EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 520 (2024) E3S Web Conf., 520 (2024) 02021 References
Open Access
Issue
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
Article Number 02021
Number of page(s) 9
Section Carbon Emission Control and Waste Resource Utilization
DOI https://doi.org/10.1051/e3sconf/202452002021
Published online 03 May 2024
  1. F. Gil-Sotres, C. Trasar-Cepeda, M.C. Leirós, and S. Seoane, “Different approaches to evaluating soil quality using biochemical properties,” Soil biology and biochemistry, vol. 37, no. 5, pp. 877–887, 2005. [CrossRef] [Google Scholar]
  2. A.-C. Da Silva, A. Triantafyllou, and N. Delmelle, “Portable x-ray fluorescence calibrations: Workflow and guidelines for optimizing the analysis of geological samples,” Chemical Geology, vol. 623, p. 121395, 2023. [CrossRef] [Google Scholar]
  3. R. M. Rousseau, “Corrections for matrix effects in X- ray fluorescence analysis—A tutorial,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 61, no. 7, pp. 759–777, 2006. [CrossRef] [Google Scholar]
  4. G. Heskestad, “An edge suction effect,” AIAA journal, vol. 3, no. 10, pp. 1958–1961, 1965. [CrossRef] [Google Scholar]
  5. F. Claisse, “Overcoming the particle size effect in the internal standard method of X-ray fluorescence analysis,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 25, no. 5, pp. 209–218, 1970. [CrossRef] [Google Scholar]
  6. P. M. S. Carvalho et al., “Energy dispersive X-ray fluorescence quantitative analysis of biological samples with the external standard method,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 174, p. 105991, 2020. [CrossRef] [Google Scholar]
  7. K. K. Nielson, “Progress in X-ray fluorescence correction methods using scattered radiation,” Advances in X-ray Analysis, vol. 22, pp. 303–315, 1978. [CrossRef] [Google Scholar]
  8. B. Ababneh, A. A. Tajuddin, R. Hashim, and I. L. Shuaib, “Investigation of mass attenuation coefficient of almond gum bonded Rhizophora spp. particleboard as equivalent human tissue using XRF technique in the 16.6-25.3 keV photon energy,” Australasian physical & engineering sciences in medicine, vol. 39, pp. 871–876, 2016. [CrossRef] [PubMed] [Google Scholar]
  9. G. Andermann, “Surface-film and interfacial analysis via variable grazing exit angle x-ray fluorescence spectrometry,” Applied surface science, vol. 31, no. 1, pp. 1–41, 1988. [CrossRef] [Google Scholar]
  10. J. Sherman, “The theoretical derivation of fluorescent X-ray intensities from mixtures,” Spectrochimica acta, vol. 7, pp. 283–306, 1955. [CrossRef] [Google Scholar]
  11. J. Omote, H. Kohno, and K. Toda, “X-ray fluorescence analysis utilizing the fundamental parameter method for the determination of the elemental composition in plant samples,” Analytica chimica acta, vol. 307, no. 1, pp. 117–126, 1995. [CrossRef] [Google Scholar]
  12. R. Sitko, “Study on the influence of X-ray tube spectral distribution on the analysis of bulk samples and thin films: fundamental parameters method and theoretical coefficient algorithms,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 63, no. 11, pp. 1297–1302, 2008. [CrossRef] [Google Scholar]
  13. P. Mishra, A. Biancolillo, J. M. Roger, F. Marini, and D. N. Rutledge, “New data preprocessing trends based on ensemble of multiple preprocessing techniques,” TrAC Trends in Analytical Chemistry, vol. 132, p. 116045, 2020. [CrossRef] [Google Scholar]
  14. A. Horta et al., “Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review,” Geoderma, vol. 241, pp. 180–209, 2015. [CrossRef] [Google Scholar]
