{"id":"W2315632561","doi":"10.1002/cjce.22478","title":"Synthesis and characterization of copper succinate and copper oxide nanoparticles by electrochemical treatment: Optimization by Taguchi robust analysis","year":2016,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Copper-based nanomaterials and applications","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copper; Taguchi methods; Scanning electron microscope; Fourier transform infrared spectroscopy; Calcination; Copper oxide; Materials science; Nanoparticle; Electrochemistry; Nuclear chemistry; Oxide; Chemical engineering; Analytical Chemistry (journal); Metallurgy; Chemistry; Composite material; Nanotechnology; Electrode; Chromatography; Organic chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001723248,0.000106756,0.0002318807,0.00007565566,0.00005405807,0.00005072835,0.0000997884,0.00005230974,0.00006302615],"category_scores_gemma":[0.00008396113,0.00006615985,0.00004058122,0.0001547583,0.00008054114,0.0001102952,0.000008570731,0.00003019064,5.826803e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009828532,"about_ca_system_score_gemma":0.0000622463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002501472,"about_ca_topic_score_gemma":0.00001931334,"domain_scores_codex":[0.9993245,0.00002275986,0.0002758817,0.0001105865,0.00008392477,0.0001823313],"domain_scores_gemma":[0.9993749,0.0001277734,0.000131251,0.00009741721,0.00005918982,0.0002094189],"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":[0.00001656405,0.000008842254,0.0001912963,0.000006383118,0.00003989656,4.927442e-7,0.00003109954,0.0004456328,0.9990304,0.00002630847,0.00004674146,0.0001563982],"study_design_scores_gemma":[0.0001906564,0.00002123939,0.0001356501,0.00003033812,0.0001653989,0.00001380051,0.000002796184,0.001892217,0.9973755,0.00000436602,0.00008657562,0.00008146694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971686,0.0002075781,0.00139805,0.001039352,0.00001500131,0.00005674156,0.0001056677,0.000006722129,0.000002277365],"genre_scores_gemma":[0.9994557,0.00005685001,0.0004169313,0.00001669715,0.00001854099,0.000007886289,0.000006938375,0.00001126087,0.000009116543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002287169,"threshold_uncertainty_score":0.2697921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004967141580831653,"score_gpt":0.1734869599234478,"score_spread":0.1685198183426162,"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."}}