- Physical Review Journals
All JournalsPhysics Magazine

Physical Review B

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

High-speed x-ray reflectometry for characterization of thin film growth at high deposition rates

David Schumi-Mareček1, Andrew Nelson2, Erwin Pfeiler1, Maximilian Eder1, Florian Bertram3, and Stefan Kowarik1,*

  • *Contact author: stefan.kowarik@uni-graz.at
PDF

Phys. Rev. B 112, 235304 – Published 8 December, 2025

DOI: https://doi.org/10.1103/xxx9-8tk2

Abstract

We present a high-throughput, real-time study of PTCDI-C8 thin film growth using quick x-ray reflectivity (qXRR) with 12 ms time resolution, combined with machine learning-based analysis. In situ qXRR enables monitoring of vacuum deposition at growth rates from 1 to 30Å/s, accessing a previously unexplored regime in molecular beam deposition. To efficiently analyze the resulting ∼20000 reflectivity curves, we employ a convolutional neural network trained on a physics-informed multilayer model. This approach robustly extracts key structural parameters—including thickness, roughness, and crystalline versus amorphous content—from noisy data. We quantify interface roughness as a function of both film thickness and growth rate, identifying rapid roughening with a scaling exponent of β=0.62 with film thickness and a secondary scaling exponent of γ=0.21 with growth rate. Additionally, we observe a reduction in the coherently ordered film thickness and a rise in amorphous content at higher deposition rates. These results demonstrate that combining qXRR with machine learning provides quantitative access to fast kinetic growth processes, offering a powerful tool for in situ characterization and morphological control in organic thin film fabrication.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (102)

