Copper alloys

Three different corrosion products of copper alloys, namely malachite, cassiterite and cuprite were chosen for investigating their influence on the XRF quantification results of the reference copper alloy BCR-E.

Malachite Cu2(CO3)(OH)2

Copper alloy BCR-E with malachite surface layer of thickness from 0.005μm to 100μm.

The formation of a malachite layer influences at minimum the Cu-Kα intensity (only 13% increase over 100 μm thickness), whereas the Sn-Kα intensity is more af- fected showing a decrease of about 50%. However, it is interesting to note that that the Sn-Lα intensity is drastically affected presented almost an order of magnitude decrease

0 10 20 30 40 50 60 70 80 90 100

6.00x10⁶ 6.15x10⁶ 6.30x10⁶ 6.45x10⁶ 6.60x10⁶ 6.75x10⁶

Malachite layer thickness (µm) BCRE_with a Malachite layer on top_10000s

Counts

Cu-Kα

Figure 62. The variation of the Cu-Kα counts with re- spect to the thickness of the malachite layer formed on

the surface of copper reference alloy BCR-E

0 10 20 30 40 50 60 70 80 90 100

1.20x10⁵ 1.44x10⁵ 1.68x10⁵ 1.92x10⁵ 2.16x10⁵ 2.40x10⁵

Malachite layer thickness (µm) BCRE_with a Malachite layer on top_10000s

Counts

Sn-Kα

Figure 63. The variation of the Sn-Kα counts with re- spect to the thickness of the malachite layer formed on

the surface of copper reference alloy BCR-E

0 10 20 30 40 50

10⁰ 10¹ 10² 10³ 10⁴

Malachite layer thickness (µm) BCRE_with a Malachite layer on top_10000s

Counts

Sn-Lα

Figure 64. The variation of the Sn-Lα counts with re- spect to the thickness of the malachite layer formed on

the surface of copper reference alloy BCR-E

0 10 20 30 40

10² 10³ 10⁴

Malachite layer thickness (µm) BCRE_with a Malachite layer on top_Sn Kα/Lα Ratio

Sn Kα/Lα

Sn Kα/Lα

Figure 65. The variation of the Sn Kα/Lα intensity ratio with respect to the thickness of the malachite layer formed on the surface of copper reference alloy BCR-E

over malachite layer thickness.

64

per 10 μm thickness of the malachite layer and subsequently this behavior is also re- flected in the variation of the Sn Kα/Sn Lα intensity ratio.

Cassiterite SnO2

In cases where bronzes contain high percentage of tin it can be observed a partic- ularly form of a buried metal corrosion product, where the copper content may be pro- gressively leached out resulting to the formation of cassiterite, i.e. stannic oxide SnO2. The volume of alloy remains the same and the cassiterite film that is created acts as a protective layer for the alloy.

The simulations were performed for the copper alloy BCR-E with a cassiterite surface layer with thickness from 0.005μm to 100μm.

65

0 10 20 30 40 10⁴

10⁵ 10⁶

Cassiterite layer thickness (µm) BCRE_with a Cassiterite layer on top_10000s

Counts

Cu-Kα

Figure 66. The variation of the Cu-Kα counts with re- spect to the thickness of the cassiterite layer formed on

the surface of copper reference alloy BCR-E

0 10 20 30 40 50 60 70 80 90 100

0.0 4.0x10⁵ 8.0x10⁵ 1.2x10⁶ 1.6x10⁶ 2.0x10⁶ 2.4x10⁶

Cassiterite layer thickness (µm) BCRE_with a Cassiterite layer on top_10000s

Counts

Sn-Kα

Figure 67. The variation of the Sn-Kα counts with respect to the thickness of the cassiterite layer formed on the sur-

face of copper reference alloy BCR-E

0 10 20 30 40 50 60 70 80 90 100

0 1x10⁴ 2x10⁴ 3x10⁴ 4x10⁴ 5x10⁴ 6x10⁴

Cassiterite layer thickness (µm) BCRE_with a Cassiterite layer on top_10000s

Counts

Sn-Lα

Figure 68. The variation of the Sn-Lα counts with respect to the thickness of the cassiterite layer formed on the sur-

face of copper reference alloy BCR-E

0 10 20 30 40 50 60 70 80 90 100

20 40

Cassiterite layer thickness (µm) BCRE_with a Cassiterite layer on top_10000s

Counts

Sn-Kα/Lα

Figure 69. The variation of the Sn-Kα/Sn-Lα intensity ra- tio with respect to the thickness of the cassiterite layer formed on the surface of copper reference alloy BCR-E

