{"id":"W4214911218","doi":"10.1007/s00216-022-03906-x","title":"Surface chemistry of metal oxide nanoparticles: NMR and TGA quantification","year":2022,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Thermogravimetric analysis; Surface modification; Chemistry; Nanomaterials; Stearic acid; Nanoparticle; Chemical engineering; Nuclear chemistry; Inorganic chemistry; Nanotechnology; Materials science; Organic chemistry; Physical chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002079891,0.0001514503,0.0002645891,0.00001025769,0.0001925909,0.00003079876,0.0001405699,0.00003823156,0.001555714],"category_scores_gemma":[0.00001747175,0.0001410335,0.00009693981,0.0002543418,0.0003324422,0.00004638438,0.0001732277,0.000236161,0.000004246349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000171496,"about_ca_system_score_gemma":0.00004033914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000692944,"about_ca_topic_score_gemma":2.747921e-7,"domain_scores_codex":[0.9988375,0.00002232776,0.0003221726,0.0003685429,0.0002159877,0.0002334563],"domain_scores_gemma":[0.9993263,0.0001182675,0.00008976727,0.0002422613,0.00004027339,0.0001831642],"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":[0.00004815358,0.0003939161,0.01689991,0.00007614461,0.000168407,0.000001896803,0.00002563162,0.00005080718,0.954109,0.02763522,0.0002107519,0.0003802313],"study_design_scores_gemma":[0.0005454454,0.00003622283,0.002318176,0.00001143371,0.0003964293,0.00001291953,0.001042762,0.02426236,0.9602995,0.007533967,0.003164631,0.0003761922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932655,0.0001368158,0.0003773229,0.0008413167,0.000004751324,0.00006304714,0.00009370039,0.00001848553,0.005199026],"genre_scores_gemma":[0.9981097,0.000007744063,0.0001779089,0.00002723884,0.00004730422,0.00001277836,0.00004714572,0.000009547663,0.001560659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02421155,"threshold_uncertainty_score":0.999357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352826421243673,"score_gpt":0.2917408341332581,"score_spread":0.2782125699208213,"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."}}