Polyethylene Identification in Ocean Water Samples by Means of 50 keV Energy Electron Beam
by
John I. Adlish
1,2,
Davide Costa
2,
Enrico Mainardi
2,
Piero Neuhold
2,*,
Riccardo Surrente
2 and
Luca J. Tagliapietra
2,*1
Biology Department, Truckee Meadow Community College, Reno, Nevada, NV 89512-3999, USA
2
Particle Physics Department, BabuHawaii Foundation, Honolulu, HI 96811, USA
*
Authors to whom correspondence should be addressed.
Instruments 2020, 4(4), 32; https://doi.org/10.3390/instruments4040032
Submission received: 16 September 2020
/
Revised: 21 October 2020
/
Accepted: 21 October 2020
/
Published: 31 October 2020
Download keyboard_arrow_down
Graphical abstract
"> Figure 1
<p>Physical model x-z section of ocean water and polyethylene.</p> "> Figure 2
<p>Geometrical model of x-z section.</p> "> Figure 3
<p>Volumetric cluster cells in 3D.</p> "> Figure 4
<p>Ocean water polyethylene plus microorganisms, x-z section model.</p> "> Figure 5
<p>Carbon total photon cross section as a function of energy.</p> "> Figure 6
<p>Carbon incoherent photon cross section as a function of energy.</p> "> Figure 7
<p>Carbon coherent photon cross section as a function of energy.</p> "> Figure 8
<p>Carbon photoelectric photon cross section as a function of energy.</p> "> Figure 9
<p>Carbon pair production photon cross section as a function of energy.</p> "> Figure 10
<p>Oxygen total photon cross section as a function of energy.</p> "> Figure 11
<p>Oxygen incoherent photon cross section as a function of energy.</p> "> Figure 12
<p>Oxygen coherent photon cross section as a function of energy.</p> "> Figure 13
<p>Oxygen photoelectric photon cross section as a function of energy.</p> "> Figure 14
<p>Oxygen pair production photon cross section as a function of energy.</p> "> Figure 15
<p>Phosphorus total photon cross section as a function of energy.</p> "> Figure 16
<p>Phosphorus incoherent photon cross section as a function of energy.</p> "> Figure 17
<p>Phosphorus coherent photon cross section as a function of energy.</p> "> Figure 18
<p>Phosphorus photoelectric photon cross section as a function of energy.</p> "> Figure 19
<p>Phosphorus pair production photon cross section as a function of energy.</p> "> Figure 20
<p>Ocean water total electron stopping power as a function of energy.</p> "> Figure 21
<p>Ocean water total photon cross section as a function of energy.</p> "> Figure 22
<p>Ocean water incoherent photon cross section as a function of energy.</p> "> Figure 23
<p>Ocean water coherent photon cross section as a function of energy.</p> "> Figure 24
<p>Ocean water photoelectric photon cross section as a function of energy.</p> "> Figure 25
<p>Photon flux—ocean water vs contamination.</p> "> Figure 26
<p>Photon fluxes—spectrum vs contamination.</p> "> Figure 27
<p>30 keV—ocean water vs contamination.</p> "> Figure 28
<p>40 keV—ocean water vs contamination.</p> "> Figure 29
<p>50 keV—ocean water vs contamination.</p> "> Figure 30
<p>Photon flux—polyethylene vs microorganisms.</p> "> Figure 31
<p>30 KeV—polyethylene vs microorganisms.</p> "> Figure 32
<p>40 KeV—polyethylene vs microorganisms.</p> "> Figure 33
<p>50 KeV—polyethylene vs microorganisms.</p> ">
Review Reports
Versions Notes
"> Figure 1
<p>Physical model x-z section of ocean water and polyethylene.</p> "> Figure 2
<p>Geometrical model of x-z section.</p> "> Figure 3
<p>Volumetric cluster cells in 3D.</p> "> Figure 4
<p>Ocean water polyethylene plus microorganisms, x-z section model.</p> "> Figure 5
<p>Carbon total photon cross section as a function of energy.</p> "> Figure 6
<p>Carbon incoherent photon cross section as a function of energy.</p> "> Figure 7
<p>Carbon coherent photon cross section as a function of energy.</p> "> Figure 8
<p>Carbon photoelectric photon cross section as a function of energy.</p> "> Figure 9
<p>Carbon pair production photon cross section as a function of energy.</p> "> Figure 10
<p>Oxygen total photon cross section as a function of energy.</p> "> Figure 11
<p>Oxygen incoherent photon cross section as a function of energy.</p> "> Figure 12
<p>Oxygen coherent photon cross section as a function of energy.</p> "> Figure 13
<p>Oxygen photoelectric photon cross section as a function of energy.</p> "> Figure 14
<p>Oxygen pair production photon cross section as a function of energy.</p> "> Figure 15
<p>Phosphorus total photon cross section as a function of energy.</p> "> Figure 16
<p>Phosphorus incoherent photon cross section as a function of energy.</p> "> Figure 17
<p>Phosphorus coherent photon cross section as a function of energy.</p> "> Figure 18
<p>Phosphorus photoelectric photon cross section as a function of energy.</p> "> Figure 19
<p>Phosphorus pair production photon cross section as a function of energy.</p> "> Figure 20
<p>Ocean water total electron stopping power as a function of energy.</p> "> Figure 21
<p>Ocean water total photon cross section as a function of energy.</p> "> Figure 22
<p>Ocean water incoherent photon cross section as a function of energy.</p> "> Figure 23
<p>Ocean water coherent photon cross section as a function of energy.</p> "> Figure 24
<p>Ocean water photoelectric photon cross section as a function of energy.</p> "> Figure 25
<p>Photon flux—ocean water vs contamination.</p> "> Figure 26
<p>Photon fluxes—spectrum vs contamination.</p> "> Figure 27
<p>30 keV—ocean water vs contamination.</p> "> Figure 28
<p>40 keV—ocean water vs contamination.</p> "> Figure 29
<p>50 keV—ocean water vs contamination.</p> "> Figure 30
<p>Photon flux—polyethylene vs microorganisms.</p> "> Figure 31
<p>30 KeV—polyethylene vs microorganisms.</p> "> Figure 32
<p>40 KeV—polyethylene vs microorganisms.</p> "> Figure 33
<p>50 KeV—polyethylene vs microorganisms.</p> ">
Abstract
:This study presents a new methodology to reveal traces of polyethylene (the most common microplastic particles, known as a structure of C2H4) in a sample of ocean water by the irradiation of a 50 keV, 1 µA electron beam. This is performed by analyzing the photon (produced by the electrons in water) fluxes and spectra (i.e., fluxes as a function of photon energy) with different types of contaminated water using an adequate device and in particular looking at the peculiar interactions of electrons/photons with the potential abnormal atomic hydrogen (H), oxygen (O), carbon (C), and phosphorus (P) compositions present in the water, as a function of living and nonliving organic organisms with PO4 group RNA/DNA strands in a cluster configuration through a volumetric cells grid.