  15. R. Studer, V. R. Benjamins, and D. Fensel, “Knowledge engineering: Principles and methods,” Data & knowledge engineering, vol. 25, no. 1-2, pp. 161–197, 1998. [CrossRef] [Google Scholar]
  16. C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of big data, vol. 6, no. 1, pp. 1–48, 2019. [CrossRef] [Google Scholar]
  17. P. Hamet and J. Tremblay, “Artificial intelligence in medicine,” Metabolism, vol. 69, pp. S36–S40, 2017. [CrossRef] [Google Scholar]
  18. J. A. Gásquez, E. Perino, E. Marchevsky, R. Olsina, and A. Riveros, “Correction of Line Interference in X-Ray Fluorescence Trace Analysis. Application to Yttrium Determination in Silicate Rocks,” X-Ray Spectrometry: An International Journal, vol. 26, no. 5, pp. 272–274, 1997. [Google Scholar]
  19. F. Li, L. Ge, Z. Tang, Y. Chen, and J. Wang, “Recent developments on XRF spectra evaluation,” Applied Spectroscopy Reviews, vol. 55, no. 4, pp. 263–287, 2020. [CrossRef] [Google Scholar]
  20. N. Saitou, A. Iida, and Y. Golism, “A Comparison of Two Smoothing Methods as Pre-Processing for Iterative Deconvolution,” Journal of the Spectroscopical Society of Japan, vol. 33, no. 1, pp. 15–20, 1984. [CrossRef] [Google Scholar]
  21. L. Benedet et al., “Soil texture prediction using portable X-ray fluorescence spectrometry and visible near-infrared diffuse reflectance spectroscopy,” Geoderma, vol. 376, p. 114553, 2020. [CrossRef] [Google Scholar]
  22. F. Nakhaei, A. Sam, M. R. Mosavi, and A. Nakhaei, “Prediction of XRF analyzers error for elements online assaying using Kalman Filter,” International Journal of Mining Science and Technology, vol. 22, no. 4, pp. 595–601, 2012. [CrossRef] [Google Scholar]
  23. S. A. Barcellos Lins, G. E. Gigante, R. Cesareo, S. Ridolfi, and A. Brunetti, “Testing the accuracy of the calculation of gold leaf thickness by MC simulations and MA-XRF scanning,” Applied Sciences, vol. 10, no. 10, p. 3582, 2020. [CrossRef] [Google Scholar]
  24. L. S. Birks, “COMBINED XRD-XRF IMPROVE MATERIALS CHARACTERIZATION,” in Recent Advances in Analytical Spectroscopy: Elsevier, 1982, pp. 27–31. [CrossRef] [Google Scholar]
  25. A. Scordo, L. Breschi, C. Curceanu, M. Miliucci, F. Sirghi, and J. Zmeskal, “High resolution multielement XRF spectroscopy of extended and diffused sources with a graphite mosaic crystal based Von Hamos spectrometer,” Journal of Analytical Atomic Spectrometry, vol. 35, no. 1, pp. 155–168, 2020. [CrossRef] [Google Scholar]
  26. T. Konishi, J. Kawai, M. Fujiwara, T. Kurisaki, H. Wakita, and Y. Gohshi, “Chemical shift and lineshape of high-resolution Ni Ka x-ray fluorescence spectra,” X-Ray Spectrometry: An International Journal, vol. 28, no. 6, pp. 470–477, 1999. [Google Scholar]
  27. I. Liritzis and N. Zacharias, “Portable XRF of archaeological artifacts: current research, potentials and limitations,” X-ray fluorescence spectrometry (XRF) in geoarchaeology, pp. 109–142, 2011. [CrossRef] [Google Scholar]
  28. E. X. Miqueles, C. A. Pérez, V. I. Suárez, and R. F. C. Vescovi, “Grazing-incidence XRF analysis of layered samples: Detailed study of amplitude calculation,” Computer Physics Communications, vol. 194, pp. 33–42, 2015. [CrossRef] [Google Scholar]
  29. V. Panchuk, I. Yaroshenko, A. Legin, V. Semenov, and D. Kirsanov, “Application of chemometric methods to XRF-data-A tutorial review,” Analytica Chimica Acta, vol. 1040, pp. 19–32, 2018. [CrossRef] [PubMed] [Google Scholar]
  30. B. Vekemans, K. Janssens, L. Vincze, F. Adams, and P. Van Espen, “Comparison of several background compensation methods useful for evaluation of energy-dispersive X-ray fluorescence spectra,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 50, no. 2, pp. 149–169, 1995. [CrossRef] [Google Scholar]