  1. K. Sakurai, M. Mizusawa, and M. Ishii, Recent novel x-ray reflectivity techniques: Moving towards quicker measurement to observe changes at surface and buried interfaces, Trans. Mater. Res. Soc. Jpn. 32, 181 (2007).
  2. H. Ogawa, H. Masunaga, S. Sasaki, S. Goto, T. Tanaka, T. Seike, S. Takahashi, K. Takeshita, N. Nariyama, K. H. Ohaski et al., Experimental station for multiscale surface structural analyses of soft-material films at SPring-8 via a GISWAX/GIXD/XR-integrated system, Polym. J. 45, 109 (2013).
  3. H. Joress, J. D. Brock, and A. R. Woll, Quick x-ray reflectivity using monochromatic synchrotron radiation for time-resolved applications, J. Synchrotron Radiat. 25, 706 (2018).
  4. H. Joress, S. Q. Arlington, T. P. Weihs, J. D. Brock, and A. Woll, X-ray reflectivity with a twist: Quantitative time-resolved x-ray reflectivity using monochromatic synchrotron radiation, Appl. Phys. Lett. 114, 081904 (2018).
  5. M. Lippmann, A. Buffet, K. Pflaum, A. Ehnes, A. Ciobanu, and O. H. Seeck, A new setup for high resolution fast x-ray reflectivity data acquisition, Rev. Sci. Instrum. 87, 113904 (2016).
  6. D. Schumi-Mareček, F. Bertram, P. Mikulík, D. Varshney, J. Novák, and S. Kowarik, Millisecond x-ray reflectometry and neural network analysis: Unveiling fast processes in spin coating, J. Appl. Crystallogr. 57, 314 (2024).
  7. C. Bishop, Neural networks and their applications, Rev. Sci. Instrum. 65, 1803 (1994).
  8. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006).
  9. D. Mareček, J. Oberreiter, A. Nelson, and S. Kowarik, Faster and lower-dose x-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement, J. Appl. Cryst. 55, 1305 (2022).
  10. L. Pithan, V. Starostin, D. Mareček, L. Petersdorf, C. Volter, V. Munteanu, M. Jankowski, O. Konovalov, A. Gerlach, A. Hinderhofer et al., Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments, J. Synchrotron Radiat. 30, 1064 (2023).
  11. V. Munteanu, V. Starostin, A. Greco, L. Pithan, A. Gerlach, A. Hinderhofer, S. Kowarik, and F. Schreiber, Neural network analysis of neutron and x-ray reflectivity data incorporating prior knowledge, J. Appl. Crystallogr. 57, 456 (2024).
  12. A. R. J. Nelson and S. W. Prescott, refnx: Neutron and x-ray reflectometry analysis in Python, J. Appl. Crystallogr. 52, 193 (2019).
  13. M. B. Maranville, P. Kienzle, R. Sheridan, M. Doucet, A. J. Caruana, J. Borreguero, A. Nelson, D. P. Hoogerheide, Glass, A. Book, M. Backman, P. P. Balakrishnan, and R. Ghose, reflectometry/refl1d: v1.0.1a1 (Zenodo, 2025), doi:10.5281/zenodo.17651288.
  14. M. Björck and G. Andersson, GenX An extensible x-ray reflectivity refinement program utilizing differential evolution, J. Appl. Cryst. 40, 1174 (2007).
  15. M. Sawatzki-Park, S.-J. Wang, H. Kleemann, and K. Leo, Highly ordered small molecule organic semiconductor thin-films enabling complex, high-performance multi-junction devices, Chem. Rev. 123, 8232 (2023).
  16. C. D. Dimitrakopoulos and D. J. Mascaro, Organic thin-film transistors: A review of recent advances, IBM J. Res. Dev. 45, 11 (2001).
  17. H. Klauk, Organic thin-film transistors, Chem. Soc. Rev. 39, 2643 (2010).
  18. B. Kumar, B. K. Kaushik, and Y. S. Negi, Perspectives and challenges for organic thin film transistors: Materials, devices, processes and applications, J. Mater. Sci. Mater. Electron. 25, 1 (2014).
  19. P. Sachan and P. C. Mondal, Coordination-driven opto-electroactive molecular thin films in electronic circuits, J. Mater. Chem. C Mater. 10, 14532 (2022).
  20. M. Eslamian, Inorganic and organic solution-processed thin film devices, Nano-Micro Lett. 9, 3 (2017).
  21. C. Li, X. Liu, X. Du, T. Yang, Q. Li, and L. Jin, Preparation and optical properties of nanostructure thin films, Appl. Nanosci. 11, 1967 (2021).
