Nuclear Engineering and Technology

  • 제53권12호
  • /
  • Pages.4130-4136
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  • 2021
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Gadolinium- and lead-containing functional terpolymers for low energy X-ray protection

  • Zhang, Yu-Juan (School of Chemistry and Chemical Engineering, Yangzhou University) ;
  • Guo, Xin-Tao (Department of Materials Research, AVIC Manufacturing Technology Institute) ;
  • Wang, Chun-Hong (School of Chemistry and Chemical Engineering, Yangzhou University) ;
  • Lu, Xiang An (Guangling College of Yangzhou University) ;
  • Wu, De-Feng (School of Chemistry and Chemical Engineering, Yangzhou University) ;
  • Zhang, Ming (School of Chemistry and Chemical Engineering, Yangzhou University)
  • 투고 : 2021.04.01
  • 심사 : 2021.06.12
  • 발행 : 2021.12.25
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초록

By polymerization of gadolinium methacrylate (Gd (MAA)3), lead methacrylate (Pb(MAA)2) and methyl methacrylate (MMA), Gd and Pb were chemically bonded into polymers. The X-ray shielding performance was evaluated by Monte Carlo simulation method, and the results showed that the more metal functional organic monomer, the better the shielding performance of terpolymers. When the X-ray energy is 65 keV, Gd (MAA)3-containing polymers have better shielding performance than Pb(MAA)2-containing polymers. Gd could compensate for the weak absorption region of Pb. Therefore, polymers containing both Gd and Pb enhanced shielding efficiency against X-ray in various low-energy ranges. For obtaining terpolymers with uniform monomer compositions, the relationship between the monomer composition of the terpolymers and the conversion level was optimized by calculating the reactivity ratios. The value of reactivity ratios of r (Gd (MAA)3/Pb(MAA)2), r (Pb(MAA)2/Gd (MAA)3), r (Gd (MAA)3/MMA), r (MMA/Gd (MAA)3), r (Pb(MAA)2/MMA) and r (MMA/Pb(MAA)2) was 0.483, 0.004, 0.338, 2.508, 0.255, 0.029. The terpolymers with uniform monomer composition could be obtained by controlling the monomer compositions or conversion levels. The results can provide new radiation protection materials and contribute to the improvement in nuclear safety.

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과제정보

This study was financially supported by the Aviation Industry Joint Fund (No.6141B05080407) and Natural Science Research Project of Guangling College of Yangzhou University (No. ZKZD18004).

