[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ background_color=”#072c72″ border_color_all=”#3255c9″ text_orientation=”right” background_layout=”dark” custom_padding=”20px|15px|15px|” z_index_tablet=”500″]<\/p>\n
2024 <\/em><\/p>\n Strategic Projects<\/em><\/p>\n @Earth Sciences\/Geosciences<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”3.22.1″ text_orientation=”right” z_index_tablet=”500″]<\/p>\n #nanoparticles<\/p>\n #spICP-ToF-MS<\/p>\n #clustering<\/p>\n #segmentation<\/p>\n \u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n The detection and characterization of inorganic nanoparticles (NPs) in natural matrices has relied primarily on single-particle ICP-MS (spICP-MS). In practice, the continuous introduction of an aqueous environmental sample produces element-specific high-frequency time series consisting of background noise randomly interspersed with peaks corresponding to individual NPs. With new generation ICP-ToF-MS, the large amount of data generated poses a challenge for traditional methods to effectively perform NP peak detection. This study proposes a machine learning (ML)-based method for automatic segmentation of spICP-ToF-MS time series using simulated single-channel scenarios with varying parameters to demonstrate its feasibility and robustness.<\/p>\n <\/p>\n <\/p>\n Jiachen Zhang<\/strong><\/p>\n Pierre Emmanuel <\/strong>Peyneau <\/strong><\/p>\n L\u00e9onard Seydoux<\/strong><\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ admin_label=”Row” locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.22.1″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”39″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″ locked=”off”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 2024 Strategic Projects@Earth Sciences\/Geosciences #nanoparticles#spICP-ToF-MS#clustering#segmentation\u00a0 Project Summary The detection and characterization of inorganic nanoparticles (NPs) in natural matrices has relied primarily on single-particle ICP-MS (spICP-MS). In practice, the continuous introduction of an aqueous environmental sample produces element-specific high-frequency time series consisting of background noise randomly interspersed with peaks corresponding to individual NPs. With new generation… Lire la suite Automated segmentation and clustering of spICP-ToF-MS time series<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[51,1,39,29],"tags":[],"class_list":["post-1512","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-51","category-diip","category-earth-sciences-geosciences","category-masters-internship"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1512","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=1512"}],"version-history":[{"count":4,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1512\/revisions"}],"predecessor-version":[{"id":2408,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1512\/revisions\/2408"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2263"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1512"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1512"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Project Summary<\/strong><\/h3>\n
Micka\u00ebl Tharaud
<\/strong>tharaud@ipgp.fr<\/h3>\n\n
(specialist in analytical chemistry, with a focus on the detection and characterization of inorganic nanoparticles in aquatic systems thanks to plasma source mass spectrometry)<\/li>\n<\/ul>\nProjects in the same discipline
<\/span><\/h2>\n