  22. M. H. Mondal, Study of in-situ structural and chemical changes of ultrathin polymer films, Appl. Phys. A 124, 864 (2018).
  23. M. Lammel, K. Geishendorf, M. A. Choffel, D. M. Hamann, D. C. Johnson, K. Nielsch, and A. Thomas, Fast Fourier transform and multi-Gaussian fitting of XRR data to determine the thickness of ALD grown thin films within the initial growth regime, Appl. Phys. Lett. 117, 213106 (2020).
  24. J. Xin, P. Sun, F. Zhu, Y. Wang, and D. Yan, Doped crystalline thin-film deep-blue organic light-emitting diodes, J. Mater. Chem. C Mater. 9, 2236 (2021).
  25. J. P. Spindler, J. W. Hamer, and M. E. Kondakova, OLED manufacturing equipment and methods, in Handbook of Advanced Lighting Technology (Springer International Publishing, Cham, 2014), pp. 1–21.
  26. N. N. Dinh, T. S. T. Khanh, L. M. Long, N. D. Cuong, and N. P. H. Nam, Nanomaterials for organic optoelectronic devices: Organic light-emitting diodes, organics solar cells and organic gas sensors, Mater. Trans. 61, 1422 (2020).
  27. S. R. Forrest, Organic thin film transistors, in Organic Electronics (Oxford University Press, Oxford, 2020), pp. 803–917.
  28. X. Guo, F. Yan, P. Cain, T. Nga Ng, W. Tang, R. Sporea, L. Li, J. Carrabina, S. Ogier, A. Perinot et al., Current status and opportunities of organic thin-film transistor technologies, IEEE Trans. Electron Devices 64, 1906 (2017).
  29. S. Kowarik, A. Gerlach, and F. Schreiber, Organic molecular beam deposition: Fundamentals, growth dynamics, and in situ studies, J. Phys. Conden. Mat. 20, 184005 (2008).
  30. P. Politi, G. Grenet, A. Marty, A. Ponchet, and J. Villain, Instabilities in crystal growth by atomic or molecular beams, Phys. Rep. 324, 271 (2000).
  31. W. Nunn, T. K. Truttmann, and B. Jalan, A review of molecular-beam epitaxy of wide bandgap complex oxide semiconductors, J. Mater. Res. 36, 4846 (2021).
  32. S. Das Sarma, C. J. Lanczycki, R. Kotlyar, and S. V. Ghaisas, Scale invariance and dynamical correlations in growth models of molecular beam epitaxy, Phys. Rev. E 53, 359 (1996).
  33. J. Krug, Four lectures on the physics of crystal growth, Physica A 313, 47 (2002).
  34. A. C. Dürr, F. Schreiber, K. A. Ritley, V. Kruppa, J. Krug, H. Dosch, and B. Struth, Rapid roughening in thin film growth of an organic semiconductor (Diindenoperylene), Phys. Rev. Lett. 90, 016104 (2003).
  35. F. Munko, C. C. Luukkonen, I. S. S. Carrasco, F. D. A. A. Reis, and M. Oettel, Island formation in heteroepitaxial growth, Phys. Rev. E 111, 035501 (2025).
  36. J. T. Dull, X. Chen, H. M. Johnson, M. C. Otani, F. Schreiber, P. Clancy, and B. P. Rand, A comprehensive picture of roughness evolution in organic crystalline growth: The role of molecular aspect ratio, Mater. Horiz. 9, 2752 (2022).
  37. G. Pradhan, P. P. Dey, and A. K. Sharma, Anomalous kinetic roughening in growth of MoS2 films under pulsed laser deposition, RSC Adv. 9, 12895 (2019).
  38. S. Yim and T. S. Jones, Anomalous scaling behavior and surface roughening in molecular thin-film deposition, Phys. Rev. B 73, 161305 (2006).
  39. N. M. Das, D. Roy, N. Clarke, V. Ganesan, and P. S. Gupta, Dynamics of roughening and growth kinetics of CdS–polyaniline thin films synthesized by the Langmuir–Blodgett technique, RSC Adv. 4, 32490 (2014).
  40. X. Zhang, E. Barrena, D. Goswami, D. G. de Oteyza, C. Weis, and H. Dosch, Evidence for a layer-dependent Ehrlich-Schwöbel barrier in organic thin film growth, Phys. Rev. Lett. 103, 136101 (2009).
  41. Y. Zhang, J. Qiao, S. Gao, B. Xu, P. Wang, F. Hu, M. Xiao, Z. Yang, X. Wang, H. Xu et al., Probing carrier transport and structure-property relationship of highly ordered organic semiconductors at the two-dimensional limit, Phys. Rev. Lett. 116, 016602 (2016).