In the case of a copper alloy with cassiterite as a surface formed corrosion product, it can be observed that while the cassiterite layer thickness is increased, the Cu-Kα intensity is attenuated exponentially, while the Sn-Kα intensity is smoothly increased.

The effect of 1.4 μm cassiterite thickness, is that the Cu-Kα counts are decreased by 4.5% producing an apparent Cu concentration of 88.3% instead of 92.4%, whereas the Sn-Kα counts are increased by 32%. What is very characteristic is that the Sn-Lα inten- sity is abruptly increased and then saturates above a thickness of 10 μm thickness. Thus, the intensity Sn-Kα/Sn-Lα ratio seems to be able to provide a discriminating parameter to identify and estimate the thickness of the cassiterite corrosion layer, in correlation with the net Sn-Kα and Sn-Lα intensities.

66

Cuprite Cu2O

Copper alloy BCRE with Cuprite surface layer from 0.005μm to 100μm.

0 10 20 30 40 50 60 70 80 90 100

6.0x10⁶ 6.2x10⁶ 6.4x10⁶ 6.6x10⁶ 6.8x10⁶ 7.0x10⁶ 7.2x10⁶ 7.4x10⁶

Cuprite layer thickness (µm) BCRE_with a Cuprite layer on top_10000s

Counts

Cu-Kα

Figure 70. The variation of the Cu-Kα counts with respect to the thickness of cuprite layer formed on the surface of the reference

copper alloy BCR-E

0 10 20 30 40 50 60 70 80 90 100

0.0 3.0x10⁴ 6.0x10⁴ 9.0x10⁴ 1.2x10⁵ 1.5x10⁵ 1.8x10⁵ 2.1x10⁵ 2.4x10⁵

Cuprite layer thickness (µm) BCRE_with a Cuprite layer on top_10000s

Counts

Sn-Kα

Figure 71. The variation of the Sn-Kα counts with respect to the thickness of cuprite layer formed on the surface of the reference

copper alloy BCR-E

0 10 20

10¹ 10² 10³

Cuprite layer thickness (µm) BCRE_with a Cuprite layer on top_10000s

Counts

Sn-Lα

Figure 72. The variation of the Sn-Lα counts with respect to the thickness of cuprite layer formed on the surface of the reference

copper alloy BCR-E

0 10 20

100 1000 10000

Cuprite layer thickness (µm) BCRE_with a Cuprite layer on top_10000s

Counts Sn-Kα/Sn-Lα

Figure 73. The variation of the Sn-Kα/Sn-Lα intensity with re- spect to the thickness of cuprite layer formed on the surface of

the reference copper alloy BCR-E

In the case of cuprite as a surface corrosion layer product the overall increase of Cu-Kα intensity over a 100 μm thickness is only by 28%, whereas the Sn-Kα intensity is decreased almost by an order of magnitude (87.5%). Again, as in the case of the malachite corrosion layer, the Sn-Lα intensity is very sensitive to the presence of the surface cuprite corrosion layer showing a decrease of an order of magnitude for every

67

5 μm. Thus, the intensity Sn-Kα/Sn-Lα ratio seems to be able to provide a discriminat- ing parameter to identify and estimate the thickness of the cuprite corrosion layer, in correlation with the net Sn-Kα and Sn-Lα intensities.

