[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″][et_pb_row _builder_version=”3.22.1″ border_color_all=”#3255c9″ border_style_all=”groove”][et_pb_column type=”1_2″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ min_height=”11px” custom_margin=”||-25px|||” custom_padding=”||0px|||”]<\/p>\n
Jean-Fran\u00e7ois Masson<\/span><\/p>\n May 4, 2022, at 4 PM<\/span><\/p>\n Online (Zoom)<\/span><\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.22.1″][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”44px|||||” custom_margin=”80px||80px” _builder_version=”3.22.1″][et_pb_column type=”2_3″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ custom_margin=”-51px|||||” custom_padding=”0px|||||”]<\/p>\n <\/p>\n SERS and Raman spectroscopy yields large data sets with information-rich spectra. Classical linear methods are limited, especially for SERS spectra of single molecules, where the spectra are highly dependent on the orientation of molecules on surfaces and for large data sets. Methods from data sciences are increasingly used to classify spectra into categories and predict SERS spectra for new data based on trained algorithms. For example, we recently introduced the concept of SERS optophysiology, which combines a SERS nanosensor on the tip of a pulled fiber to provide spatially and temporally specific molecular information near or inside biological material. To accomplish this, a SERS nanofiber decorated with a dense and well dispersed array of Au NP has been developed for the measurement of neurotransmitters and other metabolites in proximity of cells. The nanosensors are thus highly compatible with current physiology experiments also relying on similar nanosensors based on electrochemistry and electrophysiology. Specifically, we will show that the SERS optophysiology nanosensor can measure a panel of metabolites near cells in a single experiment. The SERS spectra of these neurotransmitters were identified with a barcoding data processing method, processed with TensorFlow using a convolutional neural network architecture.<\/p>\n This machine-learning driven data processing significantly improved the positive assignment rates for a series of metabolites and allows for complex measurements of the cell\u2019s biochemistry. In addition to these untargeted SERS nanosensors, we also designed molecularly specific sensors to measure pH, H2O2 and heavy metals inside cells using the same nanosensor architecture. This suite of SERS nanosensor will open the door to survey molecular changes in proximity of healthy and diseased cells.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.22.1″][et_pb_video_slider _builder_version=”3.22.1″][et_pb_video_slider_item src=”https:\/\/www.youtube.com\/watch?v=J3t07gvGm2o” src_webm=”https:\/\/www.youtube.com\/watch?v=J3t07gvGm2o” _builder_version=”3.22.1″ show_image_overlay=”off”][\/et_pb_video_slider_item][\/et_pb_video_slider][et_pb_text admin_label=”Text” _builder_version=”3.22.1″]<\/p>\n Click the image below to see slides<\/em><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-content\/uploads\/sites\/27\/2024\/10\/Screenshot-2024-10-23-123223.png” url=”https:\/\/drive.google.com\/file\/d\/15ql2oG5OPDNR_r-t39s-2x-sEodwP24l\/view” _builder_version=”3.22.1″ transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.22.1″][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_3″ _builder_version=”3.22.1″][et_pb_image src=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-content\/uploads\/sites\/27\/2022\/02\/Photo-JF-Masson.jpeg” align=”center” _builder_version=”3.22.1″ custom_margin=”20px|125px||||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ custom_padding=”0px|||||”]<\/p>\n Jean-Fran\u00e7ois Masson is full professor of Chemistry at the Universit\u00e9 de Montr\u00e9al. He studied chemistry at the Universit\u00e9 de Sherbrooke (BSc), Arizona State University (PhD) and Georgia Tech (postdoc). His laboratory develops new plasmonic materials, instruments, and surface chemistry for the detection of broad range of molecules directly in crude samples, which are then translated to functional sensors for a series of biological, environmental and industrial applications. He has published more than 125 research articles and his research has led to filing more than 10 patents on various instrumental, materials or surface chemistry innovations for biosensing. He is an Associate Editor for ACS Sensors. In 2015, he co-founded Affinit\u00e9 Instruments, a Canadian start-up company commercializing surface plasmon resonance (SPR) instrumentation. Jean-Francois received several awards including the Tomas Hirschfeld award (2005), a NSERC discovery accelerator (2011), the Fred Beamish award (2013) and the McBryde Medal (2019) of the Canadian Society for Chemistry, and an Alexander von Humboldt fellowship, Germany (2013-2014) for research at the Max-Planck Institute. In 2017, he was named Fellow of the Royal Society of Chemistry \u2013 UK and more recently, he was named in the 2018 power list of the top 40 under 40 analytical scientists and the 2019 power list of the top 100 most influential analytical scientists from The Analytical Scientist \u2013 UK.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.0.47″][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″]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”65″ 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″][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Jean-Fran\u00e7ois Masson May 4, 2022, at 4 PM Online (Zoom) Abstract SERS and Raman spectroscopy yields large data sets with information-rich spectra. Classical linear methods are limited, especially for SERS spectra of single molecules, where the spectra are highly dependent on the orientation of molecules on surfaces and for large data sets. Methods from… Lire la suite Jean-Fran\u00e7ois Masson – Surface enhanced Raman scattering (SERS) sensors: combining machine learning and nanosciences<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2808,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[55,1,65],"tags":[],"class_list":["post-2664","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-55","category-diip","category-distinguished-lectures"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2664","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=2664"}],"version-history":[{"count":9,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2664\/revisions"}],"predecessor-version":[{"id":2809,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2664\/revisions\/2809"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2808"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=2664"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=2664"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=2664"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Abstract<\/strong><\/h3>\n
Prof. Jean-Fran\u00e7ois Masson
<\/strong>(Universit\u00e9 de Montr\u00e9al)
<\/strong>
<\/strong><\/h3>\nOther distinguished lectures
<\/span><\/h2>\n