  42. E. Gann, M. Caironi, Y.-Y. Noh, Y.-H. Kim, and C. R. McNeill, Diffractive x-ray waveguiding reveals orthogonal crystalline stratification in conjugated polymer thin films, Macromolecules 51, 2979 (2018).
  43. A. Hinderhofer, K. Yonezawa, K. Kato, and F. Schreiber, Structure matters combining x-ray scattering and ultraviolet photoelectron spectroscopy for studying organic thin films, in Electronic Processes in Organic Electronics, edited by H. Ishii, K. Kudo, and T. Nakayama (Springer, Tokyo, 2014), pp. 109–129.
  44. Y. Qian, X. Zhang, D. Qi, L. Xie, B. K. Chandran, X. Chen, and W. Huang, Thin-film organic semiconductor devices: From flexibility to ultraflexibility, Sci. China Mater. 59, 589 (2016).
  45. A. M. Goryaeva, C. Fusco, M. Bugnet, and J. Amodeo, Influence of an amorphous surface layer on the mechanical properties of metallic nanoparticles under compression, Phys. Rev. Mater. 3, 033606 (2019).
  46. X. Wang, M. Wang, W. Jiang, D. Zhang, H. Wang, and Q. Shan, Mechanical reliability of flexible a-InGaZnO TFTs under dynamic stretch stress, IEEE Trans. Electron Devices 65, 2863 (2018).
  47. J. Bauri, R. B. Choudhary, and G. Mandal, Recent advances in efficient emissive materials-based OLED applications: A review, J. Mater. Sci. 56, 18837 (2021).
  48. J. Chen and Q. Zhan, Ellipsometry, in Handbook of Advanced Nondestructive Evaluation (Springer International Publishing, Cham, 2019), pp. 513–540.
  49. M. Tolan, X-Ray Scattering from Soft-Matter Thin Films (Springer-Verlag, Berlin, Heidelberg, 1999).
  50. P. I. Cohen, P. R. Pukite, J. M. Van Hove, and C. S. Lent, Reflection high energy electron diffraction studies of epitaxial growth on semiconductor surfaces, J. Vac. Sci. Technol. 4, 1251 (1986).
  51. J. Yu, Y. Xing, Z. Shen, Y. Zhu, D. Neher, N. Koch, and G. Lu, Infrared spectroscopy depth profiling of organic thin films, Mater. Horiz. 8, 1461 (2021).
  52. S. Festersen, S. B. Hrkac, C. T. Koops, B. Runge, T. Dane, B. M. Murphy, and O. M. Magnussen, X-ray reflectivity from curved liquid interfaces, J. Synchrotron Radiat. 25, 432 (2018).
  53. U. Pietsch, V. Holý, and T. Baumbach, High-Resolution X-Ray Scattering (Springer New York, New York, NY, 2004).
  54. C. Suryanarayana and M. G. Norton, X-Ray Diffraction (Springer US, Boston, MA, 1998).
  55. A. Benediktovich, I. Feranchuk, and A. Ulyanenkov, Theoretical Concepts of X-Ray Nanoscale Analysis (Springer Berlin Heidelberg, Berlin, Heidelberg, 2014), Vol. 183.
  56. V. Holý, P. Ullrich, and T. Baumbach, High-Resolution X-Ray Scattering from Thin Films and Multilayers (Springer Berlin Heidelberg, Berlin, Heidelberg, 1999), Vol. 149.
  57. A. Braslau, P. S. Pershan, G. Swislow, B. M. Ocko, and J. Als-Nielsen, Capillary waves on the surface of simple liquids measured by x-ray reflectivity, Phys. Rev. A (Coll. Park) 38, 2457 (1988).
  58. M. W. A. Skoda, B. Thomas, M. Hagreen, F. Sebastiani, and C. Pfrang, Simultaneous neutron reflectometry and infrared reflection absorption spectroscopy (IRRAS) study of mixed monolayer reactions at the air-water interface, RSC Adv. 7, 34208 (2017).
  59. T. P. Russell, X-ray and neutron reflectivity for the investigation of polymers, Materials Science Reports 5, 171 (1990).
  60. S. Kowarik, A. Gerlach, S. Sellner, F. Schreiber, L. Cavalcanti, and O. Konovalov, Real-time observation of structural and orientational transitions during growth of organic thin films, Phys. Rev. Lett. 96, 125504 (2006).
  61. J. Daillant and A. Gibaud, X-Ray and Neuron Reflectivity: Principles and Applications (Springer Berlin Heidelberg, Berlin, Heidelberg, 1999), Vol. 58.
