemical Sensing: Unified Evolution Coding by Stretched Exponential

  • B. A. Snopok
  • O. B. Snopok
Conference paper
Part of the NATO Science for Peace and Security Series A: Chemistry and Biology book series (NAPSA)

Abstract

Multicomponent biochemical environments (MBE) of various composition and aggregate state are an integral part of the world around us (living organisms, ponds, atmosphere, products of the food and pharmaceutical industry, etc.). Therefore, the possibility of identifying (i) individual target components in MBE (including potentially dangerous ones); (ii) the occurrence of some specific reactions in them; (iii) the identification or monitoring of the MBE state as a whole is a problem of significant interest, and this interest is constantly growing as a result of the acceleration of informatization of modern industrial society. At the same time, the presence of low-informative MBE components containing a large number of different, often unknown, compounds determines uncertainty in the interpretation of the results of their analysis by (bio)chemical sensors due to non-specific sorption and, accordingly, limits their widespread use. Making decisions in such uncertain conditions requires additional information regarding the fact that the sensor response is the result of the “selected” specific recognition process. In this paper, this important scientific problem is solved by analyzing the dynamics of the sensory response and setting unique kinetic process markers at the interface of phases. This not only allows to further characterize the interaction of the analyte with the sensitive layer, which facilitates the identification of the analyte, but also provides an effective tool for the development and optimization of sensor elements and systems based on physical transducers of the surface type.

Keywords

Multicomponent biochemical environments (Bio)chemical sensors Stretched exponential function 

Notes

Acknowledgements

This research was partly funded by the NATO Science for Peace and Security Programme under the Grant G5140 and Projects of the National Academy of Sciences of Ukraine.

