{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107424,0.00009058259,0.00009394081,0.00003047705,0.00008837406,0.00003483402,0.0000237016,0.00006337207,0.000004447762],"category_scores_gemma":[0.00002831689,0.00008727435,0.000006801067,0.00002686574,0.00003724796,0.0001338806,0.00003897517,0.00003974299,0.000004516168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003023215,"about_ca_system_score_gemma":8.632542e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002982288,"about_ca_topic_score_gemma":0.000004555233,"domain_scores_codex":[0.9995131,0.00002911225,0.0001311014,0.0001414669,0.00006015175,0.0001250596],"domain_scores_gemma":[0.9998432,0.00002582127,0.00003190801,0.00006261881,0.000004309169,0.00003208275],"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":[0.000006397225,0.000003426501,0.0008187341,0.00005664069,0.000003850285,0.000001012548,0.000428884,0.00001178159,0.9977124,0.0001277491,0.0001144646,0.0007147035],"study_design_scores_gemma":[0.0004538465,0.00009415857,0.04227325,0.00003156645,0.00002030533,0.00007864093,0.000228538,0.001125548,0.9547024,0.0007417711,0.00008374303,0.0001661644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984075,0.0001820231,0.0006663033,0.0003560845,0.0000816501,0.00009130848,0.000003610638,0.0001392721,0.00007221279],"genre_scores_gemma":[0.995572,0.000143952,0.004056732,0.0001090819,0.00006515665,0.00001050904,0.000009621626,0.00001713514,0.00001584256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04300989,"threshold_uncertainty_score":0.3558944,"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."}}