6 Conclusions

In the present diploma thesis, an X-ray fluorescent methodology was developed to determine the distribution of the tube intensity. The spectrometer’s operating param- eters were validated: high voltage setting, tube current linearity, thickness of the filters and thickness of the detector. Using the X-ray tube excitation spectrum, that was deter- mined, a comparison between the pXRF measurements and the simulated ones was made. The simulations were performed with the XMI-MSIM simulation program and evaluated shown differences that were less than ~15% for major elements, while for the minor elements or elements that are close to the detection limits the results showed greater deviations.

Concerning the corroded alloys, in the case of a gold alloy, if the surface of the alloy is enriched with gold and the analysis proceeds without taking this enrichment into account, then the wrong results will be obtained, and for example one would have assumed that it would have been a golden-rich alloy. So in order to be able to tell how much the thickness of the enrichment is, in the case of an enriched alloy, by identifying the percentage of decrease or increase of the concentrations of components, the simu- lations and the analysis that followed in chapter 5 should be performed. The same pro- cedure can be followed in cases of corrosion layers on alloys.

7 Future directions

The validation of the tube emission spectrum and the set-up parameters to simu- late accurately measured XRF spectra, through the XMI-MSIM tool, can lead to a more comprehensive input for the PyMca Toolkit for quantitative analysis purposes.

The present study can be extended in the analysis of other materials (Kokiasmenou, et al., 2018) and can be combined with PYMCA software for quantifi- cation procedure.

68

This project was presented in the EXRS conference in 2018 (Papadopoulou, et al., 2018) and another application using this methodology as a principle for Io was pre- sented in a second project of the laboratory at the same conference (Kokiasmenou, et al., 2018).

8 Images

Figure 1. X-ray interactions with matter. When an X-ray beam passes through the matter interacts by the Photoelectric Effect, Compton scattering and Rayleigh scattering. _________________________ 5

Figure 2. Energy dependence of the mass absorption coefficient μ of several elements (Janssens & Van Grieken, 2004) ______________________________________________________________________ 7 Figure 3. Compton scattering (Brouwer, 2010). ___________________________________________ 8 Figure 4. Geometry for Compton scattering of X-ray photons (Janssens & Van Grieken, 2004). _____ 8

Figure 5. Fluorescence /Auger electron yield versus atomic number (Karydas, 2014). For high Z elements a fluorescence phenomenon is more probable than Auger, while it is the opposite for low Z elements. __________________________________________________________________________ 10 Figure 6. Basic design of EDXRF spectrometer (Brouwer, 2010) _____________________________ 11 Figure 7. A side window of X-ray tube (Brouwer, 2010) ____________________________________ 12

Figure 8. The electrical pulses in counts per second, "CPS", are separated into the different channels via the multichannel analyzer (Σιανούδης, et al., n.d.) ______________________________________ 15 Figure 9. (1) Rh tube anode source (max 50 W, max 50kV, 1mA, spot size ~2.8 mm, model XTF5011, Oxford XTG), (2) Silicon PIN detector (FWHM 165eV @ Mn-Kα), (3) Filter wheel including different filter materials, (3, 4) Collimator parts, (5) Laser pointers for sample positioning. _______________ 18 Figure 10. XMI - MSIM on startup – first page. ___________________________________________ 24 Figure 11. Composition section - adding a new layer. ______________________________________ 25 Figure 12. Geometry coordinate system. ________________________________________________ 26 Figure 13. Coordinate system for this project. ____________________________________________ 27 Figure 14. Results panel. Displaying results from simulation of a gold alloy. ___________________ 29 Figure 15. X-ray tube (Ebel model). ____________________________________________________ 30 Figure 16. Input parameters for batching mode. __________________________________________ 31 Figure 17. Batching results. __________________________________________________________ 31

69

Figure 18. The initial beam of photons of intensity Io incidents on a surface of total thickness T, at an angle φ1. Assuming that it ionizes an atom i and produces a characteristic radiation of intensity Ii that escapes from the surface at an angle φ2. _________________________________________________ 32 Figure 19. PyMca fitting graph on ABKMF gold alloy. _____________________________________ 35

Figure 20. The accumulated spectra of a La (a) and Nd (b) compounds at different high voltages settings are presented ______________________________________________________________________ 37