1. Introduction
Plastic is the most common type of marine debris found in oceans, and it is the most widespread problem affecting the marine environment. It also threatens ocean health, food safety and quality, human health, and coastal tourism, and it contributes to climate change [1,2,3,4,5]. Plastic debris can come in many different shapes and sizes, but those that are less than five millimeters across (or the size of a sesame seed) are called “microplastics”. One of the most common microplastics in use today is polyethylene, with most of the known kinds having the chemical formula (C2H4)n. It is a linear, man-made homopolymer primarily used for packaging (plastic bags, plastic films, geomembranes, containers including bottles, etc.). As of 2019, over 100 million tons of polyethylene resins are being produced annually, accounting for 34% of the total plastics market.
This is an emerging field of study, and not much is known yet about microplastics and their impact on the environment. The NOAA Marine Debris Program is pursuing efforts within the NOAA to research this important topic.
Different standardized field methods have been developed for the collection of microplastic samples in sediment [6,7,8,9,10,11,12,13], sand, and surface water, all of which continue to be tested. In the end, the field and laboratory protocols will allow for a global comparison of the quantity of microplastics released into the environment, which is the first step in determining the final distribution, impacts, and fate of these debris.
Microplastics come from a variety of sources, including larger plastic debris that degrade into smaller and smaller pieces. In addition, microspheres, a type of microplastic, are tiny particle pieces of plastic polyethylene that are added as exfoliators to health and beauty products, such as some detergents and toothpastes, passing easily through water filtration systems, thus posing a threat to aquatic life.
The most visible impacts of marine plastics are the ingestion, suffocation, and entanglement of hundreds of marine species. Marine wildlife such as seabirds, whales, fish, and turtles, mistake plastic waste for prey, and most die of starvation as their stomachs are filled with plastic debris. They also suffer from lacerations, infections, reduced ability to swim, and internal injuries. Floating plastics also contribute to the spread of invasive marine organisms and bacteria, which disrupt ecosystems. Plastic degrades (i.e., breaks down into pieces), but it does not biodegrade (break down through natural decomposition). This has become a problem over time, as all the plastic pieces that have been generated over the last seven decades have steadily increased their presence as a contaminant, creating a biological alteration. According to the United Nations Environment Program, these plastic microspheres first appeared in personal care products about fifty years ago, with plastic replacing more and more natural ingredients. Until 2012, this problem was still relatively unknown, with an abundance of products containing plastic microspheres on the market and leading now to an increase in microplastic detection and identification demand.
Ocean water also contains microorganisms, live matter and not, such as viruses, bacteria, and microorganisms like plankton with a different PO4 phosphorus content [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]. Viruses, for example, are intracellular parasites composed of a nucleic acid surrounded by a protein coat, the capsid. Some viruses contain a lipid envelope, derived from the host, surrounding the capsid. The nucleic acid found in viruses can consist of either RNA or DNA. RNA is composed of nucleotides, each containing a sugar (deoxyribose), a nitrogen-containing base (adenine, uracil, guanine, and cytosine), and a phosphate group PO4. Members of the Coronoviridae family measure 80–160 nm in diameter. Generally, there are 1–10 million viruses and about 100,000 to 1 million bacteria cells for each milliliter of ocean water.
The proposed methodology is based on a subatomic particle analysis and the subsequent detection of particles, and the aim is to use this to identify polyethylene particles in the water among microorganisms. It could be an interesting research approach for the ocean studies field and for the food and beverage industries field in order to detect microplastic contamination in their products. This type of approach would make it easier to test water samples and analyze data in real time in comparison to other state-of-the-art detection processes, and it also allows test procedures to be conducted for quality assurance in the food and beverage industries with simple hardware.
2. Materials and Methods
The physical model under analysis and its simulation by the MCNPX Monte Carlo simulation subatomic particles code [30,31,32] are based on an electron beam source of 50 keV and 1 µA, easily accessible from an extraction line of an industrial linear/circular particle accelerator, interacting with the water sample target. The beam energy and current are based on cross section considerations and radiation requirements; the beam interacts with a cylindrical sample volume—with the axis on x—of ocean water of radius r = 5 cm and height h = 10 cm as s sample tank (Figure 1), which is analyzed at x = 10 cm through a double plates ionization chamber detector.
