{"id":"W2508405430","doi":"10.1039/c6tb01162d","title":"Laser-synthesized ligand-free Au nanoparticles for contrast agent applications in computed tomography and magnetic resonance imaging","year":2016,"lang":"en","type":"article","venue":"Journal of Materials Chemistry B","topic":"Laser-Ablation Synthesis of Nanoparticles","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier universitaire de Québec; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Centre québécois sur les matériaux fonctionnels","keywords":"Dispersity; Nanoparticle; Materials science; Colloid; PEG ratio; Laser ablation synthesis in solution; Chelation; Aqueous solution; MRI contrast agent; Nanotechnology; Nuclear magnetic resonance; Chemistry; Laser; Polymer chemistry; Organic chemistry; Laser power scaling","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":[0.0002264471,0.0004195827,0.0002515715,0.0002465364,0.0002112753,0.0002045186,0.0003236182,0.0005588042,0.001157389],"category_scores_gemma":[0.0002471702,0.0001704066,0.0001800483,0.0001368554,0.0002436632,0.0002540781,0.0001849914,0.0003796391,0.0004055158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006125278,"about_ca_system_score_gemma":0.00037071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009726773,"about_ca_topic_score_gemma":0.00199062,"domain_scores_codex":[0.9999107,0.00001281849,0.000007055483,0.00002996521,0.0000275688,0.00001192935],"domain_scores_gemma":[0.9999051,0.00002334067,0.00002224694,0.000009385943,0.00002461087,0.00001531127],"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":[0.00001661595,0.000009862862,0.00002139296,0.00003966392,0.000001911402,0.00002283852,0.00001331961,0.0001025255,0.9976066,0.0001093024,0.00003888888,0.002017038],"study_design_scores_gemma":[0.00000516599,0.00004988226,0.0001295596,0.00000202388,0.000004527791,0.00006851201,0.000004740751,0.001211347,0.9971836,0.00003300357,0.001303545,0.000004115561],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8745211,0.004341775,0.1115913,0.0004091082,0.0001331559,0.0002827197,0.0004441504,0.0008523776,0.007424285],"genre_scores_gemma":[0.9025925,0.001122492,0.08488731,0.00018453,0.00002007493,0.0001886608,0.0004166189,0.0001225671,0.01046525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001157389,"threshold_uncertainty_score":0.004444242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007604771375273833,"score_gpt":0.2032049528562012,"score_spread":0.1956001814809274,"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."}}