Analysis of Cd and Ag clusters by concurrent standard and high-resolution micro-PIXE spectroscopy

Iva Božičević Mihalić, Anja Mioković, Masedi Carington Masekane and Stjepko Fazinić

Radiation Physics and Chemistry, 2025, 229, 112480
DOI: 10.1016/j.radphyschem.2024.112480

Scanning X-ray Fluorescence Data Analysis for the Identification of Byzantine Icons’ Materials, Techniques, and State of Preservation: A Case Study

Theofanis Gerodimos, Anastasios Asvestas, Georgios P. Mastrotheodoros, Giannis Chantas, Ioannis Liougos, Aristidis Likas and Dimitrios F. Anagnostopoulos

J. Imaging, 2022, 8, 147
DOI: 10.3390/jimaging8050147

Practices and Rules of 16th Century Genoese Gilding: Exploring Gold Leaf Thickness and Caratage through X-ray and Ion Beam Techniques

Letizia Ciarlo, Massimo Chiari, Maria Clelia Galassi, Maurizio Ferretti, Elias Sideras-Haddad, Alessandro Zucchiatti and Elena Castagnotto

Heritage, 2024, 7, 3630
DOI: 10.3390/heritage7070172

Applications of machine learning in ion beam analysis of materials

Tiago Fiorini da Silva

Journal of Vacuum Science & Technology A, 2025, 43
DOI: 10.1116/6.0004277

Clustering Methods for the Characterization of Synchrotron Radiation X‐Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

Nathaly De La Rosa, Niccolò Peruzzi, Till Dreier, My Truong, Ulf Johansson, Sebastian Kalbfleisch, Isabel Gonçalves and Martin Bech

Advanced Intelligent Systems, 2024, 6
DOI: 10.1002/aisy.202400052

Combining synchrotron radiation techniques for the analysis of gold coins from the Roman Empire

I. Carlomagno, P. Zeller, M. Amati, G. Aquilanti, E. Prenesti, G. Marussi, M. Crosera and G. Adami

Sci Rep, 2022, 12
DOI: 10.1038/s41598-022-19682-8

Advanced 2D-PIXE/RBS processing with Machine Learning at the New AGLAE facility for ancient layered objects

Astrid Tazzioli, Quentin Lemasson, Alexandre Girard, Laurent Pichon, Brice Moignard and Claire Pacheco

Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 2024, 555, 165469
DOI: 10.1016/j.nimb.2024.165469

Open-Source Visual Programming Software for Introducing Principal Component Analysis to the Analytical Curriculum

Tai-Sheng Yeh

J. Chem. Educ., 2025, 102, 1428
DOI: 10.1021/acs.jchemed.4c00311

Antimony and Nickel Impurities in Blue and Green Copper Pigments

Sylwia Svorová Pawełkowicz, Barbara Wagner, Jakub Kotowski, Grażyna Zofia Żukowska, Bożena Gołębiowska, Rafał Siuda and Petras Jokubauskas

Minerals, 2021, 11, 1236
DOI: 10.3390/min11111236

Electromagnetic Techniques Applied to Cultural Heritage Diagnosis: State of the Art and Future Prospective: A Comprehensive Review

Patrizia Piersigilli, Rocco Citroni, Fabio Mangini and Fabrizio Frezza

Applied Sciences, 2025, 15, 6402
DOI: 10.3390/app15126402

The Role of PIXE and XRF in Heritage Science: The INFN-CHNet LABEC Experience

Leandro Sottili, Lorenzo Giuntini, Anna Mazzinghi, Mirko Massi, Luca Carraresi, Lisa Castelli, Caroline Czelusniak, Francesca Giambi, Pier Andrea Mandò, Marco Manetti, Chiara Ruberto, Laura Guidorzi, Alessandro Re, Alessandro Lo Giudice, Rodrigo Torres, Francesco Arneodo, Simi Maria Emilia Mangani, Silvia Calusi and Francesco Taccetti

Applied Sciences, 2022, 12, 6585
DOI: 10.3390/app12136585

Hierarchical Clustering of Spectral Images with Spatial Constraints for the Rapid Processing of Large and Heterogeneous Data Sets

Gilles Celeux, Serge X. Cohen, Agnès Grimaud and Pierre Gueriau

SN COMPUT. SCI., 2022, 3
DOI: 10.1007/s42979-022-01074-4

Experimental low velocity proton and 28Siq+ induced X-ray production cross sections for select elements within 24 ≤ Z ≤ 83

Masedi Carington Masekane, Ivančica Bogdanović Radović, Mandla Msimanga and Sabata Jonas Moloi

Radiation Physics and Chemistry, 2023, 213, 111208
DOI: 10.1016/j.radphyschem.2023.111208

MA-XRF datasets analysis based on convolutional neural network: A case study on religious panel paintings

Theofanis Gerodimos, Ioannis Georvasilis, Anastasios Asvestas, Georgios P. Mastrotheodoros, Aristidis Likas and Dimitrios F. Anagnostopoulos

Chemometrics and Intelligent Laboratory Systems, 2024, 250, 105138
DOI: 10.1016/j.chemolab.2024.105138