{"id":"W3208240611","doi":"10.3389/fclim.2021.722447","title":"Synthetic Fluid Inclusions XXIV. In situ Monitoring of the Carbonation of Olivine Under Conditions Relevant to Carbon Capture and Storage Using Synthetic Fluid Inclusion Micro-Reactors: Determination of Reaction Rates","year":2021,"lang":"en","type":"article","venue":"Frontiers in Climate","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Eötvös Loránd Tudományegyetem; Emberi Eroforrások Minisztériuma; National Science Foundation","keywords":"Carbonation; Magnesite; Olivine; Raman spectroscopy; Nucleation; Mineralization (soil science); Fluid inclusions; Mineralogy; Analytical Chemistry (journal); Reaction rate; Seawater; Brucite; Chemical engineering; Chemistry; Materials science; Hydrothermal circulation; Geology; Metallurgy; Catalysis; Magnesium; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002124834,0.0003483652,0.0002560731,0.0001740582,0.00015924,0.000297468,0.0002744437,0.0002812905,0.0005575821],"category_scores_gemma":[0.0003131124,0.0001454571,0.0002411601,0.0001546467,0.0003220291,0.0002519389,0.0002347207,0.0002710236,0.0001312724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003033006,"about_ca_system_score_gemma":0.0001356142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001028126,"about_ca_topic_score_gemma":0.001308042,"domain_scores_codex":[0.9997731,0.00003110727,0.00001987534,0.00005760869,0.00008714903,0.00003121592],"domain_scores_gemma":[0.9998115,0.00005504542,0.00005691279,0.00001793886,0.00004075699,0.00001789348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003473589,0.000006402824,0.0002715421,0.00002557434,0.000002446492,0.00001742348,0.00001484754,0.0001270484,0.9988366,0.00003076534,0.000005463312,0.0006271591],"study_design_scores_gemma":[0.000001445268,0.00004914679,0.000733838,0.000001431673,0.000002189797,0.00001966698,0.000007831917,0.001208523,0.9977539,0.00001106348,0.0002084149,0.000002583096],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937926,0.0002228153,0.005167995,0.00001643158,0.000008759483,0.00002524891,0.000229297,0.0000733419,0.0004635057],"genre_scores_gemma":[0.9896224,0.0001609793,0.009119501,0.00000690661,0.000003656879,0.00004046215,0.0002829279,0.00001575907,0.0007474941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001028126,"threshold_uncertainty_score":0.002200603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293585887686675,"score_gpt":0.2729468105532382,"score_spread":0.2600109516763714,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}