{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001831026,0.0003817509,0.00025175,0.0001673884,0.0001614988,0.0001795477,0.0002594502,0.0002774466,0.001289654],"category_scores_gemma":[0.0004351798,0.0001333028,0.0001894688,0.0001466459,0.0002515127,0.0002571675,0.0001613754,0.0003403914,0.000367938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000295789,"about_ca_system_score_gemma":0.0001102632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009258545,"about_ca_topic_score_gemma":0.001148962,"domain_scores_codex":[0.9998234,0.00001476442,0.00001397545,0.00006170273,0.00005292796,0.00003327654],"domain_scores_gemma":[0.9997634,0.00009072329,0.00004460571,0.00003685191,0.00004662879,0.00001783654],"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":[0.00004304361,0.000008952226,0.00006876723,0.00003238828,0.000003085395,0.00003917914,0.0000297077,0.00004664686,0.9986919,0.00002287959,0.00002117805,0.0009922895],"study_design_scores_gemma":[0.000001971159,0.00006004117,0.0004194952,0.000001241536,0.000003597653,0.00003273184,0.000006575829,0.0001685739,0.9988104,0.000008114382,0.0004852093,0.000002126406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9929731,0.001783817,0.002718386,0.00007121881,0.00006220494,0.0000359821,0.0001241179,0.0001303785,0.002100744],"genre_scores_gemma":[0.9924551,0.00080992,0.003724895,0.00009221911,0.0000292582,0.00002433438,0.0001882323,0.00005426123,0.002621795],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.001289654,"threshold_uncertainty_score":0.004314363,"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."}}