References

  1. 1.
    Grimes CA, Dickey EC, Pishko MV (eds) (2005) Encyclopedia of sensors. American Scientific Publishers, ValenciaGoogle Scholar
  2. 2.
    Snopok BA (2008) Rapid methods for multiply determining potent xenobiotics based on the optoelectronic imaging. In: Bonca J, Kruchinin S (eds) Proceeding of NATO ARW “Electron transport in nanosystems”. Springer, pp 331–339Google Scholar
  3. 3.
    Snopok BA, Kruglenko IV (2002) Multisensor systems for chemical analysis: state-of-the-art in electronic nose technology and new trends in machine olfaction. Thin Solid Films 418(1):21–41Google Scholar
  4. 4.
    Burlachenko J, Kruglenko I, Snopok B, Persaud K (2016) Sample handling for electronic nose technology: state of the art and future trends. Trends Anal Chem 82:222–236Google Scholar
  5. 5.
    Ermakov V, Kruchinin S, Fujiwara A (2008) Electronic nanosensors based on nanotransistor with bistability behaviour. In: Bonca J, Kruchinin S (eds) Proceeding of NATO ARW “Electron transport in nanosystems”. Springer, pp 341–349Google Scholar
  6. 6.
    Ermakov V, Kruchinin S, Hori H, Fujiwara A (2007) Phenomena of strong electron correlastion in the resonant tunneling. Int J Mod Phys B 11:827–835Google Scholar
  7. 7.
    Snopok BA, Kostyukevich KV, Lysenko SI, Lytvyn PM, Lytvyn OS, Mamykin SV, Zynyo SA, Shepelyavyj PE, Venger EF, Kostyukevich SA, Shirshov YuM (2001) Optical biosensors based on the surface plasmon resonance phenomenon: optimization of the metal layer parameters. Semicond Phys Quantum Electron Optoelectron 4(1):56–69Google Scholar
  8. 8.
    Boltovets PM, Snopok BA (2009) Measurement uncertainty in analytical studies based on surface plasmon resonance. Talanta 80:466–472Google Scholar
  9. 9.
    Gromashevskii V, Tatyanenko N, Snopok B (2015) Effect of the formation of silicon oxide on the sign, magnitude and formation of surface charge upon water adsorption on a silicon surface. Theor Exp Chem 51(3):170–176Google Scholar
  10. 10.
    Snopok BA (2012) Theory and practical use of surface plasmon resonance for analytical purposes (Review). Theor Exp Chem 48(5):265–284Google Scholar
  11. 11.
    Boltovets PM, Boyko VR, Snopok BA (2013) Surface capturing of virion-antibody complexes: kinetic study. Mater Sci Eng Tech 44:112–118Google Scholar
  12. 12.
    Boltovets PM, Polischuk OM, Kovalenko OG, Snopok BA (2013) A simple SPR-based method for the quantification of the effect of potential virus inhibitors. Analyst 138:480–486Google Scholar
  13. 13.
    Snitka V, Naumenko DO, Ramanauskaite L, Kravchenko SA, Snopok BA (2012) Generation of diversiform gold nanostructures inspired by honey’s components: growth mechanism, characterization and shape separation by the centrifugation-assisted sedimentation. J Colloid Interface Sci 386:99–106Google Scholar
  14. 14.
    Snopok BA, Kruglenko IV (2005) Nonexponential relaxations in sensor arrays: forecasting strategy for electronic nose performance. Sensors Actuators B Chem 106(1):101–113Google Scholar
  15. 15.
    Snopok BA, Yurchenko M, Szekely L, Klein G, Kasuba E (2006) SPR based immuno-capture approach for in vitro analysis of protein complex formation: mapping of MRS18-2 binding site on retinoblastoma protein. Anal Bioanal Chem 386:2063–2073Google Scholar
  16. 16.
    Boltovets P, Shinkaruk S, Bennetau-Pelissero C, Bennetau B, Snopok B (2011) The effect of low pH on the glycitein-BSA conjugate interaction with specific antiserum: competitive inhibition study using surface plasmon resonance technique. Talanta 84(3):867–873Google Scholar
  17. 17.
    Naumenko D, Snopok BA, Serviene E, Bruzaite I, Snitka V (2013) Confocal Raman spectroscopy of biological objects in the face of photoinduced luminescence self-quenching. Theor Exp Chem 49:215–221Google Scholar
  18. 18.
    Snopok B, Kruglenko I (2015) Analyte induced water adsorbability in gas phase biosensors: the influence of ethinylestradiol on the water binding protein capacity. Analyst 140:3225–3232Google Scholar
  19. 19.
    Snopok BA, Darekar S, Kashuba EV (2012) Analysis of protein–protein interactions in a complex environment: capture of an analyte–receptor complex with standard additions of the receptor (CARSAR) approach. Analyst 137:3767Google Scholar
  20. 20.
    Manoilov EG, Kravchenko SA, Snopok BA (2017) Features of near-surface layer at monomolecular isotropic adsorption: nonequilibrium molecular dynamics simulation. Ukr J Phys 62(8):717–726Google Scholar
  21. 21.
    Manoilov EG, Kravchenko SA, Snopok BA (2017) Features of cooperative adsorption described by sticking probability that depends on the number of neighbors. Theor Exp Chem 53(1):17–24Google Scholar
  22. 22.
    Volkenshteyn FF (1987) Electronic processes on surface of semiconductor at chemisorptions. Nauka, MoskawGoogle Scholar
  23. 23.
    Snopok BA (2014) Nonexponential kinetics of surface chemical reactions (Review). Theor Exp Chem 50(2):67–95Google Scholar
  24. 24.
    Haken H (1984) Synergetics – theory of nonequilibrium phase transitions and formation of spatio-temporal patterns. In: Grünewald H (ed) Chemistry for the future. Pergamon Press, OxfordGoogle Scholar
  25. 25.
    Haken H (1981) Erfolgsgeheimnisse der Natur: Synergetik, die Lehre vom Zusammenwirken. Deutsche Verlags-AnstaltGoogle Scholar
  26. 26.
    Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51:661–703MathSciNetCrossRefADSGoogle Scholar
  27. 27.
    Eni-Olorunda I, Sadana A (2010) Kinetics of chemo/biosensors. In: Zourob M (ed) Recognition receptors in biosensors. Springer, New YorkGoogle Scholar
  28. 28.
    Sworakowski J, Matczyszyn K (2007) Non-exponential decays in first-order kinetic processes. The case of “Squeezed exponential”. Acta Phys Pol A 112:S153–S159CrossRefGoogle Scholar
  29. 29.
    Laherrere J, Sornette D (1998) Stretched exponential distributions in nature and economy: “Fat tails” with characteristic scales. Eur Phys J B 2:525–539CrossRefADSGoogle Scholar
  30. 30.
    Brouers F, Sotolongo-Costa O (2006) Generalized fractal kinetics in complex systems (application to biophysics and biotechnology) Phys A 368:165–175Google Scholar
  31. 31.
    Schweiss R, Mirsky VM, Wolfbeis OS (1998) Capacity study of self-assembled alkylthiol monolayers: surface charge effect and kinetics of surfactants adsorption. Mater Sci Forum 287–288:427–430CrossRefGoogle Scholar
  32. 32.
    Boltovets P, Shinkaruk S, Vellutini L, Snopok B (2017) Self-tuning interfacial architecture for Estradiol detection by surface plasmon resonance biosensor. Biosens Bioelectron 90:91–95CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.V.E. Lashkaryov Institute of Semiconductor PhysicsNational Academy of Sciences of UkraineKyivUkraine

Personalised recommendations