{"id":"W2323479698","doi":"10.1016/j.dib.2016.03.104","title":"Washing effect on superparamagnetic iron oxide nanoparticles","year":2016,"lang":"en","type":"article","venue":"Data in Brief","topic":"Iron oxide chemistry and applications","field":"Energy","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec; Fonds Québécois de la Recherche sur la Nature et les Technologies; Fonds De La Recherche Scientifique - FNRS; European Cooperation in Science and Technology; European Regional Development Fund; Appalachian Regional Commission","keywords":"Nanotechnology; Nanoparticle; Superparamagnetism; Nanoscopic scale; Materials science; Iron oxide nanoparticles; Iron oxide; Chemical engineering; Metallurgy; Engineering; Magnetization; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838572,0.0001088794,0.0001113311,0.00002253997,0.00004924303,0.00002706766,0.0005169056,0.00005740059,0.0002183128],"category_scores_gemma":[0.0003201949,0.00007847357,0.00001663345,0.0001016937,0.0000613478,0.0001797782,0.0001612029,0.0000768495,0.0004635324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003676632,"about_ca_system_score_gemma":0.000009789049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003501635,"about_ca_topic_score_gemma":0.0001218487,"domain_scores_codex":[0.9990769,0.00006158707,0.0001650496,0.0003482256,0.0001129306,0.0002353248],"domain_scores_gemma":[0.9983163,0.0004407526,0.00002701366,0.001151041,0.000006143102,0.00005876959],"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":[0.00004015559,0.00005592227,0.001238032,0.00001753598,0.000002675867,0.000006399663,0.00001646117,0.00002921524,0.9688132,0.006004269,0.003178318,0.02059785],"study_design_scores_gemma":[0.0008071899,0.00005875266,0.01305419,0.00009341589,0.00001022412,0.000006201182,0.00001052747,0.00009176968,0.8283558,0.0003994132,0.1569205,0.0001919695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915165,0.0000356573,0.00001752912,0.0007362829,0.0000281458,0.0000822243,0.00009913622,0.00005408291,0.007430478],"genre_scores_gemma":[0.998718,0.00001314379,0.0001328439,0.0001827566,0.00006050127,0.00003748442,0.0001821721,0.00001575405,0.0006573478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1537422,"threshold_uncertainty_score":0.5957926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036012410023867,"score_gpt":0.2631674475101766,"score_spread":0.2428073234099379,"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."}}