Among the all possible subatomic particles generated, only photons (coming from electron coherent and incoherent scattering, absorption, knock on, decay, fluorescence, bremsstrahlung, and photoelectric effect) were taken into account, as reported in Table 2 (where the percent contribution of different phenomena which create photons are shown) and Table 3 (where the percent contribution of different elements to the production of photons are shown), as the other ones are actually negligible. As for Table 2, the photoelectric effect consists of the absorption of the incident photon energy E, with emission of several fluorescent photons and the ejection or excitation of an orbital electron of binding energy e < E. Photons of first fluorescence are emitted with energy greater than 1 keV; those of second fluorescence are still greater than 1 keV and are caused by residual excitation of the first fluorescence process, leading to a second emission.
It has to be underlined that the MCNPX analysis took into account both electrons and photons without neglecting any secondary photon production by performing a photon/electron coupled calculation and by keeping track of the electron mean free path in the water sample, which is around 5060 nm due to multiple volume cells of the electron mean path magnitude. All the results proposed concern the photon fluxes and spectra of interest, where all the possible primary and secondary electron productions (mode e, p) into the sample volume were taken into account.
The polyethylene particles are described in 11 cluster configurations (Table 4) through a highly sophisticated volumetric cell grid (Figure 2 and Figure 3); each cluster is composed of microspheres with a radius of 0.1 mm and a volume of 4 × 1019 − 3 mm3 per particle, with a mutual distance of 1–9 cm among the clusters along all the axes (Figure 3) and evaluated on an atomic fraction of C, H in the ocean water sample tank at different concentrations from 10 ppm up to 10,000 ppm (Table 5, Table 6, Table 7 and Table 8).
It must be underlined that a benchmark model was also taken into consideration in order to evaluate a potential enrichment in microorganisms, bacteria, and viruses, which can alter mainly the carbonium and in particular the phosphorus PO4 group analysis outcome; these all were analyzed on multiple “tallies” (control check volumes/surfaces) in order to evaluate energy distributions and particle mean free path (yellow squares, Figure 4). In order to do that, in the benchmark, a 100-ppm polyethylene content in the ocean water sample in the cluster configuration was kept constant, and different enriched mixture scenarios at 0.7 ppm, 7 ppm, 70 ppm, and 700 ppm of potential living/nonliving matter and microorganisms were studied, adjusting their own contributions in the final solution in terms of atomic C, H, O, P content and the result in terms of particle spectra and fluxes.
MCNPX was performed chronologically in different cluster stages: Stage 1, with 0 ppm contamination to investigate the physics involved in the basic case; Stage 2, evaluating an escalating contamination grade as maximum stress test of 10 ppm, 100 ppm, 1000 ppm, and 10,000 ppm (Table 9 and Table 10), just as a benchmark to determine the subatomic particles’ stopping power and the shielding effects that give the photon fluxes and energy spectra, due to all the experimental cross sections involved in these cases (Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19, Figure 20, Figure 21, Figure 22, Figure 23 and Figure 24). The MCNPX code by various variance reduction techniques fulfils 10 statistical tests [30] with an average relative error of 2%.
3. Results
This section presents the results of the analysis showing the photon fluxes and energy spectra of the Monte Carlo simulations in the presence of polyethylene contaminations and also without it in the detector chamber, located at x = 10 cm on the top of the sample tank on the x-axis.
The study analyzed the photon fluxes and their contributions on three discrete energy bins: 30, 40, and 50 keV at different polyethylene grades with an energy spectrum peak located at 40 keV. The reason of a 40 keV peak can be explained by the cross section considerations and energy spectrum degradation. As shown in Figure 21, the total photon cross section value (in barns) decreases as a function of the energy from 8 barns at 40 keV to 3 barns at 50 keV. Moreover, the detection surface is located at x = 10 cm after the primary injection beam at x = 0 cm, leading to the detection of a particle flux and spectrum in a different energy configuration due to scattering, fluorescence, absorption, and photoelectric effect, which are responsible for leaving an intact high energy photon band after x = 5 cm and thus made negligible the energy contribution for the low band spectrum E < 20 keV. Between the interval 5 < x < 10 cm, the photon flux, present in a high energy band configuration, interacts with the nonhomogeneous media due to scattering, fluorescence, absorption, and photoelectric effect, thus causing a degradation of the 50 keV energy bin and leading to an average value of 40 keV.
As shown in Figure 25 and Figure 26, the total photon flux and each flux that was evaluated on 30, 40, and 50 keV, increase between 0–10 ppm of 1.4% due to electron bremsstrahlung and photelectric-fluorescence on polyethylene particles. However, it has to be underlined that in the beginning of the contamination process, the main atomic element present in the water is oxygen with a weight percentage of 85.70%, and its photon cross sections (Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14) show a higher value (in barn unities) compared to the carbon ones (Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9). These cross-section considerations are the main reason to understand the decrease of 5.6% between 10–100 ppm where the amount of oxygen reduces while the amount of carbon increases but with a less effective cross-section value. However, after 100 ppm, due to the electron stopping power and the bremsstrahlung/photoelectric process on the mixture, the photon flux trend starts to increase 10% up to 1000 ppm and of 50.7% from 1000–10,000 ppm.
