{"id":"W4210348027","doi":"10.1038/s41467-022-28405-6","title":"Identification of CO2 adsorption sites on MgO nanosheets by solid-state nuclear magnetic resonance spectroscopy","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"Division of Materials Research; East China University of Science and Technology; High Magnetic Field Laboratory, Chinese Academy of Sciences; Newton Fund; National Natural Science Foundation of China; National High Magnetic Field Laboratory; Royal Society; National Science Foundation","keywords":"Adsorption; Nanomaterials; Spectroscopy; Solid-state; Materials science; Solid-state nuclear magnetic resonance; Identification (biology); Nanotechnology; Solid surface; Nuclear magnetic resonance spectroscopy; Chemistry; Nuclear magnetic resonance; Chemical physics; Physical chemistry; Physics; Organic chemistry","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":[0.00006348053,0.0001699356,0.0001457387,0.0002420105,0.0001320235,0.0001745157,0.0001973846,0.000220066,0.0007948622],"category_scores_gemma":[0.0001457277,0.00009461812,0.0000830946,0.000119819,0.0001752534,0.0001406037,0.000115385,0.0001116056,0.0001227357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001537209,"about_ca_system_score_gemma":0.00005898037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006871573,"about_ca_topic_score_gemma":0.001543818,"domain_scores_codex":[0.9999371,0.000004627613,0.000003189819,0.00001559194,0.0000263417,0.00001312728],"domain_scores_gemma":[0.9999473,0.00001878438,0.00001345924,0.000005469941,0.000008255671,0.000006730106],"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":[0.00007937925,0.00001073808,0.0008071219,0.00004036976,0.000005806255,0.00003400515,0.00001395412,0.0003947965,0.9972317,0.00008319492,0.00004678019,0.001252173],"study_design_scores_gemma":[0.00001070633,0.00007559637,0.007331609,0.000004886138,0.000009716838,0.00003950989,0.00006760613,0.008078584,0.9835004,0.0000985219,0.0007760493,0.000006811294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977921,0.0002098956,0.00119108,0.00002032606,0.00001179999,0.000006593169,0.0001658561,0.0000278239,0.0005745155],"genre_scores_gemma":[0.9984832,0.00006724833,0.001117716,0.000007575134,0.000002665698,0.000004883836,0.00008802108,0.000004239857,0.0002245385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007948622,"threshold_uncertainty_score":0.002659082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220943957116344,"score_gpt":0.3064764242506344,"score_spread":0.2942669846794709,"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."}}