journal article·2025-09-01Open access

Discovery of an energy-dependent many-body process in the K β spectrum of manganese metal using extended-range high-energy-resolution fluorescence detection with advanced structural insights from principal component analysis

Jack Stephens, Ramesh Rijal, Daniel Sier, Nicholas T. T. Tran, Jonathan W. Dean, Paul Di Pasquale, Tony Kirk, Minh Dao, Chanh Q. Tran, Shusaku Hayama, Sofia Diaz-Moreno, Christopher T. Chantler

IUCrJ· Volume 12· Issue 5

Abstract

The discovery of the novel n = 2 satellite transition in the K β emission spectrum of manganese and its evolution with incident photon energy are presented. Using the XR-HERFD (extended-range high-energy-resolution fluorescence detection) technique, we conclusively demonstrate the existence of this phenomenon with a statistical significance corresponding to 652 σ se across the measured spectrum, far above the discovery threshold of 3–6 σ se . We apply principal component analysis (PCA) to the XR-HERFD data to extract advanced structural insights. The evolution of this novel spectral feature and physical process are quantified by incorporating regression, revealing the increase in intensity over a wide range of incident photon energies. We validate these findings through independent test data. These results directly challenge the conventional treatment of the many-body reduction factor S 0 2 as a constant independent of incident photon energy in the standard XAFS (X-ray absorption fine structure) equation. Thereby, these results present compelling evidence that S 0 2 should be modelled as a varying function of incident photon energy, marking the first observation of this behaviour in K β spectra. This facilitates a greater quantitative understanding of HERFD spectra and a comprehensive representation of many-body effects in condensed matter systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: X-ray Spectroscopy and Fluorescence Analysis · Electron and X-Ray Spectroscopy Techniques · Electrochemical Analysis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (71)