  62. K. Lament, M. Grodzicki, R. Wasielewski, P. Mazur, and A. Ciszewski, Growth and properties of ultra-thin PTCDI-C8 films on GaN(0001), Crystals (Basel) 14, 201 (2024).
  63. A. Zykov, S. Bommel, C. Wolf, L. Pithan, C. Weber, P. Beyer, G. Santoro, J. P. Rabe, and S. Kowarik, Diffusion and nucleation in multilayer growth of PTCDI-C8 studied with in situ x-ray growth oscillations and real-time small angle x-ray scattering, J. Chem. Phys. 146, 052803 (2017).
  64. T. N. Krauss, E. Barrena, X. N. Zhang, D. G. de Oteyza, J. Major, V. Dehm, F. Würthner, L. P. Cavalcanti, and H. Dosch, Three-dimensional molecular packing of thin organic films of PTCDI-C8 determined by surface x-ray diffraction, Langmuir 24, 12742 (2008).
  65. G. Witte and C. Wöll, Growth of aromatic molecules on solid substrates for applications in organic electronics, J. Mater. Res. 19, 1889 (2004).
  66. R. J. Chesterfield, J. C. McKeen, C. R. Newman, P. C. Ewbank, D. A. da Silva Filho, J.-L. Brédas, L. L. Miller, K. R. Mann, and C. D. Frisbie, Organic thin film transistors based on N -Alkyl perylene diimides: Charge transport kinetics as a function of gate voltage and temperature, J. Phys. Chem. B 108, 19281 (2004).
  67. D. J. Gundlach, K. P. Pernstich, G. Wilckens, M. Grüter, S. Haas, and B. Batlogg, High mobility n-channel organic thin-film transistors and complementary inverters, J. Appl. Phys. 98, 064502 (2005).
  68. S. Tatemichi, M. Ichikawa, T. Koyama, and Y. Taniguchi, High mobility n-type thin-film transistors based on N,N′-ditridecyl perylene diimide with thermal treatments, Appl. Phys. Lett. 89, 112108 (2006).
  69. C. Rolin, K. Vasseur, S. Schols, M. Jouk, G. Duhoux, R. Müller, J. Genoe, and P. Heremans, High mobility electron-conducting thin-film transistors by organic vapor phase deposition, Appl. Phys. Lett. 93, 033305 (2008).
  70. O. H. Seeck, H. Franz, F. Bertam, M. Greve, A. Beerlink, K. Pflaum, B. M. Murphy, H. Schulte-Schrepping, O. Magnussen, C. Deiter et al., The high-resolution diffraction beamline P08 at PETRA III, J. Synchrotron Radiat. 19, 30 (2012).
  71. See Supplemental Material at http://link.aps.org/supplemental/10.1103/xxx9-8tk2 for details.
  72. K. A. Ritley, B. Krause, F. Schreiber, and H. Dosch, A portable ultrahigh vacuum organic molecular beam deposition system for in situ x-ray diffraction measurements, Rev. Sci. Instrum. 72, 1453 (2001).
  73. S. R. Forrest, Ultrathin organic films grown by organic molecular beam deposition and related techniques, Chem. Rev. 97, 1793 (1997).
  74. O. Filies, O. Böling, K. Grewer, J. Lekki, M. Lekka, Z. Stachura, and B. Cleff, Surface roughness of thin layers—a comparison of XRR and SFM measurements, Appl. Surf. Sci. 141, 357 (1999).
  75. P. R. Nayak, Random process model of rough surfaces, J. Lubr. Technol. 93, 398 (1971).
  76. T. Velinov, L. Ahtapodov, A. Nelson, M. Gateshki, and M. Bivolarska, Influence of the surface roughness on the properties of Au films measured by surface plasmon resonance and x-ray reflectometry, Thin. Solid. Films 519, 2093 (2011).
  77. Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature (London) 521, 436 (2015).
  78. K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2016), pp. 770–778.
  79. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2017), pp. 2261–2269.
  80. M. Tan and Q. Le, EfficientNet: Rethinking model scaling for convolutional neural networks, in Proceedings of the 36th International Conference on Machine Learning, edited by K. Chaudhuri, and R. Salakhutdinov, Vol. 97 (PMLR, 2019), pp. 6105–6114.
  81. F. Murtagh, Multilayer perceptrons for classification and regression, Neurocomputing 2, 183 (1991).
  82. D. Hendrycks and K. Gimpel, Gaussian error linear units (GELUs), arXiv:1606.08415.