  31. A. Andrle et al., “Shape-and element-sensitive reconstruction of periodic nanostructures with grazing incidence X-ray fluorescence analysis and machine learning,” Nanomaterials, vol. 11, no. 7, p. 1647, 2021. [CrossRef] [PubMed] [Google Scholar]
  32. C. R. Appoloni and F. L. Melquiades, “Portable XRF and principal component analysis for bill characterization in forensic science,” Applied Radiation and Isotopes, vol. 85, pp. 92–95, 2014. [CrossRef] [PubMed] [Google Scholar]
  33. Jiang, Xiaoyu, Fusheng Li, Qingya Wang, Jie Luo, Jun Hao, and Muqiang Xu. “Baseline correction method based on improved adaptive iteratively reweighted penalized least squares for the x-ray fluorescence spectrum.” Applied Optics 60, no. 19 (2021): 5707–5715. [CrossRef] [PubMed] [Google Scholar]
  34. D. C. Weindorf, S. Chakraborty, B. Li, S. Deb, A. Singh, and N. Y. Kusi, “Compost salinity assessment via portable X-ray fluorescence (PXRF) spectrometry,” Waste Management, vol. 78, pp. 158–163, 2018. [CrossRef] [Google Scholar]
  35. Andersen, Lars Klembt, Trevor J. Morgan, Aikaterini K. Boulamanti, Patricia Alvarez, Stanislav V. Vassilev, and David Baxter. “Quantitative X-ray fluorescence analysis of biomass: Objective evaluation of a typical commercial multi-element method on a WD-XRF spectrometer.” Energy & fuels 27, no. 12 (2013): 7439–7454. [CrossRef] [Google Scholar]
  36. Sitko, Rafał, and Beata Zawisza. “Quantification in X-ray fluorescence spectrometry.” X-ray spectroscopy (2012): 137–162. [Google Scholar]
  37. M. Kubista, R. Sjöback, S. Eriksson, and B. Albinsson, “Experimental correction for the innerfilter effect in fluorescence spectra,” Analyst, vol. 119, no. 3, pp. 417–419, 1994. [CrossRef] [Google Scholar]
  38. J. T. Sloop, G. L. Donati, and B. T. Jones, “Multiinternal standard calibration applied to inductively coupled plasma optical emission spectrometry,” Analytica Chimica Acta, vol. 1190, p. 339258, 2022. [CrossRef] [PubMed] [Google Scholar]
  39. G. Andermann and J. W. Kemp, “Scattered X-rays as internal standards in X-ray emission spectroscopy,” Analytical chemistry, vol. 30, no. 8, pp. 1306–1309, 1958. [CrossRef] [Google Scholar]
  40. L. Confalonieri, R. Crippa, and M. Milazzo, “An experimental approach to the analysis of binary alloys by the Rayleigh-Compton backscatter ratio method,” International Journal of Radiation Applications and Instrumentation. Part A. Applied Radiation and Isotopes, vol. 38, no. 2, pp. 139–142, 1987. [CrossRef] [Google Scholar]
  41. E. L. Gunn, “Fluorescent x-ray spectral analysis of powdered solids by matrix dilution,” Analytical Chemistry, vol. 29, no. 2, pp. 184–189, 1957. [CrossRef] [Google Scholar]
  42. A. Ludwig, R. Zarnetta, S. Hamann, A. Savan, and S. Thienhaus, “Development of multifunctional thin films using high-throughput experimentation methods,” International Journal of Materials Research, vol. 99, no. 10, pp. 1144–1149, 2008. [CrossRef] [Google Scholar]
  43. L. E. Eberly, “Multiple linear regression,” Topics in Biostatistics, pp. 165–187, 2007. [CrossRef] [PubMed] [Google Scholar]
  44. E. Ostertagová, “Modelling using polynomial regression,” Procedia Engineering, vol. 48, pp. 500–506, 2012. [CrossRef] [Google Scholar]
  45. P. Teunissen, “Nonlinear least squares,” 1990. [Google Scholar]
  46. G. R. Lachance, “Practical solution to the matrix problem in X-ray analysis,” Canadian Spectroscopy, vol. 11, pp. 43–48, 1966. [Google Scholar]
  47. R. Rousseau and F. Claisse, “Theoritical alpha coefficients for the Claisse-Quintin relation for X-ray spectrochemical analysis,” X-Ray Spectrometry, vol. 3, no. 1, pp. 31–36, 1974. [CrossRef] [Google Scholar]
  48. J. W. Criss and L. S. Birks, “Calculation methods for fluorescent x-ray spectrometry. Empirical coefficients versus fundamental parameters,” Analytical Chemistry, vol. 40, no. 7, pp. 1080–1086, 1968. [CrossRef] [Google Scholar]
  49. W. K. De Jongh, “X-ray fluorescence analysis applying theoretical matrix corrections. Stainless steel,” X-ray spectrometry, vol. 2, no. 4, pp. 151–158, 1973. [CrossRef] [Google Scholar]
  50. J. W. Criss, L. S. Birks, and J. V. Gilfrich, “Versatile X-ray analysis program combining fundamental parameters and empirical coefficients,” Analytical Chemistry, vol. 50, no. 1, pp. 33–37, 1978. [CrossRef] [Google Scholar]
  51. G. Y. Tao, P. A. Pella, and R. M. Rousseau, “NBSGSC-a FORTRAN program for quantitative x- ray fluorescence analysis. Technical note (final),” National Bureau of Standards, Washington, DC (USA) 1985. [Google Scholar]
  52. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems and their applications, vol. 13, no. 4, pp. 18–28, 1998. [CrossRef] [Google Scholar]
  53. G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, pp. 197–227, 2016. [CrossRef] [Google Scholar]
  54. A. K. Jain, J. Mao, and K. M. Mohiuddin, “Artificial neural networks: A tutorial,” Computer, vol. 29, no. 3, pp. 31–44, 1996. [CrossRef] [Google Scholar]
  55. J. Gu et al., “Recent advances in convolutional neural networks,” Pattern recognition, vol. 77, pp. 354–377, 2018. [CrossRef] [Google Scholar]
  56. L. R. Medsker and L. C. Jain, “Recurrent neural networks,” Design and Applications, vol. 5, pp. 64–67, 2001. [Google Scholar]
  57. K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree-structured long short-term memory networks,” arXiv preprint arXiv:1503.00075, 2015. [Google Scholar]
  58. E. J. Jones and A. B. McBratney, “In situ analysis of soil mineral composition through conjoint use of visible, near-infrared and X-ray fluorescence spectroscopy,” Digital soil morphometrics, pp. 51–62, 2016. [CrossRef] [Google Scholar]
  59. S.-Q. Wang, W.-D. Li, J. Li, and X.-S. Liu, “Prediction of soil texture using FT-NIR spectroscopy and PXRF spectrometry with data fusion,” Soil Science, vol. 178, no. 11, pp. 626–638, 2013. [CrossRef] [Google Scholar]
  60. B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, “Multisensor data fusion: A review of the state-of-the-art,” Information fusion, vol. 14, no. 1, pp. 28–44, 2013. [CrossRef] [Google Scholar]
  61. Wang Yaonan, Li Shutao, “Multi-sensor Information fusion and its Application Review,” Control and Decision, vol. 16, no. 5, pp. 518–522, 2001. [Google Scholar]
  62. K.H. Janssens, Automated data handling in X-ray analysis: An integration of expert systems and conventional software. Universitaire Instelling Antwerpen (Belgium), 1989. [Google Scholar]
  63. M. H. Kalos and P. A. Whitlock, Monte carlo methods. John Wiley & Sons, 2009. [Google Scholar]
  64. L. Vincze, K. Janssens, F. Adams, M. L. Rivers, and K. W. Jones, “A general Monte Carlo simulation of ED-XRF spectrometers. II: Polarized monochromatic radiation, homogeneous samples,” Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 50, no. 2, pp. 127–147, 1995. [CrossRef] [Google Scholar]
  65. J. A. Helsen and B. A. R. Vrebos, “Ageing of glass disks. A Monte Carlo simulation of the related XRF intensities,” X-Ray Spectrometry, vol. 15, no. 3, pp. 173–175, 1986. [CrossRef] [Google Scholar]
  66. Creswell, Antonia, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. “Generative adversarial networks: An overview.” IEEE signal processing magazine 35, no. 1 (2018): 53–65. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.