Show source-provided references
1. Åberg · Phys. Rev. · 1967 · 156 · 3510.1103/PhysRev.156.35 ↗
2. Ament · Rev. Mod. Phys. · 2011 · 83 · 70510.1103/RevModPhys.83.705 ↗
3. Asiedu · Br. J. Math. Comput. Sci. · 2016 · 12(6) · 110.9734/BJMCS/2016/22141 ↗
4. Bauer · Phys. Chem. Chem. Phys. · 2014 · 16 · 1382710.1039/C4CP00904E ↗
5. Beckwith · Inorg. Chem. · 2011 · 50 · 839710.1021/ic200970t ↗
6. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B. & Varoquaux, G. (2013). ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108-122.
7. Carlson · Phys. Rev. · 1965 · 140 · A105710.1103/PhysRev.140.A1057 ↗
8. Chantler · Nat. Rev. Methods Primers · 2024 · 4 · 110.1038/s43586-024-00366-8 ↗
9. Chantler, C. T., Smale, L. F. & Hudson, L. T. (2025). International Tables for Crystallography, Vol. C, 4th ed., edited by T. R. Welberry. International Union of Crystallography. In the press.
10. de Groot, F. & Kotani, A. (2008). Core level spectroscopy of solids. CRC Press.10.1201/9781420008425 ↗
11. Dean · Molecules · 2024 · 29 · 419910.3390/molecules29174199 ↗
12. Deutsch · Phys. Rev. Lett. · 1996 · 76 · 242410.1103/PhysRevLett.76.2424 ↗
13. Diaz-Moreno · J. Synchrotron Rad. · 2012 · 19 · 86310.1107/S090904951203854X ↗
14. Diaz-Moreno · J. Phys. Conf. Ser. · 2009 · 190 · 01203810.1088/1742-6596/190/1/012038 ↗
15. Eisenberger · Phys. Rev. B · 1976 · 13 · 237710.1103/PhysRevB.13.2377 ↗
16. Gamblin · J. Electron Spectrosc. Relat. Phenom. · 2001 · 113 · 17910.1016/S0368-2048(00)00416-3 ↗
17. Gaur · Proc. Indian Natl. Sci. Acad. · 2013 · 79 · 921
18. Gel'mukhanov · Rev. Mod. Phys. · 2021 · 93 · 03500110.1103/RevModPhys.93.035001 ↗
19. Glatzel · Coord. Chem. Rev. · 2005 · 249 · 6510.1016/j.ccr.2004.04.011 ↗
20. Glatzel · AIP Conf. Proc. · 2003 · 652 · 25010.1063/1.1536383 ↗
21. Glatzel · Phys. Rev. B · 2001 · 64 · 04510910.1103/PhysRevB.64.045109 ↗
22. Glatzel · J. Electron Spectrosc. Relat. Phenom. · 2013 · 188 · 1710.1016/j.elspec.2012.09.004 ↗
23. Günter · J. Phys. Conf. Ser. · 2016 · 712 · 01207110.1088/1742-6596/712/1/012071 ↗
24. Guo · Chem. Phys. Lett. · 1995 · 235 · 15210.1016/0009-2614(95)00083-G ↗
25. Hämäläinen · Phys. Rev. Lett. · 1991 · 67 · 285010.1103/PhysRevLett.67.2850 ↗
26. Hao · J. Educ. Behav. Stat. · 2019 · 44 · 34810.3102/1076998619832248 ↗
27. Hayama · J. Phys. Condens. Matter · 2021 · 33 · 28400310.1088/1361-648X/abfe93 ↗
28. Hölzer · Phys. Rev. A · 1997 · 56 · 455410.1103/PhysRevA.56.4554 ↗
29. Huang · At. Data Nucl. Data Tables · 1976 · 18 · 24310.1016/0092-640X(76)90027-9 ↗
30. Jolliffe, I. (2002). Principal component analysis. New York: Springer-Verlag.
31. Jolliffe · Phil. Trans. R. Soc. A · 2016 · 374 · 2015020210.1098/rsta.2015.0202 ↗
32. Kavčič, M. & Žitnik, M. (2024). International Tables for Crystallography, Vol. I, ch. 5.5, edited by C. T. Chantler, F. Boscherini & B. Bunker. International Union of Crystallography.
33. Kheifets · Atoms · 2022 · 10 · 8910.3390/atoms10030089 ↗
34. Kim, D. & You, K. (2023). arXiv:2307.15213.
35. Kochur · J. Phys. B At. Mol. Opt. Phys. · 2006 · 39 · 333510.1088/0953-4075/39/16/016 ↗
36. Kraft · Rev. Sci. Instrum. · 1996 · 67 · 68110.1063/1.1146657 ↗
37. Lafuerza · Nanoscale · 2020 · 12 · 1627010.1039/D0NR01983F ↗
38. Lancaster · Science · 2011 · 334 · 97410.1126/science.1206445 ↗
39. Lee · Phys. Rev. B · 1977 · 15 · 286210.1103/PhysRevB.15.2862 ↗
40. Lee · Phys. Rev. B · 1975 · 11 · 279510.1103/PhysRevB.11.2795 ↗
41. Martin-Diaconescu · Inorg. Chem. · 2016 · 55 · 343110.1021/acs.inorgchem.5b02842 ↗
42. Melia · Phys. Rev. A · 2023 · 107 · 01280910.1103/PhysRevA.107.012809 ↗
43. Millikan, R. (1917). The electron: Its isolation and measurement and the determination of some of its properties. University of Chicago Press.
44. Moretti Sala · J. Synchrotron Rad. · 2018 · 25 · 58010.1107/S1600577518001200 ↗
45. Mukoyama · X-ray Spectrom. · 2009 · 38 · 13810.1002/xrs.1143 ↗
46. Nguyen · Phys. Rev. A · 2022 · 105 · 02281110.1103/PhysRevA.105.022811 ↗
47. Pedersen · J. Chem. Phys. · 2023 · 159 · 024121-1-1210.1063/5.0156612 ↗
48. Pedregosa · J. Mach. Learn. Res. · 2011 · 12 · 2825
49. Pollock · J. Am. Chem. Soc. · 2011 · 133 · 559410.1021/ja200560z ↗
50. Praus · Open Chem. · 2005 · 3 · 73110.2478/BF02475200 ↗
51. Praus · Water SA · 2005 · 31 · 417
52. Ramsay, J. & Silverman, B. W. (2006). Functional data analysis. Springer.10.1007/b98888 ↗
53. Rees · J. Biol. Inorg. Chem. · 2016 · 21 · 79310.1007/s00775-016-1358-7 ↗
54. Rehr · Rev. Mod. Phys. · 2000 · 72 · 62110.1103/RevModPhys.72.621 ↗
55. Rehr · Phys. Rev. B · 1978 · 17 · 56010.1103/PhysRevB.17.560 ↗
56. Rijal · J. Synchrotron Rad. · 2025 · 32 · 99410.1107/S1600577525004084 ↗
57. Roy · J. Synchrotron Rad. · 2001 · 8 · 110310.1107/S0909049501008160 ↗
58. Schmitt · J. Synchrotron Rad. · 2014 · 21 · 106510.1107/S1600577514017123 ↗
59. Sier · IUCrJ · 2024 · 11 · 62010.1107/S2052252524005165 ↗
60. Sier · J. Appl. Cryst. · 2025 · 58 · 4210.1107/S1600576724010574 ↗
61. Stern · Contemp. Phys. · 1978 · 19 · 28910.1080/00107517808210887 ↗
62. Stern · Phys. Rev. B · 1974 · 10 · 302710.1103/PhysRevB.10.3027 ↗
63. Stöhr · Phys. Rev. Lett. · 1983 · 51 · 82110.1103/PhysRevLett.51.821 ↗
64. Thomas · Phys. Rev. Lett. · 1984 · 52 · 41710.1103/PhysRevLett.52.417 ↗
65. Thompson, A., Attwood, D., Gullikson, E., Howells, M. K., Kim, K., Kirz, J., Kortright, J., Lindau, I., Liu, Y., Pianetta, P., Robinson, A., Scofield, J., Underwood, J., Williams, G. & Winick, H. (2001). X-ray data booklet. Lawrence Berkeley National Laboratory, USA.
66. Tran · J. Synchrotron Rad. · 2023 · 30 · 60510.1107/S1600577523002539 ↗
67. Valenza · Phys. Rev. A · 2017 · 96 · 03250410.1103/PhysRevA.96.032504 ↗
68. Vankó · J. Phys. Chem. B · 2006 · 110 · 1164710.1021/jp0615961 ↗
69. Wall, M. E., Rechtsteiner, A. & Rocha, L. M. (2003). Singular value decomposition and principal component analysis. In A practical approach to microarray data analysis, edited by D. P. Berrar, W. Dubitzky & M. Granzow, pp. 91-109. Springer.10.1007/0-306-47815-3_5 ↗
70. Wang, Y. & Zhu, L. (2017). 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 471-475. IEEE.
71. Witten, I. H., Frank, E., Hall, M. A. & Pal, C. J. (2016). Data mining: Practical machine learning tools. Morgan Kaufmann Series in Data Management Systems. Amsterdam: Elsevier.