Figure 25, Figure 26, Figure 27, Figure 28 and Figure 29 show the fluxes and photon energy spectra and the different behaviors as a function of polyethylene contamination on 3 discrete energy bins.
As mentioned in Section 2, the graphs in Figure 30, Figure 31, Figure 32 and Figure 33 show the photon fluxes and energy spectra as well as the different behaviors of fixed contamination test case of 100 ppm polyethylene, in cluster configuration, and mixed as a function of microorganism group PO4, evaluated on 3 discrete energy bins, i.e., 30, 40, and 50 keV.
4. Discussion
The photon fluxes and spectra can discriminate the amount of polyethylene contamination by using to its own “particle signature” in terms of photon flux at the detector point combined with the spectrum analysis, as reported for 30, 40, and 50 keV.
As shown in Figure 27, Figure 28 and Figure 29, the photon flux associated with the sample of ocean water at different concentrations of the polyethylene shows both a trend in term of photon/s*cm2 and differences from an energy spectrum point of view to evaluate its own contributions in counting the number of photons on each energy line:
- The 10-ppm polyethylene case can be discriminated using the photon flux counts at the detector evaluated on the 30 and 40 keV spectra compared to the standard ocean water.
- The 100-ppm polyethylene case can be discriminated using the photon flux counts at the detector and the 30, 40, and 50 keV spectra compared to the 10 ppm one.
- The 1000-ppm polyethylene case can be discriminated using the photon flux counts at the detector and the 30, 40, and 50 keV spectra compared to the 100 ppm one.
- The 10,000-ppm polyethylene case can be discriminated using the photon flux counts at the detector and the 30, 40, and 50 keV spectra compared to the 1000 ppm one.
As shown in Figure 30, the photon flux, starting from the ocean water plus 100 ppm polyethylene contamination, increases as a function of the ppm amount of microorganisms added in the water sample tank. This behavior is due to an increase from 0.7 ppm to 700 ppm of P (present in the PO4 group in the sample) and also due to a change subsequently in the cross-section value, thus affecting the photon population (Figure 15, Figure 16, Figure 17 and Figure 18). In the presence of microorganism living/nonliving matter, the photon flux shows, taking a parametric comparison case of 100 ppm polyethylene, an increase of 2.3% from 0 to 0.7 ppm of microorganisms, 0.2% from 0.7 to 7 ppm of microorganisms, 0.7% from 7 to 70 ppm of microorganisms, and a decrease of 1% from 70 to 700 ppm of microorganisms. Furthermore, it has to be underlined that even if there is a significant change in the total photon population counts, what has been one of the research main goals was to discriminate the number of microorganisms present in the sample tank through a spectrum analysis and relative photon flux counts on the 3 energy bins.
As shown, the photon flux associated with the 100-ppm polyethylene at different concentrations of microorganisms increases in terms of photon/s*cm2, and differences appear in the contribution to the total by different energy photons (Figure 31, Figure 32 and Figure 33):
- 5.
- The 0.7-ppm microorganisms case can be discriminated using the photon flux counts at the detector evaluated on the 30 and 50 keV spectrum lines compared to the ocean water + 100 ppm polyethylene combination at the same energy conditions.
- 6.
- The 7-ppm microorganisms case can be discriminated using the photon flux counts at the detector evaluated on the 50 keV spectrum line compared to the ocean water + 100 ppm polyethylene + 0.7 ppm microorganisms combination at the same energy condition.
- 7.
- The 70-ppm microorganisms case can be discriminated using the photon flux counts at the detector evaluated on the 40 and 50 keV spectrum lines compared to the ocean water + 100 ppm polyethylene + 7 ppm microorganisms combination at the same energy conditions.
- 8.
- The 700-ppm microorganisms case can be discriminated using the photon flux counts at the detector evaluated on the 40 and 50 keV spectrum lines compared to the ocean water + 100 ppm polyethylene + 70 ppm microorganisms combination at the same energy conditions.
5. Conclusions
This study proposed a new approach to identify low contaminations of polyethylene mixed in water using a Monte Carlo simulation performed by the MCNPX subatomic particles code and evaluating the secondary photon (generated by an electron beam of 50 keV and 1 µA) energy spectra and fluxes revealed by an adequate detector.
Different types of contamination grades can be discriminated using their trend Vs photon/s*cm2 evaluated on at least three energy bins, which in this case are 30, 40, and 50 keV. Every single contamination is unique in its own spectrum photon signature, and the flux acts as a unique identifier in the detection process so that, in combination with the microorganisms analysis, it can give the ppm amount of polyethylene in ocean water, drinking/non-drinking water, and food/beverage processing.
Author Contributions
Conceptualization, L.J.T. and P.N.; methodology, L.J.T., P.N. and J.I.A.; software, L.J.T., P.N.; validation, E.M. and J.I.A.; investigation, E.M., D.C.; data curation, R.S.; writing—original draft preparation, P.N., D.C.; writing—review and editing, L.J.T., P.N., J.I.A.; visualization, R.S.; All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Acknowledgments
We deeply thank: Giulio Magrin, Alessandro Alemberti, Ilaria A. Valli.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Parker, L. Microplastics. National Geographic Society. Available online: https://www.nationalgeographic.com/environment/2019/06/microplastics-spread-throughout-deep-sea-monterey-canyon/ (accessed on 20 October 2020).
- Rogers, K. Microplastics “Plastic Particulate”. Britannica. Available online: https://www.britannica.com/technology/microplastic (accessed on 20 October 2020).