Figure 21. Intensity of La-Kα and Nd-Kα at different tube high voltages. ______________________ 37 Figure 22. The normalized counts per second (cps) of Cu-Kα X ray line versus tube current exhibit very good linearity. _____________________________________________________________________ 39

Figure 23. The ratio of filtered /unfiltered scattered tube radiation versus energy at the range of 14 - 20keV. Using a least square procedure, the thickness of the filters used were estimated. __________ 40

Figure 24. The experimental ratio of the intensities (𝜤𝜤𝑲𝑲𝜷𝜷𝑰𝑰𝑲𝑲𝜶𝜶) (stars in the graph) and their theoretical calculations (lines in the graph) for different crystal thicknesses. _____________________________ 42 Figure 25. Input parameters at geometry settings. _________________________________________ 43 Figure 26. Used absorbers. ___________________________________________________________ 43 Figure 27. Input parameters at detector settings. __________________________________________ 44 Figure 28. Input parameters in X-ray tube and the tube distribution. __________________________ 44 Figure 29. PTFE unfiltered without correction at the distribution. ___________________________ 45 Figure 30. PTFE experimental over simulated ratio before any correction. ____________________ 45 Figure 31. Polynomial fit to the experimental over simulated ratio of the intensity of the continuum distribution (5-13 keV). ______________________________________________________________ 46

Figure 32. Linear fit to the experimental over simulated ratio of the intensity of the continuum distribution (13-32 keV). The spectra data that correspond to the discrete scattered peaks of the tube anode material were removed from the fitting procedure. ___________________________________ 46 Figure 33. PTFE experimental over simulated at 40kV, unfiltered ____________________________ 46 Figure 34. PTFE experimental over simulated ratio at 40kV, unfiltered. _______________________ 46 Figure 35. PTFE experimental over simulated at 40kV, filtered excitation _____________________ 47 Figure 36. PTFE experimental over simulated ratio at 40kV, filtered excitation _________________ 47 Figure 37. ABKMF experimental over simulated at 40kV. __________________________________ 48 Figure 38. ABSBL experimental over simulated at 40kV. ___________________________________ 48 Figure 39. ABLLI experimental over simulated at 40kV. ____________________________________ 48

70

Figure 40. ABQAQ experimental over simulated at 40kV. ___________________________________ 48

Figure 41. The % difference between experimental and simulated net peak area intensities of the detected characteristic X-ray lines of gold alloys. ________________________________________________ 49 Figure 42. CNR91 experimental over simulated at 40kV. ___________________________________ 50 Figure 43. CNR92 experimental over simulated at 40kV. ___________________________________ 50 Figure 44. CNR141 experimental over simulated at 40kV. __________________________________ 50 Figure 45. CNR152 experimental over simulated at 40kV. __________________________________ 50

Figure 46. The % difference between experimental and simulated net peak area intensities of the detected characteristic X-ray lines of silver alloys. _______________________________________________ 51 Figure 47. BCRA experimental over simulated at 40kV. ____________________________________ 52 Figure 48. BCRB experimental over simulated at 40kV. ____________________________________ 52 Figure 49. BCRD experimental over simulated at 40kV. ____________________________________ 52 Figure 50. BCRE experimental over simulated at 40kV. ____________________________________ 52

Figure 51. The % difference between experimental and simulated net peak area intensities of the detected characteristic X-ray lines of copper alloys. ______________________________________________ 53

Figure 52. A comparison between the simulated versus experimental peak counts obtained from the analysis of the different reference gold, silver and copper alloys. The observed deviations at low peak statistics refer to the elements As and Zn which are detected close to the respective detection limits _ 54

Figure 53. The variation of the Au-M counts with respect to the thickness of a surface enriched pure gold layer _____________________________________________________________________________ 61

Figure 54. The variation of the Au-Lα counts with respect to the thickness of a surface enriched pure gold layer _________________________________________________________________________ 61

Figure 55. The variation of the Au-Lα/Αu-M intensity ratio with respect to the thickness of a surface enriched pure gold layer. _____________________________________________________________ 61

Figure 56. The variation of the Ag-Kα counts with respect to the thickness of a surface enriched pure gold layer _________________________________________________________________________ 61