참고문헌

  1. A. Alhudhaif, K. Polat, O. Karaman, Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images, Expert Syst. Appl. 180 (2021) 115141, 115141. https://doi.org/10.1016/j.eswa.2021.115141
  2. S. Nambiar, J.T.W. Yeow, Polymer-composite materials for radiation protection, ACS Appl. Mater. Interfaces 4 (11) (2012) 5717-5726. https://doi.org/10.1021/am300783d
  3. P.F. Lou, X.B. Teng, Q.X. Jia, Y.Q. Wang, L.Q. Zhang, Preparation and structure of rare earth/thermoplastic polyurethane fiber for X-ray shielding, J. Appl. Polym. Sci. 136 (17) (2019). https://doi:10.1002/app.47435.
  4. L. Liu, L. He, C. Yang, W. Zhang, R.G. Jin, L.Q. Zhang, In situ reaction and radiation protection properties of Gd(AA)(3)/NR composites, Macromol. Rapid Commun. 25 (12) (2004) 1197-1202. https://doi.org/10.1002/marc.200400077
  5. S. Jayakumar, T. Saravanan, J. Philip, Preparation, characterization and X-ray attenuation property of Gd2O3-based nanocomposites, Appl. Nanosci. 7 (8) (2017) 919-931. https://doi.org/10.1007/s13204-017-0631-6
  6. M.I. Sayyed, A.A. Ati, M.H.A. Mhareb, K.A. Mahmoud, K.M. Kaky, S.O. Baki, M.A. Mahdi, Novel tellurite glass (60-x)TeO2-10GeO(2)-20ZnO-10BaO-xBi(2) O(3)for radiation shielding, J. Alloys Compd. 844 (2020). https://doi:10.1016/j.jallcom.2020.155668.
  7. P.P. Jiang, J.B. Chen, X.M. Lin, Chinese Pat., 101319025, Jiangnan University, Peop. Rep, China, 2010.
  8. N. Haruo, U. Hiroshi, N. Kunikazu, Japanese Pat., 53063310, Kyowa Gas Chemical Industry Co., Ltd., Japan, 1978.
  9. C.H. Wang, S. Wang, Y.J. Zhang, Z.F. Wang, J.L. Liu, M. Zhang, Self-polymerization and co-polymerization kinetics of gadolinium methacrylate, J. Rare Earths 36 (3) (2018) 298-303. https://doi.org/10.1016/j.jre.2017.09.015
  10. Y.J. Zhang, X.T. Guo, C.H. Wang, D.F. Wu, M. Zhang, Self-polymerization and co-polymerization kinetics of lead methacrylate, Rare Met. 40 (3) (2021) 736-742. https://doi.org/10.1007/s12598-019-01358-4
  11. N. Kazemi, T.A. Duever, A. Penlidis, Demystifying the estimation of reactivity ratios for terpolymerization systems, AIChE J. 60 (5) (2014) 1752-1766. https://doi.org/10.1002/aic.14439
  12. A. Jukic, M. Rogosic, E. Vidovic, Z. Janovic, Terpolymerization kinetics of methyl methacrylate or styrene/dodecyl methacrylate/octadecyl methacrylate systems, Polymer International, Poly. Int 56 (1) (2006) 112-120.
  13. A.D. Azzahari, R. Yahya, M.R. Ahmad, M.B. Zubir, New terpolymers from nbutyl acrylate, glycidyl methacrylate and tetrahydrofurfuryl acrylate: synthesis, characterisation and estimation of reactivity ratios. Fibers and Polymers, Fiber, Polymja 15 (3) (2014) 437-445. https://doi.org/10.1007/s12221-014-0437-z
  14. I. Soljic, A. Jukic, Z. Janovic, Terpolymerization kinetics of N,N-dimethylaminoethyl methacrylate/alkyl methacrylate/styrene systems. Polymer Engineering & Science, Polym. Eng. Sci. 50 (3) (2009) 577-584.
  15. K.A. Mahmoud, M.I. Sayyed, O.L. Tashlykov, Gamma ray shielding characteristics and exposure buildup factor for some natural rocks using MCNP-5 code, Nucl. Eng. Technol. 51 (2019) 1835-1841. https://doi.org/10.1016/j.net.2019.05.013
  16. O. Agar, M.I. Sayyed, F. Akman, H.O. Tekin, M.R. Kacal, An extensive investigation on gamma ray shielding features of Pd/Ag-based alloys, Nucl. Eng. Technol. 51 (3) (2019) 853-859. https://doi.org/10.1016/j.net.2018.12.014
  17. Y. Wang, G.K. Wang, T. Hu, S.P. Wen, S. Hu, L. Liu, Enhanced photon shielding efficiency of a flexible and lightweight rare earth/polymer composite: a Monte Carlo simulation study, Nucl. Eng. Technol. 52 (7) (2020) 1565-1570. https://doi.org/10.1016/j.net.2019.12.028
  18. J.J.M. Goeij, Nuclear analytical methods in the life sciences, Biol. Trace Elem. Res. 43-45 (1) (1994) 9-17. https://doi.org/10.1007/BF02917295
  19. S. Srikanth, G.J.N. Raju, Quantitative study of trace elements in coal and coal related ashes using PIXE, J. Geol. Soc. India 94 (5) (2019) 533-537. https://doi.org/10.1007/s12594-019-1351-1
  20. W. Maenhaut, Present role of PIXE in atmospheric aerosol research, Nucl. Instrum. Methods B 363 (2015) 86-91. https://doi.org/10.1016/j.nimb.2015.07.043
  21. J. Reyes-Herrera, J. Miranda, O.G. de Lucio, Simultaneous PIXE and XRF elemental analysis of atmospheric aerosols, Microchem. J. 120 (2015) 40-44. https://doi.org/10.1016/j.microc.2015.01.004
