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Adsorption kinetics are a particularly underexplored lever to improve adsorbents, despite their significance in realistic processes. Today, the profile of 'the' optimal DAC adsorbent displaying high CO2 uptake and selectivity as well as fast sorption kinetics remains unknown. Here, we present a closed-loop approach combining high-throughput synthesis and characterisation with Bayesian optimization to understand how the material synthesis space impacts adsorbent properties and how it can ultimately be optimized to enhance adsorption-based DAC performance. To carry out this study, we chose amine-functionalized porous adsorbents as our model system. By jointly optimizing adsorption kinetics and equilibrium CO2 uptake at 400 ppm, the framework identifies high-performing materials while experimentally mapping a Pareto front. Sorbents with the highest CO2 uptakes exhibit the slowest kinetics, and vice versa. We link this trade-off to the pore structure of the materials and how the pore geometry imposes diffusion constraints. The optimization study uncovers adsorbents with order-of-magnitude higher adsorption kinetics than benchmark adsorbents at half the uptakes. Beyond this model system, our Bayesian optimization framework is a blueprint for multi-objective materials optimization in fields beyond DAC adsorbents.",{"alignment":66},{"alignment":60},{"id":68,"type":55,"data":69,"tunes":71},"0GDDtD_BlX",{"text":70},"For details: ",{"alignment":72},{"alignment":60},{"id":74,"type":55,"data":75,"tunes":76},"6HZ3yiJncO",{"text":47},{"alignment":77},{"alignment":60},{"id":79,"type":55,"data":80,"tunes":82},"RGQ0rYHtds",{"text":81},"Tristan L Spreng 1, Paul Schweng 2, David Danaci 1,3,4, Ronny Pini 1,3, and Camille Petit 1",{"alignment":83},{"alignment":60},{"id":85,"type":55,"data":86,"tunes":88},"2fdQXYr8vA",{"text":87},"\n\n1) Department of Chemical Engineering, Imperial College London, London, United Kingdom\u003Cbr>2) Institute of Materials Chemistry and Research, University of Vienna, Vienna, Austria,\u003Cbr>3) The Sargent Centre for Process Systems Engineering, Imperial College London, London, United Kingdom,\u003Cbr>4) I-X Centre for AI in Science, Imperial College London, London, United Kingdom\u003Cbr>5) National University of Singapore (Suzhou) Research Institute, Suzhou, Jiangsu, China\u003Cbr>",{"alignment":89},{"alignment":60},{"id":91,"type":55,"data":92,"tunes":94},"K0pjBVJphw",{"text":93},"ChemRxiv\u003Cbr>https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000640/v1",{"alignment":95},{"alignment":60},{"id":97,"type":98,"data":99,"tunes":102},"9oMz5gamR8","header",{"text":100,"level":101},"Contact us to learn more about this exciting article:\u003Ca href=\"https://www.chemspeed.com/contact/\">https://www.chemspeed.com/contact/\u003C/a>",3,{"alignment":103},{"alignment":60},"2.31.2","2026-03-24",[107,110],{"id":108,"news_id":44,"industry_id":109},1304,4,{"id":111,"news_id":44,"industry_id":112},1305,16,[114,117,119,122],{"id":115,"news_id":44,"workflow_id":116},1259,8,{"id":118,"news_id":44,"workflow_id":4},1260,{"id":120,"news_id":44,"workflow_id":121},1261,2,{"id":123,"news_id":44,"workflow_id":124},1262,7,{"id":4,"sort":49,"user_created":5,"date_created":126,"user_updated":127,"date_updated":128,"internal_name":129,"header_presentation":49,"cta_text":49,"cta_link":49,"preview":130,"items":131},"2025-07-03T09:39:06.000Z","396bdb39-b8ef-4bbd-ad5d-54f68f2da054","2026-04-24T06:51:26.000Z","Default Header","42627e3f-7447-4f40-8aa6-1aedfb54c7f6",[132,207,319,363,400,444],{"item":133},{"title":134,"style":135,"items":136},"Portfolio","double",[137,185],{"nova_blocks_header_submenu_items_id":138},{"title":139,"items":140},"Scalable Automation",[141,147,153,159,165,172,179],{"sort":4,"item":142},{"page_title":143,"slug":144,"menu_description":145,"menu_icon":146},"Benchtop Solutions","benchtop-crystal","Space-Saving, High-Impact","breakfast_dining",{"sort":121,"item":148},{"page_title":149,"slug":150,"menu_description":151,"menu_icon":152},"Configurable Solutions","configurable-solutions","Tailored for Your Workflow","edit_square",{"sort":101,"item":154},{"page_title":155,"slug":156,"menu_description":157,"menu_icon":158},"NMR Automation Portfolio","bruker-nmr-automation","E2E automated NMR workflows","location_automation",{"sort":109,"item":160},{"page_title":161,"slug":162,"menu_description":163,"menu_icon":164},"XRD Automation Portfolio","bruker-xrd-automation","E2E automated XRD Workflows","api",{"sort":166,"item":167},5,{"page_title":168,"slug":169,"menu_description":170,"menu_icon":171},"Automated \"Make\" and IR & Raman Workflows (Optics)","automated-ir-and-raman-workflows","Spectroscopy Automation","escalator",{"sort":173,"item":174},6,{"page_title":175,"slug":176,"menu_description":177,"menu_icon":178},"QC Packages","qc-packages","Quality Control Solutions","verified_user",{"sort":124,"item":180},{"page_title":181,"slug":182,"menu_description":183,"menu_icon":184},"Software","software-and-digitalization","Connect. 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Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and timeconsuming, making it difficult to balance accuracy and efficiency.\u003C/p>","2026-05-12",[495,497,500],{"id":496,"news_id":488,"industry_id":109},1229,{"id":498,"news_id":488,"industry_id":499},1230,10,{"id":501,"news_id":488,"industry_id":166},1231,[503,505,507,509,511],{"id":504,"news_id":488,"workflow_id":4},1187,{"id":506,"news_id":488,"workflow_id":121},1188,{"id":508,"news_id":488,"workflow_id":101},1189,{"id":510,"news_id":488,"workflow_id":124},1190,{"id":512,"news_id":488,"workflow_id":109},1191,{"id":514,"featured_news":489,"news_type":46,"title":515,"url":516,"preview":517,"target_date":518,"picture":49,"industry":519,"workflow":558},14012,"Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories","localization-inspection-and-reasoning-lira-module-for-autonomous-workflows-in-self-driving-laboratories","\u003Cp>Self-driving labs (SDLs) combine robotic automation with artificial intelligence (AI) to allow autonomous, high-throughput experimentation. 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