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Ahmed Rida GALALY, Guido VAN OOST. Fast inactivation of microbes and degradation of organic compounds dissolved in water by thermal plasma[J]. Plasma Science and Technology, 2018, 20(8): 85504-085504. DOI: 10.1088/2058-6272/aac1b7
Citation: Ahmed Rida GALALY, Guido VAN OOST. Fast inactivation of microbes and degradation of organic compounds dissolved in water by thermal plasma[J]. Plasma Science and Technology, 2018, 20(8): 85504-085504. DOI: 10.1088/2058-6272/aac1b7

Fast inactivation of microbes and degradation of organic compounds dissolved in water by thermal plasma

Funds: GVO acknowledges the partial financial support from MEPhI in the fram
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  • Received Date: December 13, 2017
  • The multifunctionality and the advantages of thermal plasma for the fast inactivation of viable cells and degradation of organic compounds dissolved in waste water are presented. A complete bacterial inactivation process was observed and studied using a thermal plasma treatment source with very short application times, in particular for Staphylococcus aureus bundle spore survival. The survival curves and analyses of the experimental data of the initial and final densities of S. aureus bacteria show a dramatic inhibitory effect of the plasma discharge on the residual bacteria survival ratio. As the exposure time increased, the inactivation process rate increased for direct exposure more than it did for indirect exposure. The evaluation of direct and indirect exposure was based on the analysis of the ultraviolet spectrum from the absorbance spectra of the organic compound dye called benzene sulfonate (C16H11N2NaO4S) and of viable cells called S. aureus. Organic compounds were degraded and viable cells were killed in a short time by thermal plasma. Moreover, analyses of total carbon, total organic carbon, and total inorganic carbon showed a fast decrease in organically bound carbon, however, this was not as fast as the absorbance spectra revealed by the exposure time increasing more for direct exposure than indirect exposure. After 100 s of exposure to the organic compound dye the removal had a maximun of 40% for samples with indirect exposure to the plasma and a maximum of 90% for samples with the direct exposure. For both samples, where some organic contaminants still remained in treated water, four electrolytes (KCl, NaCl, Na2SO4, and CH3COONa) were added to be effective for complete sterilization, reaching a purity of 100%. A proposal is made for an optimized thermal plasma water purification system (TPWPS) to improve fast inactivation of microbes and the degradation of organic compounds dissolved in water (especially for direct exposure rather than indirect exposure) using a hybrid plasma torch with an electrical power of 125 kW (500 V–250 A) producing a high-temperature (10 000 K–19 000 K) plasma jet with a maximum gas consumption of 28 mg s−1.
  • [1]
    Rida Galaly A and Van Oost G 2017 Plasma Sci. Technol. 19 105503
    [2]
    Morent R et al 2009 Prog. Org. Coat. 64 304
    [3]
    Jeni?ta J, Bartlová M and Aubrecht V 2006 Czech. J. Phys. 56 B1224
    [4]
    Mok Y S, Jo J O and Woo C 2007 J. Adv. Oxid. Technol. 10 439
    [5]
    US Environmental Protection Agency 2015 Review of thermal destruction technologies for chemical and biological agents bound on materials Washington (Washingto, DC: EPA) ch 2 p 41
    [6]
    Abdel-Shafy H I and Aly R O 2002 CEJOEM 8 3
    [7]
    Baker D J et al 2008 Prehosp. Disaster Med. 24 180
    [8]
    Sato M et al 2005 J. Adv. Oxidation Technol. 8 198
    [9]
    Machala Z, Hensel K and Akishev Y 2012 Plasma for Bio- Decontamination, Medicine and Food Security (Dordrecht: Springer)
    [10]
    Xiong Q et al 2008 IEEE Trans. Plasma Sci. 36 986
    [11]
    Laroussi M 2002 IEEE Trans. Plasma Sci. 30 1409
    [12]
    Anyaegbunam F N C 2014 IOSR J. Appl. Phys. 6 36
    [13]
    Solonenko O P 2001 Thermal Plasma Torches and Technologies vol II (Cambridge: Cambridge International Science Publishing) p 234
    [14]
    Hlína M et al 2006 22nd Symp. on Plasma Phys. Tech. vol 56, p 1179
    [15]
    Zhao Z L 2003 Abstr. Pap. Am. Chem. Soc. 226 U536
    [16]
    Hrabovsky M et al 2006 IEEE Trans. Plasma Sci. 34 1566
    [17]
    Jenista J et al 2010 High Temp. Mater. Processes 14 63
    [18]
    Aubrecht V and Bartlova M 2004 Czech. J. Phys. 54 C759
    [19]
    Moussa D et al 2007 IEEE Trans. Plasma Sci. 35 444
    [20]
    Hrabovsky M et al 2009 High Temp. Mater. Processes 13 299
    [21]
    Laroussi M and Leipold F 2004 Int. J. Mass Spectrom. 233 81
    [22]
    Moisan M et al 2002 Pure Appl. Chem. 74 349
    [23]
    Lee K et al 2006 J. Microbiol. 44 269
    [24]
    Galaly A R and Zahran H H 2013 J. Phys. Conf. Ser. 431 012014
    [25]
    Laroussi M, Mendis D A and Rosenberg M 2003 New J. Phys. 5 41
    [26]
    Galaly A R and Zahran H H 2014 J. Mod. Phys. 5 781
    [27]
    Laroussi M 2005 Plasma Processes Polym. 2 391
    [28]
    Lu X P et al 2008 J. Appl. Phys. 104 053309
    [29]
    Graves D B 2014 Phys. Plasmas 21 080901
    [30]
    Sato M 2009 Int. J. Plasma Environ. Sci. Technol. 3 8
    [31]
    Du C M et al 2016 Sci. Rep. 6 18838
    [32]
    Locke B R et al 2006 Ind. Eng. Chem. Res. 45 882
    [33]
    Hlina M et al 2010 High Temp. Mater. Processes 14 89
    [34]
    Nomura S et al 2006 Appl. Phys. Lett. 88 211503
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    3. Joshi-Thompson, J., Ramisch, M. A neural network for the analysis of Langmuir-probe characteristics. Plasma Physics and Controlled Fusion, 2024, 66(10): 105015. DOI:10.1088/1361-6587/ad7289
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