- Kane, I.A.; Clare, M.A.; Miramontes, E.; Wogelius, R.; Rothwell, J.J.; Garreau, P.; Pohl, F. Seafloor microplastic hotspots controlled by deep-sea circulation. Science 2020, 368, 1140–1145. [Google Scholar] [CrossRef] [PubMed]
- Smith, M.; Love, D.C.; Rochman, C.M.; Neff, R.A. Microplastics in Seafood and the Implications for Human Health. Curr. Environ. Health Rep. 2018, 5, 375–386. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- National Research Council (US) Safe Drinking Water Committee. Drinking Water and Health; National Academies Press: Washington, DC, USA, 1977. [Google Scholar]
- Wesch, C.; Barthel, A.-K.; Braun, U.; Klein, R.; Paulus, M. No microplastics in benthic eelpout (Zoarces viviparus): An urgent need for spectroscopic analyses in microplastic detection. Environ. Res. 2016, 148, 36–38. [Google Scholar] [CrossRef] [PubMed]
- Prata, J.C.; Da Costa, J.P.; Duarte, A.C.; Rocha-Santos, T. Methods for sampling and detection of microplastics in water and sediment: A critical review. TrAC Trends Anal. Chem. 2019, 110, 150–159. [Google Scholar] [CrossRef]
- Maes, T.; Jessop, R.; Wellner, N.; Haupt, K.; Mayes, A.G. A rapid-screening approach to detect and quantify microplastics based on fluorescent tagging with Nile Red. Sci. Rep. 2017, 7, srep44501. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Araujo, C.F.; Nolasco, M.M.; Ribeiro, A.M.; Ribeiro-Claro, P.J. Identification of microplastics using Raman spectroscopy: Latest developments and future prospects. Water Res. 2018, 142, 426–440. [Google Scholar] [CrossRef] [PubMed]
- Marine & Environmental Research Institute. Guide to Microplastic Identification. 2012. Available online: https://docplayer.net/27438419-Guide-to-microplastic-identification.html (accessed on 20 October 2020).
- Segebade, C.; Starovoitova, V.N.; Borgwardt, T.; Wells, D. Principles, Methodologies, and Applications of Photon Activation Analysis: A Review; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
- Joseph, A.; Cotruvo, W.H.O. Water, Sanitation and Health Protection and the Human Environment World; WHO: Geneva, Switzerland, 2006. [Google Scholar]
- Fries, E.; Dekiff, J.H.; Willmeyer, J.; Nuelle, M.T.; Ebert, M.; Remy, D. Identification of polymer types and additives in marine microplastic particles using pyrolysis-GC/MS and scanning electron microscopy. Environ. Sci. Process. Impacts 2013, 15, 1949–1956. [Google Scholar] [CrossRef] [Green Version]
- Bar-On, Y.M.; Phillips, R.; Milo, R. The biomass distribution on Earth. Proc. Natl. Acad. Sci. USA 2018, 115, 6506–6511. [Google Scholar] [CrossRef] [Green Version]
- Mann, N.H. The Third Age of Phage. PLoS Biol. 2005, 3, 753–755. [Google Scholar] [CrossRef] [Green Version]
- Wommack, K.E.; Colwell, R.R. Virioplankton: Viruses in Aquatic Ecosystems. Microbiol. Mol. Biol. Rev. 2000, 64, 69–114. [Google Scholar] [CrossRef] [Green Version]
- Suttle, C.A. Viruses in the sea. Nat. Cell Biol. 2005, 437, 356–361. [Google Scholar] [CrossRef]
- Bergh, O.; Børsheim, K.Y.; Bratbak, G.; Heldal, M. High abundance of viruses found in aquatic environments. Nat. Cell Biol. 1989, 340, 467–468. [Google Scholar] [CrossRef]
- Wigington, C.H.; Sonderegger, D.; Brussaard, C.P.D.; Buchan, A.; Finke, J.F.; Fuhrman, J.A.; Lennon, J.T.; Middelboe, M.; Suttle, C.A.; Stock, C.; et al. Re-examination of the relationship between marine virus and microbial cell abundances. Nat. Microbiol. 2016, 1, 15024. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Brum, J.R.; O Schenck, R.; Sullivan, M.B. Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses. ISME J. 2013, 7, 1738–1751. [Google Scholar] [CrossRef] [Green Version]
- Krupovič, M.; Bamford, D.H. Putative prophages related to lytic tailless marine dsDNA phage PM2 are widespread in the genomes of aquatic bacteria. BMC Genom. 2007, 8, 236. [Google Scholar] [CrossRef] [Green Version]
- Xue, H.; Xu, Y.; Boucher, Y.F.; Polz, M.F. High Frequency of a Novel Filamentous Phage, VCYϕ, within an Environmental Vibrio cholerae Population. Appl. Environ. Microbiol. 2011, 78, 28–33. [Google Scholar] [CrossRef] [Green Version]
- Roux, S.; Krupovic, M.; Poulet, A.; Debroas, D.; Enault, F. Evolution and Diversity of the Microviridae Viral Family through a Collection of 81 New Complete Genomes Assembled from Virome Reads. PLoS ONE 2012, 7, e40418. [Google Scholar] [CrossRef]
- Lawrence, C.M.; Menon, S.; Eilers, B.J.; Bothner, B.; Khayat, R.; Douglas, T.; Young, M.J. Structural and Functional Studies of Archaeal Viruses. J. Biol. Chem. 2009, 284, 12599–12603. [Google Scholar] [CrossRef] [Green Version]
- Prangishvili, D.; Forterre, P.; Garrett, R.A. Viruses of the Archaea: A unifying view. Nat. Rev. Microbiol. 2006, 4, 837–848. [Google Scholar] [CrossRef]
