[vc_row full_width=”stretch_row” bg_type=”image” parallax_style=”vcpb-default” bg_image_new=”id^2273|url^https:\/\/wp.icmm.csic.es\/greener\/wp-content\/uploads\/sites\/55\/2021\/11\/fondo-gris.png|caption^null|alt^null|title^fondo-gris|description^null” bg_image_repeat=”no-repeat” bg_image_size=”contain” bg_override=”ex-full” css=”.vc_custom_1638273238459{padding-top: 70px !important;padding-bottom: 90px !important;}”][vc_column css=”.vc_custom_1635954687684{padding-top: 25px !important;}”][vc_row_inner][vc_column_inner width=”2\/3″][vc_column_text]<\/p>\n
The \u201cEnergy storage, harvesting, and conversion\u201d (GREENER) group combines innovative analytical synthesis routes and advanced characterization methodology of innovative energy materials with tailored architecture and properties. Our research is about a commitment to expanding fundamental and applied scientific knowledge, driving discoveries, fostering innovation, and technology development with remarkable beneficial economic and societal impact.<\/span><\/p>\n Progress towards developing sustainable energy solutions is becoming increasingly critical as concerned with the increase of carbon emissions released by burning fossil fuels, which has led to a rise in greenhouse gases, contributing to human-induced climate change. Our group is looking for novel and performant materials which can pave the way for efficient, sustainable energy conversion, harvesting, and storage devices, mainly including but not exclusively fuel cells, batteries, super-capacitors, and solar cells.<\/p>\n <\/p>\n GREENER group bases its studies on the fruitful synergy between conventional in-house laboratory techniques, the use of advanced Artificial Intelligence (AI) algorithms to optimize properties of materials, and the utilization of major scientific user facilities, including powerful X-ray light and neutron sources, as well as the access to powerful computer centres.<\/span><\/p>\n Effectively, conventional laboratory techniques like photoelectron spectroscopy (XPS and UPS), Raman, X-ray diffraction, and electron microscopies, together with machine learning and generative AI approaches, provide helpful information that reinforces measurements of classical transport properties of devices and electrochemistry characterization of batteries. This panoply of classical and cutting-edge techniques like X-Ray nano-scopes, advanced diffraction methods, and Machine Learning methods allows discovering disruptive energy materials with optimal properties.<\/span><\/p>\n [\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/3″ css=”.vc_custom_1637340667510{padding-top: 75px !important;}”]World-Cass Scientific tools<\/h2>\n