{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248266,0.0008274982,0.0007825859,0.0006677687,0.0001972785,0.0004592448,0.0004892395,0.0004501462,0.0004469322],"category_scores_gemma":[0.001026124,0.000335267,0.0005769471,0.0006397219,0.00030542,0.0002501207,0.000258563,0.0004394863,0.0002753067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004927776,"about_ca_system_score_gemma":0.0004715145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00104647,"about_ca_topic_score_gemma":0.00241613,"domain_scores_codex":[0.998919,0.0001806197,0.0001310254,0.0001585632,0.0005294556,0.00008139048],"domain_scores_gemma":[0.9995459,0.0001020858,0.0001064791,0.00003061453,0.0001897224,0.00002520873],"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":[0.00003960539,0.00002265507,0.0001182417,0.00006580405,0.000006726443,0.0000134403,0.0000111285,0.0004549751,0.995055,0.00005096969,0.00002557623,0.004135869],"study_design_scores_gemma":[0.000006327836,0.0001166027,0.000476732,0.000002668979,0.00001247633,0.00002198272,0.000008904331,0.004327342,0.9944653,0.00002209814,0.0005301272,0.000009540525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6817521,0.003591252,0.3101092,0.0001877781,0.0001382284,0.0006439632,0.0007964497,0.0005903959,0.002190634],"genre_scores_gemma":[0.7462867,0.00156553,0.2479331,0.00007996497,0.00002893092,0.0007034607,0.0006045568,0.00008543368,0.002712202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001248266,"threshold_uncertainty_score":0.006601572,"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."}}