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Your search keyword '"Maffezzoli, Niccolò"' showing total 11 results
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11 results on '"Maffezzoli, Niccolò"'

3. The seasonal change of PAHs in Svalbard surface snow

Author

Vecchiato, Marco, Barbante, Carlo, Barbaro, Elena, Burgay, François, Cairns, Warren RL., Callegaro, Alice, Cappelletti, David, Dallo, Federico, D'Amico, Marianna, Feltracco, Matteo, Gallet, Jean-Charles, Gambaro, Andrea, Larose, Catherine, Maffezzoli, Niccolò, Mazzola, Mauro, Sartorato, Ivan, Scoto, Federico, Turetta, Clara, Vardè, Massimiliano, Xie, Zhiyong, and Spolaor, Andrea

Published
2024
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4. Characterization of atmospheric total gaseous mercury at a remote high-elevation site (Col Margherita Observatory, 2543 m a.s.l.) in the Italian Alps

Author

Vardè, Massimiliano, Barbante, Carlo, Barbaro, Elena, Becherini, Francesca, Bonasoni, Paolo, Busetto, Maurizio, Calzolari, Francescopiero, Cozzi, Giulio, Cristofanelli, Paolo, Dallo, Federico, De Blasi, Fabrizio, Feltracco, Matteo, Gabrieli, Jacopo, Gambaro, Andrea, Maffezzoli, Niccolò, Morabito, Elisa, Putero, Davide, Spolaor, Andrea, and Cairns, Warren R.L.

Published
2022
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6. Detection of ice core particles via deep neural networks

Author

Maffezzoli, N, Cook, E, van der Bilt, W, Støren, E, Festi, D, Muthreich, F, Seddon, A, Burgay, F, Baccolo, G, Mygind, A, Petersen, T, Spolaor, A, Vascon, S, Pelillo, M, Ferretti, P, dos Reis, R, Simões, J, Ronen, Y, Delmonte, B, Viccaro, M, Steffensen, J, Dahl-Jensen, D, Nisancioglu, K, Barbante, C, Maffezzoli, Niccolò, Cook, Eliza, van der Bilt, Willem G. M., Støren, Eivind N., Festi, Daniela, Muthreich, Florian, Seddon, Alistair W. R., Burgay, François, Baccolo, Giovanni, Mygind, Amalie R. F., Petersen, Troels, Spolaor, Andrea, Vascon, Sebastiano, Pelillo, Marcello, Ferretti, Patrizia, dos Reis, Rafael S., Simões, Jefferson C., Ronen, Yuval, Delmonte, Barbara, Viccaro, Marco, Steffensen, Jørgen Peder, Dahl-Jensen, Dorthe, Nisancioglu, Kerim H., Barbante, Carlo, Maffezzoli, N, Cook, E, van der Bilt, W, Støren, E, Festi, D, Muthreich, F, Seddon, A, Burgay, F, Baccolo, G, Mygind, A, Petersen, T, Spolaor, A, Vascon, S, Pelillo, M, Ferretti, P, dos Reis, R, Simões, J, Ronen, Y, Delmonte, B, Viccaro, M, Steffensen, J, Dahl-Jensen, D, Nisancioglu, K, Barbante, C, Maffezzoli, Niccolò, Cook, Eliza, van der Bilt, Willem G. M., Støren, Eivind N., Festi, Daniela, Muthreich, Florian, Seddon, Alistair W. R., Burgay, François, Baccolo, Giovanni, Mygind, Amalie R. F., Petersen, Troels, Spolaor, Andrea, Vascon, Sebastiano, Pelillo, Marcello, Ferretti, Patrizia, dos Reis, Rafael S., Simões, Jefferson C., Ronen, Yuval, Delmonte, Barbara, Viccaro, Marco, Steffensen, Jørgen Peder, Dahl-Jensen, Dorthe, Nisancioglu, Kerim H., and Barbante, Carlo

Abstract

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8ĝ€ ̄% classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented.

Published
2023

7. Detection of ice core particles via deep neural networks

Author

Maffezzoli, Niccolò, primary, Cook, Eliza, additional, van der Bilt, Willem G. M., additional, Støren, Eivind N., additional, Festi, Daniela, additional, Muthreich, Florian, additional, Seddon, Alistair W. R., additional, Burgay, François, additional, Baccolo, Giovanni, additional, Mygind, Amalie R. F., additional, Petersen, Troels, additional, Spolaor, Andrea, additional, Vascon, Sebastiano, additional, Pelillo, Marcello, additional, Ferretti, Patrizia, additional, dos Reis, Rafael S., additional, Simões, Jefferson C., additional, Ronen, Yuval, additional, Delmonte, Barbara, additional, Viccaro, Marco, additional, Steffensen, Jørgen Peder, additional, Dahl-Jensen, Dorthe, additional, Nisancioglu, Kerim H., additional, and Barbante, Carlo, additional

Published
2023
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10. Bromine, Iodine and Mercury on the East Antarctic plateau: preliminary results from sampling along a traverse. 

Author

Celli, Giuditta, primary, Cairns, Warren R.L., additional, Savarino, Joel, additional, Stenni, Barbara, additional, Frezzotti, Massimo, additional, Maffezzoli, Niccolò, additional, Turetta, Clara, additional, Scarchilli, Claudio, additional, Delmonte, Barbara, additional, Traversi, Rita, additional, and Spolaor, Andrea, additional

Published
2022
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11. Detection of ice core particles via deep neural networks.

Author

Maffezzoli, Niccolò, Cook, Eliza, van der Bilt, Willem G. M., Støren, Eivind N., Festi, Daniela, Muthreich, Florian, Seddon, Alistair W. R., Burgay, François, Baccolo, Giovanni, Mygind, Amalie R. F., Petersen, Troels, Spolaor, Andrea, Vascon, Sebastiano, Pelillo, Marcello, Ferretti, Patrizia, dos Reis, Rafael S., Simões, Jefferson C., Ronen, Yuval, Delmonte, Barbara, and Viccaro, Marco

Abstract

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented. [ABSTRACT FROM AUTHOR]

Published
2022
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