{"id":"W4210298248","doi":"10.1016/j.colsurfa.2022.128480","title":"1D magnetic resonance imaging and low-field nuclear magnetic resonance relaxometry of water-based silica nanofluids","year":2022,"lang":"en","type":"article","venue":"Colloids and Surfaces A Physicochemical and Engineering Aspects","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Nanofluid; Relaxation (psychology); Relaxometry; Nuclear magnetic resonance; Materials science; Nanoparticle; Intensity (physics); Magnetic resonance imaging; Emulsion; Ultrasound; Analytical Chemistry (journal); Chemistry; Nanotechnology; Spin echo; Chromatography; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003455883,0.000294343,0.000206368,0.0003220426,0.0002705333,0.0002216684,0.0002487219,0.0003124671,0.0006761901],"category_scores_gemma":[0.000444049,0.0002302453,0.0001109054,0.0001370083,0.0005641747,0.0004868797,0.000309126,0.0003680926,0.0001484398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002538408,"about_ca_system_score_gemma":0.0002205342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001279045,"about_ca_topic_score_gemma":0.00140082,"domain_scores_codex":[0.9998889,0.00002619109,0.000006777801,0.00003030649,0.00002522859,0.00002251938],"domain_scores_gemma":[0.9998247,0.00007946127,0.00002666624,0.00001561375,0.0000323491,0.00002129103],"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":[0.00004643446,0.000004362423,0.00007434974,0.00002986001,0.000001664822,0.00001892518,0.00002868002,0.0001187796,0.9989723,0.0001294771,0.00002693471,0.0005482798],"study_design_scores_gemma":[0.00001574455,0.00008666072,0.00182151,0.000006848622,0.000009384626,0.00008000946,0.00004712017,0.002958651,0.9939656,0.0001024625,0.0008936978,0.00001231322],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878622,0.00142784,0.008212737,0.0001665666,0.00002158853,0.000015696,0.0001708198,0.00006539484,0.002057133],"genre_scores_gemma":[0.9860006,0.0008292403,0.01114301,0.00008256432,0.00002126385,0.00003221563,0.000194469,0.00002757715,0.001669039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001279045,"threshold_uncertainty_score":0.002543211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002609450569565122,"score_gpt":0.203045051438815,"score_spread":0.2004356008692499,"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."}}