  83. D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings 1 (ICLR, 2014).
  84. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., TensorFlow: A System for Large-Scale Machine Learning, in OSDI'16: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation (ACM, 2016), pp. 265–283.
  85. A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, and S. Kowarik, Fast fitting of reflectivity data of growing thin films using neural networks, J. Appl. Crystallogr. 52, 1342 (2019).
  86. A. Greco, V. Starostin, A. Hinderhofer, A. Gerlach, M. W. A. Skoda, S. Kowarik, and F. Schreiber, Neural network analysis of neutron and x-ray reflectivity data: Pathological cases, performance and perspectives, Mach. Learn Sci. Technol. 2, 1 (2021).
  87. D. Mironov, J. H. Durant, R. Mackenzie, and J. F. K. Cooper, Towards automated analysis for neutron reflectivity, Mach. Learn Sci. Technol. 2, 035006 (2021).
  88. M. Doucet, R. K. Archibald, and W. T. Heller, Machine learning for neutron reflectometry data analysis of two-layer thin films, Mach. Learn Sci. Technol. 2, 035001 (2021).
  89. J. M. C. Loaiza and Z. Raza, Towards reflectivity profile inversion through artificial neural networks, Mach. Learn Sci. Technol. 2, 025034 (2021).
  90. Z. Chen, N. Andrejevic, N. C. Drucker, T. Nguyen, R. P. Xian, T. Smidt, Y. Wang, R. Ernstorfer, D. A. Tennant, M. Chan et al., Machine learning on neutron and x-ray scattering and spectroscopies, Chem. Phys. Rev. 2, 031301 (2021).
  91. V. Starostin, M. Dax, A. Gerlach, A. Hinderhofer, Á. Tejero-Cantero, and F. Schreiber, Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimation, Sci. Adv. 11, eadr9668 (2024).
  92. J. Yang, S. Yim, and T. S. Jones, Molecular-orientation-induced rapid roughening and morphology transition in organic semiconductor thin-film growth, Sci. Rep. 5, 9441 (2015).
  93. A.-L. Barabási and H. E. Stanley, Fractal Concepts in Surface Growth (Cambridge University Press, Cambridge, 1995).
  94. F. Family and T. Vicsek, Scaling of the active zone in the Eden process on percolation networks and the ballistic deposition model, J. Phys. A Math Gen. 18, L75 (1985).
  95. T. Vicsek and F. Family, Dynamic scaling for aggregation of clusters, Phys. Rev. Lett. 52, 1669 (1984).
  96. F. L. Forgerini and R. Marchiori, A brief review of mathematical models of thin film growth and surfaces, Biomatter 4, e28871 (2014).
  97. K. Bordo and H.-G. Rubahn, Effect of deposition rate on structure and surface morphology of thin evaporated al films on dielectrics and semiconductors, Mater. Sci. 18, 392 (2012).
  98. D. G. Foster, Y. Shapir, and J. Jorne, The effect of rate of surface growth on roughness scaling, J. Electrochem. Soc. 152, C462 (2005).
  99. J. Krug, Origins of scale invariance in growth processes, Adv. Phys. 46, 139 (1997).
  100. G. Hlawacek, P. Puschnig, P. Frank, A. Winkler, C. Ambrosch-Draxl, and C. Teichert, Characterization of step-edge barriers in organic thin-film growth, Science 321, 108 (2008).
  101. A. Greco, N. Rußegger, A. Hinderhofer, E. Edel, F. Schreiber, V. Starostin, V. Munteanu, A. Gerlach, I. Dax, C. Shen et al., Neural network analysis of neutron and x-ray reflectivity data: Automated analysis using mlreflect, experimental errors and feature engineering, J. Appl. Crystallogr. 55, 362 (2022).
  102. D. Schumi-Mareček, M. Haberl, A. Nelson, E. Pfeiler, M. Eder, F. Bertram, and S. Kowarik, “High-speed x-ray reflectometry dataset for PTCDI-C8 molecules” [Data set], Zenodo (2025), https://doi.org/10.5281/zenodo.17340970.

Outline

Information

Phys. Rev. B 112, 235304– Published 8 December, 2025
  • Received 21 January 2025
  • Revised 19 July 2025
  • Accepted 4 September 2025
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation