Francesco d’Errico

3.9k total citations
232 papers, 2.9k citations indexed

About

Francesco d’Errico is a scholar working on Radiation, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Francesco d’Errico has authored 232 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 161 papers in Radiation, 106 papers in Pulmonary and Respiratory Medicine and 63 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Francesco d’Errico's work include Radiation Therapy and Dosimetry (101 papers), Nuclear Physics and Applications (81 papers) and Radiation Detection and Scintillator Technologies (72 papers). Francesco d’Errico is often cited by papers focused on Radiation Therapy and Dosimetry (101 papers), Nuclear Physics and Applications (81 papers) and Radiation Detection and Scintillator Technologies (72 papers). Francesco d’Errico collaborates with scholars based in Italy, United States and Brazil. Francesco d’Errico's co-authors include Ravinder Nath, S.O. Souza, Maurizio Marrale, M. Luszik-Bhadra, M. Matzke, W.G. Alberts, L. Tana, Riccardo Ciolini, M. Moscovitch and Angela Di Fulvio and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Francesco d’Errico

222 papers receiving 2.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Francesco d’Errico Italy 28 2.0k 1.4k 765 416 315 232 2.9k
F. Vanhavere Belgium 31 2.0k 1.0× 1.7k 1.2× 1.9k 2.5× 480 1.2× 598 1.9× 225 3.7k
P. Olko Poland 29 2.2k 1.1× 1.5k 1.1× 517 0.7× 1.0k 2.5× 176 0.6× 225 3.1k
Alex F. Bielajew United States 28 2.2k 1.1× 1.6k 1.1× 1.2k 1.6× 276 0.7× 512 1.6× 65 2.6k
M. Pelliccioni Italy 21 1.1k 0.5× 1.3k 0.9× 677 0.9× 442 1.1× 124 0.4× 86 2.1k
Takuya Furuta Japan 18 1.5k 0.7× 880 0.6× 597 0.8× 556 1.3× 124 0.4× 60 2.4k
Joseph Perl United States 21 1.7k 0.8× 1.8k 1.3× 603 0.8× 164 0.4× 184 0.6× 56 2.1k
Tatsuhiko Ogawa Japan 13 1.4k 0.7× 813 0.6× 456 0.6× 545 1.3× 105 0.3× 61 2.1k
Lembit Sihver Sweden 27 2.4k 1.2× 2.2k 1.6× 778 1.0× 812 2.0× 227 0.7× 162 4.3k
F.H. Attix United States 24 2.5k 1.2× 1.6k 1.1× 1.1k 1.4× 750 1.8× 432 1.4× 88 3.3k
Norihiro Matsuda Japan 22 1.6k 0.8× 953 0.7× 545 0.7× 632 1.5× 120 0.4× 74 2.7k

Countries citing papers authored by Francesco d’Errico

Since Specialization
Citations

This map shows the geographic impact of Francesco d’Errico's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Francesco d’Errico with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco d’Errico more than expected).

Fields of papers citing papers by Francesco d’Errico

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Francesco d’Errico. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Francesco d’Errico. The network helps show where Francesco d’Errico may publish in the future.

Co-authorship network of co-authors of Francesco d’Errico

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco d’Errico. A scholar is included among the top collaborators of Francesco d’Errico based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesco d’Errico. Francesco d’Errico is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cottone, Grazia, Maria Cristina D’Oca, A. Bartolotta, et al.. (2024). Deep learning approach for diffusion correction in Fricke hydrogel dosimeters. Radiation Measurements. 175. 107171–107171. 2 indexed citations
2.
Cottone, Grazia, Maria Cristina D’Oca, A. Bartolotta, et al.. (2024). Diffusion Correction in Fricke Hydrogel Dosimeters: A Deep Learning Approach with 2D and 3D Physics-Informed Neural Network Models. Gels. 10(9). 565–565. 2 indexed citations
3.
Ciolini, Riccardo, et al.. (2024). Calibration HPGe detector using IAEA-U source for CBRNe. The European Physical Journal Plus. 139(7). 1 indexed citations
4.
Ciolini, Riccardo, et al.. (2023). Resource Constrained Electronics and Signal Processing for UAV Radiation Sensors. SHILAP Revista de lepidopterología. 288. 10019–10019.
5.
6.
Ciolini, Riccardo, et al.. (2023). A SENSORISED LORA-BASED NETWORK FOR THE MONITORING AND IDENTIFICATION OF RADIOACTIVE WASTE DRUMS. The Proceedings of the International Conference on Nuclear Engineering (ICONE). 2023.30(0). 1170–1170. 1 indexed citations
7.
Souza, S.O., et al.. (2022). Effects of Ionizing Radiation on Flora Ten Years after the Fukushima Dai-ichi Disaster. Plants. 11(2). 222–222. 14 indexed citations
8.
Souza, S.O., et al.. (2021). Effectiveness of a UVC air disinfection system for the HVAC of an ICU. The European Physical Journal Plus. 137(1). 37–37. 22 indexed citations
9.
Martellucci, Luca, et al.. (2021). Numerical Fluid Dynamics Simulation for Drones’ Chemical Detection. Drones. 5(3). 69–69. 15 indexed citations
10.
d’Errico, Francesco, Daniele Di Giovanni, Riccardo Rossi, et al.. (2020). Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations. Sensors. 20(6). 1770–1770. 22 indexed citations
11.
Souza, S.O., et al.. (2020). Monte Carlo simulations of PVC films loaded with microparticles of MgB4O7 to detect albedo neutrons. Radiation Measurements. 134. 106322–106322. 3 indexed citations
12.
Lazzeri, Luigi, et al.. (2019). Dosimetric and chemical characteristics of Fricke gels based on PVA matrices cross-linked with glutaraldehyde. Physics in Medicine and Biology. 64(8). 85015–85015. 22 indexed citations
13.
Lazzeri, Luigi, et al.. (2017). Fricke gel dosimeters with low-diffusion and high-sensitivity based on a chemically cross-linked PVA matrix. Radiation Measurements. 106. 618–621. 57 indexed citations
14.
Bordy, J.M., J. Daures, I. Clairand, et al.. (2008). Evaluation of the calibration procedure of active personal dosemeters for interventional radiology. Radiation Protection Dosimetry. 131(1). 87–92. 9 indexed citations
15.
Roa, D, Haijun Song, Ning J. Yue, Francesco d’Errico, & Ravinder Nath. (2004). Dosimetric characteristics of the Novoste Beta‐Cath source trains at submillimeter distances. Medical Physics. 31(5). 1269–1276. 9 indexed citations
16.
Bartlett, D. T., Peter Beck, J. F. Bottollier-Depois, et al.. (2002). Investigation of radiation doses at aircraft altitudes during a complete solar cycle. ESASP. 477. 525–528. 15 indexed citations
17.
d’Errico, Francesco, et al.. (2001). Electronic personal neutron dosimetry with superheated drop detectors. Radiation Protection Dosimetry. 261–264. 3 indexed citations
18.
d’Errico, Francesco, et al.. (2001). Depth Dose-Equivalent and Effective Energies of Photoneutrons Produced by 6-18 MeV X-Ray Beams for Radiotherapy. Health Physics. 4–11. 1 indexed citations
19.
Lamba, Michael, et al.. (1998). Magnetic resonance imaging of microbubbles in a superheated emulsion chamber for brachytherapy dosimetry. Medical Physics. 25(12). 2316–2325. 9 indexed citations
20.
d’Errico, Francesco. (1994). Superheated drop (bubble) detectors and their compliance with ICRP 60. Radiation Protection Dosimetry. 357–360. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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