Figure 57. The variation of the Cu-Kα counts with respect to the thickness of a surface enriched pure gold layer _________________________________________________________________________ 61

Figure 58. The variation of the Ag-Kα counts with respect to the thickness of the silver pure layer formed on the surface of the silver reference alloy CNR-141. ______________________________________ 63

Figure 59. The variation of the Ag-Lα counts with respect to the thickness of the silver pure layer formed on the surface of the silver reference alloy CNR-141. ______________________________________ 63

71

Figure 60. The variation of the Ag-Kα/Ag-Lα intensity ratio with respect to the thickness of the silver pure layer formed on the surface of the silver reference alloy CNR-141. _______________________ 63 Figure 61. The variation of the Cu-Kα counts with respect to the thickness of the silver pure layer formed on the surface of the silver reference alloy CNR-141. ______________________________________ 63

Figure 62. The variation of the Cu-Kα counts with respect to the thickness of the malachite layer formed on the surface of copper reference alloy BCR-E __________________________________________ 64

Figure 63. The variation of the Sn-Kα counts with respect to the thickness of the malachite layer formed on the surface of copper reference alloy BCR-E __________________________________________ 64

Figure 64. The variation of the Sn-Lα counts with respect to the thickness of the malachite layer formed on the surface of copper reference alloy BCR-E __________________________________________ 64 Figure 65. The variation of the Sn Kα/Lα intensity ratio with respect to the thickness of the malachite layer formed on the surface of copper reference alloy BCR-E over malachite layer thickness. ______ 64

Figure 70. The variation of the Cu-Kα counts with respect to the thickness of the cassiterite layer formed on the surface of copper reference alloy BCR-E __________________________________________ 66

Figure 71. The variation of the Sn-Kα counts with respect to the thickness of the cassiterite layer formed on the surface of copper reference alloy BCR-E __________________________________________ 66

Figure 72. The variation of the Sn-Lα counts with respect to the thickness of the cassiterite layer formed on the surface of copper reference alloy BCR-E __________________________________________ 66

Figure 73. The variation of the Sn-Kα/Sn-Lα intensity ratio with respect to the thickness of the cassiterite layer formed on the surface of copper reference alloy BCR-E _______________________________ 66

Figure 74. The variation of the Cu-Kα counts with respect to the thickness of cuprite layer formed on the surface of the reference copper alloy BCR-E _____________________________________________ 67

Figure 75. The variation of the Sn-Kα counts with respect to the thickness of cuprite layer formed on the surface of the reference copper alloy BCR-E _____________________________________________ 67

Figure 76. The variation of the Sn-Lα counts with respect to the thickness of cuprite layer formed on the surface of the reference copper alloy BCR-E _____________________________________________ 67

Figure 77. The variation of the Sn-Kα/Sn-Lα intensity with respect to the thickness of cuprite layer formed on the surface of the reference copper alloy BCR-E _______________________________________ 67

9 Tables

Table I. Certified concentrations of copper alloys. ... 19 Table II. Certified concentrations of the elements in silver alloys. ... 20

72

Table III. Certified concentrations of gold alloys. ... 20 Table IV. Counts and normalized cps of Cu-Kα at 100-500μA. ... 38

10 Bibliography

Adler, T. A. et al., 2003. ASM HANDBOOK / Corrosion: Fundamentals, Testing and Protection. 1st ed. United States: ASM International.

Anon., n.d. ESRF. [Online] Available at: http://pymca.sourceforge.net/references.html [Accessed 28 8 2018].

Bauters, S., 2013. Development and applications of Monte Carlo based XRF quantification algorithms for the elemental analysis of cosmic particles, Gent:

Universiteit Gent.

Beck, L. et al., 2004. Silver surface enrichment of silver–copper alloys:a limitation for the analysis of ancient silver coins by surface techniques. Nuclear Instruments and Methods in Physics Research B(226), pp. 153-162.

