Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T23:36:29.919Z Has data issue: false hasContentIssue false
  • English
  • Français

Polyfuran-based chemical sensors: reactivity analysis via Fukui indexes and reactive molecular dynamics

Published online by Cambridge University Press:  13 April 2020

Leonardo Gois Lascane ,
Eliezer Fernando Oliveira  and
Augusto Batagin-Neto
Leonardo Gois Lascane
Affiliation:
São Paulo State University (UNESP), Campus of Itapeva, Itapeva, SP, Brazil
Eliezer Fernando Oliveira*
Affiliation:
Gleb Wataghin Institute of Physics, University of Campinas (UNICAMP), Campinas, SP, Brazil Center for Computational Engineering & Sciences (CCES), University of Campinas (UNICAMP), Campinas, SP, Brazil Department of Materials Science and Nanoengineering, Rice University, Houston, TX, United States
Augusto Batagin-Neto
Affiliation:
São Paulo State University (UNESP), Campus of Itapeva, Itapeva, SP, Brazil
*
*(Email: [email protected])
Get access

Abstract

In the present study we employ electronic structure calculations (based on Density Functional Theory -DFT approach) and Fully Atomistic Reactive Molecular Dynamics (FARMD) simulations (based on ReaxFF reactive force field) to evaluate the reactivity of branched polyfuran (PF) derivatives and identify promising systems for chemical sensing. Condensed-to-atoms Fukui indexes (CAFI) were employed to identify the most reactive sites on the oligomers structure. The chemical sensing abilities of the most promising systems were evaluated via FARMD simulations in the presence of distinct gaseous compounds. The results indicate the derivatives PF-CCH and PF-NO₂ (i.e. CCH and NO2 as side groups) as the most promising systems for chemical sensor applications, presenting higher reactivity on the most accessible sites. An interesting correspondence between DFT and MD results was also identified, suggesting the plausibility of using CAFI parameters for the identification of improved materials for chemical sensors.

Keywords

chemical substitutionelectronic structurepolymeroptical properties
Type
Articles
Information
MRS Advances , Volume 5 , Issue 10: Energy and Environment , 2020 , pp. 497 - 503
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References:

Liu, X., Cheng, S., Liu, H., et al., Sensors 12, 9635 (2012).CrossRefGoogle ScholarPubMed
Fortuna, L., Soft sensors for monitoring and control of industrial processes, 1st ed., Springer: London, 2007.Google Scholar
Hrubovčáková, M., Dudrová, E., Hryha, E., et al., Adv. Mater. Sci. Eng. 2013, 1 (2013).CrossRefGoogle Scholar
Villoria-Sáez, P., Tam, V. W. Y.,would like to thank M. del Río Merino, et al., J. Clean. Prod. 127, 49 (2016).CrossRefGoogle Scholar
Bai, H., Shi, G., Sensors 7, 267 (2007).CrossRefGoogle Scholar
Yoon, H., Nanomaterials 3, 524 (2013).CrossRefGoogle ScholarPubMed
Zaikov, G. E., Interaction of polymers with polluted atmosphere nitrogen oxides, 1st ed., iSmithers: Shawbury, 2009.Google Scholar
Zeng, W., Zhang, M. Q., Rong, M. Z., Zheng, Q., Sens. Actuators B Chem. 124, 118 (2007).CrossRefGoogle Scholar
Sharma, S., Zamoshchik, N., Bendikov, M., Isr. J. Chem. 54, 712 (2014).CrossRefGoogle Scholar
Gandini, A., Prog. Polym. Sci. 22, 1203 (1997).CrossRefGoogle Scholar
Carey, F. A., Sundberg, R. J., Advanced Organic Chemistry: Part A: Structure and Mechanisms, 1st ed. Springer Science & Business Media, 2007.Google Scholar
Oliveira, E. F., Lavarda, F. C., Polym. Eng. Sci. 56, 479 (2016).CrossRefGoogle Scholar
Oliveira, E. F., Lavarda, F. C., Mater. Chem. Phys. 148, 923 (2014).CrossRefGoogle Scholar
Stewart, J. J. P., J. Mol. Model. 13, 1173 (2007).CrossRefGoogle Scholar
Stewart, J. J. P., MOPAC2016. Stewart Computational Chemistry, Colorado Springs, CO, USA, 2018Google Scholar
Becke, A. D., J. Chem. Phys. 98, 5648 (1993).CrossRefGoogle Scholar
Frisch, M. J., Trucks, G. W., Schlegel, H. B., et al., Gaussian 09. Gaussian, Inc., Wallingford, CT, 2009.Google Scholar
Yang, W., J Mortier, W., J. Am. Chem. Soc. 108, 5708 (1986).CrossRefGoogle Scholar
Batagin-Neto, A., Bronze-Uhle, E.S., Vismara, M., et al., Curr. Phys. Chem. 3, 431 (2013).CrossRefGoogle Scholar
Bronze-Uhle, E. S., Batagin-Neto, A., Lavarda, F. C., Graeff, C. F. O., J. Appl. Phys. 110, 073510 (2011).CrossRefGoogle Scholar
Cesarino, I., Simões, R. P., Lavarda, F. C., Batagin-Neto, A., Electrochimica Acta 192, 8 (2016).CrossRefGoogle Scholar
Martins, L. M., de Faria Vieira, S., Baldacim, G. B., et al., Dyes Pigments 148, 81 (2018).CrossRefGoogle Scholar
Mandú, L. O., Batagin-Neto, A., J. Mol. Model 24, 157 (2018).CrossRefGoogle Scholar
Maia, R. A., Ventorim, G., Batagin-Neto, A., J. Mol. Model. 25, 228 (2019).CrossRefGoogle Scholar
do Amaral Rodrigues, J., de Araújo, A. R., Pitombeira, N. A., et al., Int. J. Biol. Macromol. 128, 965 (2019).CrossRefGoogle Scholar
Rodrigues de Araújo, A., Iles, B., de Melo Nogueira, K., et al., Mart. J. Ethnopharmacol. 240, 111941 (2019).CrossRefGoogle Scholar
Roy, R. K., Pal, S., Hirao, K., J. Chem. Phys. 110, 8236 (1999).CrossRefGoogle Scholar
De Proft, F., Van Alsenoy, C., Peeters A, A., et al., J. Comput. Chem. 23, 1198 (2002).CrossRefGoogle Scholar
Leach, A. R., Molecular Modelling: Principles and Applications, 1st ed. Prentice Hall, 2001Google Scholar
van Duin, A. C. T., Dasgupta, S., Lorant, F., Goddard, W. A., J. Phys. Chem. A 105, 9396 (2001)CrossRefGoogle Scholar
Fantauzzi, D., Mueller, J. E., Sabo, L., et al., Chem. Phys. Chem. 16, 2797 (2015).CrossRefGoogle Scholar
Plimpton, S. J., Comput. Phys. 117, 1 (1995).CrossRefGoogle Scholar