  22. J. Cruz, M. Manso, V. Corregidor, R.J.C. Silva, E. Figueiredo, M.L. Carvalho, L.C. Alves, Surface analysis of corroded XV-XVI century copper coins by µ-XRF and µ-PIXE/µ-EBS self-consistent analysis, Mater. Char. 161 (2020). https://doi:10.1016/j.matchar.2020.110170.
  23. X.F. Li, G.F. Wang, J.H. Chu, L.D. Yu, Charge integration in external PIXE-PIGE for the analysis of aerosol samples, Instrum. Meth. B. 289 (2012) 1-4. https://doi.org/10.1016/j.nimb.2012.08.009
  24. E. Punzon-Quijorna, M. Kelemen, P. Vavpeti, R. Kavalar, S.K. Fokter, Particle induced x-ray emission (PIXE) for elemental tissue imaging in hip modular prosthesis fracture case, Instrum. Meth. B. 462 (2020) 182-186. https://doi.org/10.1016/j.nimb.2019.10.019
  25. A.R. Justino, N. Canha, C. Gamelas, J.T. Coutinho, S.M. Almeida, Contribution of micro-pixe to the characterization of settled dust events in an urban area affected by industrial activities, J. Radioanal. Nucl. Chem. 322 (3) (2019) 1953-1964. https://doi.org/10.1007/s10967-019-06860-8
  26. K. Nadeem, J. Hussain, N.U. Haq, A.U. Haq, I. Ahmad, A proton induced X-ray emission (PIXE) analysis of concentration of major/trace and toxic elements in broiler gizzard and flesh of Tehsil Gujar Khan area in Pakistan, Nucl. Eng. Technol. 51 (8) (2019) 2042-2049. https://doi.org/10.1016/j.net.2019.06.005
  27. S. Kavlak, A. Guner, Z.M.O. Rzayev, Functional terpolymers containing vinylphosphonic acid: the synthesis and characterization of poly(vinylphosphonic acid-co-styrene-co-maleic anhydride), J. Appl. Polym. Sci. 125 (5) (2012) 3617-3629. https://doi.org/10.1002/app.36522
  28. P.G. Sanghvi, N.K. Pokhriyal, S. Devi, Effect of partitioning of monomer on the reactivities of monomers in microemulsion, J. Appl. Polym. Sci. 84 (10) (2002) 1832-1837. https://doi.org/10.1002/app.10401
  29. T. Alfrey, G. Goldfinger, The mechanism of copolymerization, The Journal of Chemical Physics, J. Chem. Phys. 12 (6) (1944) 205-209. https://doi.org/10.1063/1.1723934
  30. N. Kazemi, T.A. Duever, A. Penlidis, Demystifying the estimation of reactivity ratios for terpolymerization systems, AIChE J. 60 (5) (2014) 1752-1766. https://doi.org/10.1002/aic.14439
  31. N. Kazemi, T.A. Duever, A. Penlidis, A powerful estimation scheme with the error-in-variables-model for nonlinear cases: reactivity ratio estimation examples, Comput. Chem. Eng. 48 (2013) 200-208. https://doi.org/10.1016/j.compchemeng.2012.08.015
  32. A. Scott, N. Kazemi, A. Penlidis, AMPS/AAm/AAc terpolymerization: experimental verification of the EVM framework for ternary reactivity ratio estimation, Processes 5 (4) (2017) 9-24. https://doi.org/10.3390/pr5010009
  33. M. Riahinezhad, N. Kazemi, N. McManus, A. Penlidis, Effect of ionic strength on the reactivity ratios of acrylamide/acrylic acid (sodium acrylate) copolymerization, J. Appl. Polym. Sci. 131 (20) (2014) 40949.
  34. N. Kazemi, T.A. Duever, A. Penlidis, Reactivity ratio estimation from cumulative copolymer composition Data,Macromol, React. Eng. 5 (9-10) (2011) 385-403. https://doi.org/10.1002/mren.201100009
  35. F.K. Yousefi, A. Jannesari, S. Pazokifard, M.R. Saeb, A.J. Scott, A. Penlidis, Reactivity ratio estimation from cumulative copolymer composition Data,- Macromol, React. Eng. 13 (4) (2019) 1900014. https://doi.org/10.1002/mren.201900014
  36. I. Skeist, Copolymerization: the composition distribution curve, J. Am. Chem. Soc. 68 (9) (1946) 1781-1784. https://doi.org/10.1021/ja01213a031
  37. P.M. Reilly, H. Patino-Leal, A bayesian study of the error-in-variables model, Technometrics 23 (3) (1981) 221-231. https://doi.org/10.2307/1267784
  38. M. Dube, R.A. Sanayei, A. Penlidis, K.F. O'Driscoll, P.M. Reilly, A microcomputer program for estimation of copolymerization reactivity ratios, J. Polym. Sci., Polym. Chem. Ed. 29 (5) (1991) 703-708. https://doi.org/10.1002/pola.1991.080290512
  39. A.L. Polic, T.A. Duever, A. Penlidis, Case studies and literature review on the estimation of copolymerization reactivity ratios, J. Polym. Sci., Polym. Chem. Ed. 36 (5) (1998) 813-822. https://doi.org/10.1002/(SICI)1099-0518(19980415)36:5<813::AID-POLA14>3.0.CO;2-J
  40. A. Scott, A. Penlidis, Computational package for copolymerization reactivity ratio estimation: improved access to the error-in-variables-model, Processes, Processes 6 (1) (2018) 8. https://doi.org/10.3390/pr6010008
  41. A.J. Scott, A. Penlidis, Binary vs. ternary reactivity ratios: appropriate estimation procedures with terpolymerization data, Eur. Polym. J. 105 (2018) 442-450. https://doi.org/10.1016/j.eurpolymj.2018.06.021