{"id":"W2319859017","doi":"10.1021/la503598b","title":"Cu(II) Galvanic Reduction and Deposition onto Iron Nano- and Microparticles: Resulting Morphologies and Growth Mechanisms","year":2014,"lang":"en","type":"article","venue":"Langmuir","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; McGill University; Centre in Green Chemistry and Catalysis","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Galvanic cell; Deposition (geology); Nano-; Chemical engineering; Materials science; Nanotechnology; Reduction (mathematics); Nanoparticle; Chemistry; Metallurgy; Composite material","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":[0.00009110258,0.0001943334,0.0001214064,0.0001648665,0.0001215171,0.0002289424,0.0002254722,0.0002854553,0.0003465934],"category_scores_gemma":[0.0001926537,0.0001390416,0.0001073125,0.00007193047,0.0001774979,0.000162866,0.0001185787,0.0001852551,0.0001783531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003393859,"about_ca_system_score_gemma":0.00007473411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007560815,"about_ca_topic_score_gemma":0.001202148,"domain_scores_codex":[0.9999217,0.000006981226,0.000004768225,0.00002363189,0.00002418786,0.0000187942],"domain_scores_gemma":[0.999917,0.00002325758,0.00001978672,0.00001010353,0.00002080263,0.000008914006],"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":[0.00001907036,0.000008014268,0.0001283531,0.00003207295,0.000001979073,0.00005488168,0.00002546208,0.0001398942,0.9973355,0.0001158975,0.00004573115,0.00209317],"study_design_scores_gemma":[0.000002244511,0.0000246382,0.0009088295,0.000002198589,0.00000295654,0.00005952817,0.00001000545,0.001047511,0.9972669,0.00002856933,0.0006446837,0.000001918015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865209,0.001179223,0.009090431,0.00006949717,0.00002227903,0.00004314659,0.000116955,0.0001389981,0.002818735],"genre_scores_gemma":[0.9912047,0.0003329236,0.006876948,0.00002437461,0.000006640643,0.00001780197,0.00006940727,0.00002411495,0.00144315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007560815,"threshold_uncertainty_score":0.002462447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005219395957493054,"score_gpt":0.179176440315694,"score_spread":0.173957044358201,"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."}}