- Mainardi, E.; Donahue, R.J.; Wilson, W.E.; Blakely, E.A. Comparison of microdosimetric simulations using PENELOPE and PITS for a 25 keV electron microbeam in water. Radiat. Res. 2004, 162, 326–331. [Google Scholar] [CrossRef] [Green Version]
- Vilhena, M.D.P.S.P.; Da Costa, M.L.; Berrêdo, J.F.; Paiva, R.S.; Almeida, P.D. Chemical composition of phytoplankton from the estuaries of Eastern Amazonia. Acta Amaz. 2014, 44, 513–526. [Google Scholar] [CrossRef] [Green Version]
- Romera-Castillo, C.; Pinto, M.; Langer, T.M.; Álvarez-Salgado, X.A.; Herndl, G.J. Dissolved organic carbon leaching from plastics stimulates microbial activity in the ocean. Nat. Commun. 2018, 9, 1–7. [Google Scholar] [CrossRef]
- Gin, K.Y.-H.; Lin, X.; Zhang, S. Dynamics and size structure of phytoplankton in the coastal waters of Singapore. J. Plankton Res. 2000, 22, 1465–1484. [Google Scholar] [CrossRef]
- Pelowitz, D.B. MCNPX User’s Manual, Version 2.5.0; Report LA-CP-05-0369; Los Alamos National laboratory: Los Alamos, NM, USA, 2005. [Google Scholar]
- White, M.C. Photo Atomic Data Library, MCPLIB04; Los Alamos National Laboratory: Los Alamos, NM, USA, 2003. [Google Scholar]
- Oak Ridge National Laboratory. MCNP-MCNPX Code Collection; Los Alamos national Laboratory: Los Alamos, NM, USA, 2006. [Google Scholar]
Figure 1.
Physical model x-z section of ocean water and polyethylene.
Figure 2.
Geometrical model of x-z section.
Figure 3.
Volumetric cluster cells in 3D.
Figure 4.
Ocean water polyethylene plus microorganisms, x-z section model.
Figure 5.
Carbon total photon cross section as a function of energy.
Figure 6.
Carbon incoherent photon cross section as a function of energy.
Figure 7.
Carbon coherent photon cross section as a function of energy.
Figure 8.
Carbon photoelectric photon cross section as a function of energy.
Figure 9.
Carbon pair production photon cross section as a function of energy.
Figure 10.
Oxygen total photon cross section as a function of energy.
Figure 11.
Oxygen incoherent photon cross section as a function of energy.
Figure 12.
Oxygen coherent photon cross section as a function of energy.
Figure 13.
Oxygen photoelectric photon cross section as a function of energy.
Figure 14.
Oxygen pair production photon cross section as a function of energy.
Figure 15.
Phosphorus total photon cross section as a function of energy.
Figure 16.
Phosphorus incoherent photon cross section as a function of energy.
Figure 17.
Phosphorus coherent photon cross section as a function of energy.
Figure 18.
Phosphorus photoelectric photon cross section as a function of energy.
Figure 19.
Phosphorus pair production photon cross section as a function of energy.
Figure 20.
Ocean water total electron stopping power as a function of energy.
Figure 21.
Ocean water total photon cross section as a function of energy.
Figure 22.
Ocean water incoherent photon cross section as a function of energy.
Figure 23.
Ocean water coherent photon cross section as a function of energy.
Figure 24.
Ocean water photoelectric photon cross section as a function of energy.
Figure 25.
Photon flux—ocean water vs contamination.
Figure 26.
Photon fluxes—spectrum vs contamination.
Figure 27.
30 keV—ocean water vs contamination.
Figure 28.
40 keV—ocean water vs contamination.
Figure 29.
50 keV—ocean water vs contamination.
Figure 30.
Photon flux—polyethylene vs microorganisms.
Figure 31.
30 KeV—polyethylene vs microorganisms.
Figure 32.
40 KeV—polyethylene vs microorganisms.
Figure 33.
50 KeV—polyethylene vs microorganisms.
Table 1.
Ocean water weight chemical composition.
| Element. | Element (%) | Element | Element (%) |
|---|---|---|---|
| Oxygen | 85.7 | Molybdenum | 0.000001 |
| Hydrogen | 10.8 | Zinc | 0.000001 |
| Chlorine | 1.9 | Nickel | 0.00000054 |
| Sodium | 1.05 | Arsenic | 0.0000003 |
| Magnesium | 0.135 | Copper | 0.0000003 |
| Sulfur | 0.0885 | Tin | 0.0000003 |
| Calcium | 0.04 | Uranium | 0.0000003 |
| Potassium | 0.038 | Chromium | 0.00000003 |
| Bromine | 0.0065 | Krypton | 0.00000025 |
| Carbon | 0.0028 | Manganese | 0.0000002 |
| Strontium | 0.00081 | Vanadium | 0.0000001 |
| Boron | 0.00046 | Titanium | 0.0000001 |
| Silicon | 0.0003 | Cesium | 0.00000005 |
| Fluoride | 0.00013 | Cerium | 0.00000004 |
| Argon | 0.00006 | Antimony | 0.000000033 |
| Nitrogen | 0.00005 | Silver | 0.00000003 |
| Lithium | 0.000018 | Yttrium | 0.00000003 |
| Rubidium | 0.000012 | Cobalt | 0.000000027 |
| Phosphorus | 0.000007 | Neon | 0.000000014 |
| Iodine | 0.000006 | Cadmium | 0.000000011 |
| Barium | 0.000003 | Tungsten | 0.00000001 |
| Aluminum | 0.000001 | Lead | 0.000000005 |
| Iron | 0.000001 | Mercury | 0.000000003 |
| Indium | 0.000001 | Selenium | 0.000000002 |
Table 2.