Bottaini, C. et al., 2015. Energy dispersive X-ray fluorescence spectroscopy/Monte Carlo simulation approach for the non-destructive analysis of corrosion patina-bearing alloys in archaeological bronzes: The case of the bowl from the Fareleira 3 site (Vidigueira, South Portugal). ELSEVIER, Volume Spectrochimica Acta Part B, pp. 9-13.

Bounakhla, M. & Tahri, M., n.d. yumpu. [Online] Available at:

https://www.yumpu.com/en/document/view/12372751/x-ray-fluorescence- analytical-techniques-cnstn-centre-

Brouwer, P., 2010. Theory of XRF - Getting acquaintend with the principles. 3η Έκδοση ed. The Netherlands: PANalytical B.V..

Brunetti, A., Golosio, B., Schoonjans, T. & Olva, P., 2015. Use of Monte Carlo simulations for cultural heritage X-ray fluorescence analysis. ELSEVIER, Volume Spectrochimica Acta Part B, pp. 15-20.

Ebel, H., 1999. X-ray tube spectra. X-ray spectrometry, Volume 28, pp. 255-266.

73

Ebel, H., 1999. X-ray tube spectra. X Ray Spectrometry, 27 9, p. 255.

Eckhardt, R., 1987. Stan Ulam, John Von Neumann and the Monte Carlo Method. Los Alamos Science, Issue 15, pp. 131 - 136.

Galli, A. & Bonizzoni, L., 2014. True versus forged in the cultural heritage materials:

the role of PXRF analysis. X-RAY SPECTROMETRY, Issue 43, pp. 22 - 28.

Heinrich, K. J. & Newbury, D. E., 1991. Electron Probe Quantitaion. New York: P1enum Press.

Helmut Fischer, G. I., 2012. Primary reference standards for precious metal analysis (gold alloys), Hürth, Nordrhein-Westfalen: Ficsher.

Ingelbrecht, C., Adriaens, A. & Maier, E., 2001. The certification of arsenic, lead, tin and zinc (mass fractions) in five copper alloys., Luxembourg: EUROPEAN COMMISSION.

Ingo, G. M. et al., 2004. Combined use of XPS and SEM + EDS for the study of surface microchemical structure of archaeological bronze Roman mirrors. Surface and interface analysis, Volume 36.

Ingo, G. M. et al., 2006. Bronze roman mirrors: the secret of brightness. Applied Physics A / Materials Science & Processing, Volume 83.

Janssens, K., 2013. Modern Methods for Analysing Archaeological and Historical Glass.

1st Editio ed. Chichester(West Sussex,): John Wiley & Sons.

Janssens, K., 2013. Modern Methods for Analysing Archaeological and Historical Glass.

United Kingdom: Wiley.

Janssens, K. & Van Grieken, R., 2004. Non - Destructive Microanalysis Of Cultural Heritage Materials. s.l.:ELSEVIER.

Karydas, A. G., 2014. Introduction to Quantitative XRF analysis, Trieste: ICTP-IAEA School.

Kokiasmenou, E. et al., 2018. pXRF analysis and MA-XRF imaging of Mycenean Wall Painting pigments from Nestor's palace at Pylos. s.l., EXRS.

74

La Niece, S. & Craddock, P., 1993. Metal plating and patination, Cultural, Technical and Historical Developments. 1st ed. s.l.:Buterworth Heinemann.

Lowenthal, G. C. & Airey, P. L., 2004. Practical Applications of Radioacactivity and Nuclear Radiations. New York: Cambridge University Press.

Manso, M., 2015. Alloy characterization of a 7th Century BC archeological bronze vase

— Overcoming patina constraints using Monte Carlo simulations. ELSEVLVIER, Volume Spectrochimica Acta Part B, pp. 93-96.

Mantler & Schreiner, 2001. X-ray analysis of objects of art and archeology. 247(Special Issue), pp. 635-644.

Metropolis, N., 1987. The Beginning of the Monte Carlo Method. Los Alamos Science, Issue 15, pp. 125 - 130.

Miller, A. I., 2002. Αϊνστάιν Πικάσο: Ο χώρος ο χρόνος και η ομορφιά. 1η Έκδοση ed.

Αθήνα: Π. ΤΡΑΥΛΟΣ.

Miller, A. I., 2002. Αϊντάιν Πικάσο: Ο χώρος ο χρόνος και η ομορφιά. [μεταφρ.] Σπύρος Πιέρρης. 1η Έκδοση ed. Αθήνα: Π. ΤΡΑΥΛΟΣ.

Papadopoulou, D. et al., 2018. Optimizing the use of the XMI-MSIM Monte Carlo tool for the simulation of tube excited XRF spectra. s.l., EXRS.

Polese, C., 2015. Instruments and Methods for XRF analysis of Materials of Cultural Interest, Roma: s.n.

Scheid, F., 1976. Αριθμητική Ανάλυση. Αθήνα: ΕΣΠΙ, SCHAUM 2.

Schiavon, N. et al., 2016. An Energy-Dispersive X-Ray Fluorescence Spectrometry andMonte Carlo simulation study of Iron-Age Nuragic small bronzes (“Navicelle”) from Sardinia, Italy. ELSEVIER, Volume Spectrochimica Acta Part B, pp. 42-46.

Schoonjans, T., 2014. An introduction to Monte Carlo methods in XRF analysis. s.l.:s.n.

Schoonjans, T., 2017. XMI - MSIM The definitive manual, s.l.: s.n.

Schoonjans, T., n.d. http://lvserver.ugent.be/xraylib-web/, s.l.: Ghent Universty.

Schoonjans, T. et al., 2013. Spectrochimica Acta Part B. ELSEVIER, pp. 36-41.

75

Selwyn, L., 2004. Metals and Corrosion, A Handbook for the Conservation Professional.

1st ed. Canada: Canadian Conservation Institute.

Serway, R. A., 1990. Physics for Scientists & Engineers with Modern Physics,. 3η Έκδοση ed. Philadelphia: Saunders Golden Sunburst Series, Saunders College Publisshing.

Serway, R. A., 1990. Physics for scientists and engineers. 3rd ed. Athens: Κορφιάτης.

Shackley, S., 2011. X-Ray Fluorescence Spectrometry (FRX) in Geoarchaeology. New York Dordrecht Heildelberg London: Springer.

Solé, A. et al., 2007. A multiplatform code for the analysis of energy-dispersive X-ray fluorescence spectra. Volume Part B, 62, pp. 63 - 68.

Specht, A. J., 2016. X-ray fluorescence for quantification of lead and strontium in vivo, West Lafayette, Indiana: Purdue University.

Thornton, S. T. & Rex, A., 2013. Modern physics for scientists and engineers. 4th ed.

United States: Gengage Learning.

Torreti, R., 2012. Η Φιλοσοφία της Φυσικής. 1η Έκδοση ed. Ηράκλειο(Κρήτης):

Πανεπιστημιακές Εκδόσεις Κρήτης.

Van Grieken, R. E. & Markowicz, A. A., 2001. Handbook of X-Ray Spectrometry. Second ed. New York(Basel): Marcel Dekker, Inc..

Van Grieken, R. E. & Markowicz, A. A., 2002. Handbook of X-Ray Spectrometry. Second Edition, Revised and Expanded ed. NewYork: Marcel Dekker.

Van Grieken, R. E. & Markowicz, A. A., 2002. Handbook of X-Ray Spectrometry. Second Edition, Revised and Expanded ed. NewYork: Marcel Dekker.

Van Grieken, R. & Janssens, K., 2005. Cultural Heritage Conservation and Environmental Impact Assessment by Non - Distructive Testing and Micro - Analysis..

1st ed. London: A.A. BALKEMA PUBLISHERS.

Van Grieken, R. & Janssens, K., 2005. Environmental Impact Assessment by Non- Destructive Testing and Micro-Analysis. London: A.A. Balkema Publishers.

Varella, E. A., 2013. Conservation Science for the Cultural Heritage. Thessaloniki:

Springer.

76

No documento EVALUATION OF MONTE CARLO SIMULATION TECHNIQUE IN XRF QUANTITATIVE ANALYSIS OF (páginas 77-90)