Photon Creation.
| Ocean Water No Contamination | Polyethylene 10 ppm | Polyethylene 100 ppm | Polyethylene 1000 ppm | Polyethylene 10,000 ppm | |
|---|---|---|---|---|---|
| Bremsstrahlung | 99.1265% | 99.1237% | 99.1182% | 99.1545% | 99.3538% |
| 1st Fluorescence | 0.8733% | 0.8755% | 0.8812% | 0.8449% | 0.6448% |
| 2nd Fluorescence | 0.0002% | 0.0008% | 0.0006% | 0.0006% | 0.0015% |
| Norm | 100.0000% | 100.0000% | 100.0000% | 100.0000% | 100.0000% |
Table 3.
Nuclide Photon Activity.
| Element | Ocean Water No Contamination | Polyethylene 10 ppm | Polyethylene 100 ppm | Polyethylene 1000 ppm | Polyethylene 10,000 ppm |
|---|---|---|---|---|---|
| Oxygen | 76.210% | 76.273% | 76.387% | 73.211% | 52.813% |
| Hydrogen | 7.585% | 7.405% | 6.998% | 6.686% | 4.259% |
| Chlorine | 12.357% | 12.107% | 12.179% | 11.938% | 8.902% |
| Sodium | 1.924% | 1.912% | 1.873% | 1.912% | 1.384% |
| Magnesium | 0.306% | 0.325% | 0.316% | 0.370% | 0.244% |
| Sulfur | 0.490% | 0.573% | 0.536% | 0.448% | 0.372% |
| Calcium | 0.429% | 0.512% | 0.434% | 0.409% | 0.277% |
| Potassium | 0.316% | 0.360% | 0.337% | 0.384% | 0.330% |
| Bromine | 0.322% | 0.294% | 0.281% | 0.340% | 0.198% |
| Carbon | 0.000% | 0.193% | 0.628% | 4.257% | 31.188% |
| Strontium | 0.056% | 0.046% | 0.031% | 0.044% | 0.029% |
| Silicon | 0.005% | 0.000% | 0.000% | 0.000% | 0.000% |
| Argon | 0.000% | 0.000% | 0.000% | 0.000% | 0.004% |
Table 4.
Parts per million contamination in cluster configuration.
| Cluster N | (10 ppm) | (100 ppm) | (1000 ppm) | (10,000 ppm) |
|---|---|---|---|---|
| ppm perCluster | ppm perCluster | ppm perCluster | ppm perCluster | |
| 1 | 1 | 10 | 100 | 1000 |
| 2 | 0.5 | 5 | 50 | 500 |
| 3 | 2 | 20 | 200 | 2000 |
| 4 | 1.3 | 13 | 130 | 1300 |
| 5 | 1.9 | 19 | 190 | 1900 |
| 6 | 0.3 | 3 | 30 | 300 |
| 7 | 0.8 | 8 | 80 | 800 |
| 8 | 0.4 | 4 | 40 | 400 |
| 9 | 0.2 | 2 | 20 | 200 |
| 10 | 0.9 | 9 | 90 | 900 |
| 11 | 0.7 | 7 | 70 | 700 |
| Norm | 10 | 100 | 1000 | 10,000 |
Table 5.
Particles and volume in 10 ppm.
| Cluster N | (10 ppm) | (10 ppm) | Particles N | Volume (mm3) |
|---|---|---|---|---|
| ppm per Cluster | % ppm Cluster | per Cluster | per Cluster | |
| 1 | 1 | 10% | 262 | 1 |
| 2 | 0.5 | 5% | 131 | 1 |
| 3 | 2 | 20% | 525 | 2 |
| 4 | 1.3 | 13% | 341 | 1 |
| 5 | 1.9 | 19% | 498 | 2 |
| 6 | 0.3 | 3% | 79 | 0.3 |
| 7 | 0.8 | 8% | 210 | 1 |
| 8 | 0.4 | 4% | 105 | 0.4 |
| 9 | 0.2 | 2% | 52 | 0.2 |
| 10 | 0.9 | 9% | 236 | 1 |
| 11 | 0.7 | 7% | 184 | 1 |
| Norm | 10 | 100.00% | 2623 | 11 |
Table 6.
Particles and volume in 100 ppm.
| Cluster N | (100 ppm) | (100 ppm) | Particles N | Volume (mm3) |
|---|---|---|---|---|
| ppm per Cluster | % ppm Cluster | per Cluster | per Cluster | |
| 1 | 10 | 10% | 2623 | 11 |
| 2 | 5 | 5% | 1311 | 5 |
| 3 | 20 | 20% | 5245 | 22 |
| 4 | 13 | 13% | 3409 | 14 |
| 5 | 19 | 19% | 4983 | 21 |
| 6 | 3 | 3% | 787 | 3 |
| 7 | 8 | 8% | 2098 | 9 |
| 8 | 4 | 4% | 1049 | 4 |
| 9 | 2 | 2% | 525 | 2 |
| 10 | 9 | 9% | 2360 | 10 |
| 11 | 7 | 7% | 1836 | 8 |
| Norm | 100 | 100.00% | 26,227 | 110 |
Table 7.
Particles and volume in 1000 ppm.
| Cluster N | (1000 ppm) | (1000 ppm) | Particles N | Volume (mm3) |
|---|---|---|---|---|
| ppm per Cluster | % ppm Cluster | per Cluster | per Cluster | |
| 1 | 100 | 10% | 26,227 | 110 |
| 2 | 50 | 5% | 13,113 | 55 |
| 3 | 200 | 20% | 52,454 | 220 |
| 4 | 130 | 13% | 34,095 | 143 |
| 5 | 190 | 19% | 49,831 | 209 |
| 6 | 30 | 3% | 7868 | 33 |
| 7 | 80 | 8% | 20,981 | 88 |
| 8 | 40 | 4% | 10,491 | 44 |
| 9 | 20 | 2% | 5245 | 22 |
| 10 | 90 | 9% | 23,604 | 99 |
| 11 | 70 | 7% | 18,359 | 77 |
| Norm | 1000 | 100.00% | 262,268 | 1099 |
Table 8.
Particles and volume in 10,000 ppm.
| Cluster N | (10,000 ppm) | (10,000 ppm) | Particles N | Volume (mm3) |
|---|---|---|---|---|
| ppm per Cluster | % ppm Cluster | per Cluster | per Cluster | |
| 1 | 1000 | 10% | 262,268 | 1099 |
| 2 | 500 | 5% | 131,134 | 549 |
| 3 | 2000 | 20% | 524,535 | 2198 |
| 4 | 1300 | 13% | 340,948 | 1429 |
| 5 | 1900 | 19% | 498,308 | 2088 |
| 6 | 300 | 3% | 78,680 | 330 |
| 7 | 800 | 8% | 209,814 | 879 |
| 8 | 400 | 4% | 104,907 | 440 |
| 9 | 200 | 2% | 52,454 | 220 |
| 10 | 900 | 9% | 236,041 | 989 |
| 11 | 700 | 7% | 183,587 | 769 |
| Norm | 10,000 | 100.00% | 2,622,676 | 10,989 |
Table 9.
Polyethylene ppm.
| C | H | |
|---|---|---|
| ppm | (mg/L) | (mg/L) |
| 10 | 8.57142857 | 1.42857143 |
| 100 | 85.7142857 | 14.2857143 |
| 1000 | 857.142857 | 142.857143 |
| 10,000 | 8571.42857 | 1428.57143 |
Table 10.
Ocean Water Vs Polyethylene ppm composition.
| Element | Origin Element (%) | Element (ppm) | 10 ppm Polyethylene (ppm) | 100 ppm Polyethylene (ppm) | 1000 ppm Polyethylene (ppm) | 10,000 ppm Polyethylene (ppm) |
|---|---|---|---|---|---|---|
| Oxygen | 85.70 | 8.57 × 105 | 8.570 × 105 | 8.569 × 105 | 8.561 × 105 | 8.484 × 105 |
| Hydrogen | 10.80 | 1.08 × 105 | 1.080 × 105 | 1.080 × 105 | 1.081 × 105 | 1.094 × 105 |
| Chlorine | 1.90 | 19,000 | 1.900 × 104 | 1.900 × 104 | 1.898 × 104 | 1.881 × 104 |
| Sodium | 1.05 | 10,500 | 1.050 × 104 | 1.050 × 104 | 1.049 × 104 | 1.040 × 104 |
| Magnesium | 0.14 | 1350 | 1.350 × 103 | 1.350 × 103 | 1.349 × 103 | 1.337 × 103 |
| Sulfur | 0.09 | 885 | 8.850 × 102 | 8.849 × 102 | 8.841 × 102 | 8.762 × 102 |
| Calcium | 0.04 | 400 | 4.000 × 102 | 4.000 × 102 | 3.996 × 102 | 3.960 × 102 |
| Potassium | 0.04 | 380 | 3.800 × 102 | 3.800 × 102 | 3.796 × 102 | 3.762 × 102 |
| Bromine | 0.01 | 65 | 6.500 × 101 | 6.499 × 101 | 6.494 × 101 | 6.435 × 101 |
| Carbon | 0.00 | 28 | 3.657 × 101 | 1.137 × 102 | 8.851 × 102 | 8.599 × 103 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Adlish, J.I.; Costa, D.; Mainardi, E.; Neuhold, P.; Surrente, R.; Tagliapietra, L.J. Polyethylene Identification in Ocean Water Samples by Means of 50 keV Energy Electron Beam. Instruments 2020, 4, 32. https://doi.org/10.3390/instruments4040032
AMA Style
Adlish JI, Costa D, Mainardi E, Neuhold P, Surrente R, Tagliapietra LJ. Polyethylene Identification in Ocean Water Samples by Means of 50 keV Energy Electron Beam. Instruments. 2020; 4(4):32. https://doi.org/10.3390/instruments4040032
Chicago/Turabian StyleAdlish, John I., Davide Costa, Enrico Mainardi, Piero Neuhold, Riccardo Surrente, and Luca J. Tagliapietra. 2020. "Polyethylene Identification in Ocean Water Samples by Means of 50 keV Energy Electron Beam" Instruments 4, no. 4: 32. https://doi.org/10.3390/instruments4040032
APA StyleAdlish, J. I., Costa, D., Mainardi, E., Neuhold, P., Surrente, R., & Tagliapietra, L. J. (2020). Polyethylene Identification in Ocean Water Samples by Means of 50 keV Energy Electron Beam. Instruments, 4(4), 32. https://doi.org/10.3390/instruments4040032