{"id":"W2381588531","doi":"10.2320/matertrans.m2016011","title":"Application of the Taguchi Method to Optimize Graphene Coatings on Copper Nanoparticles Formed Using a Solid Carbon Source","year":2016,"lang":"en","type":"article","venue":"MATERIALS TRANSACTIONS","topic":"Graphene research and applications","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"Korea Institute of Energy Technology Evaluation and Planning; National Research Foundation of Korea; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Graphene; Materials science; Raman spectroscopy; Chemical vapor deposition; Chemical engineering; Taguchi methods; Graphene nanoribbons; Nanoparticle; Nanotechnology; Scanning electron microscope; Graphene oxide paper; Carbon fibers; Graphene foam; Composite material; Composite number","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001633783,0.002242086,0.001372806,0.0014513,0.0003500191,0.0008878219,0.0008026988,0.0008390965,0.000497203],"category_scores_gemma":[0.001537833,0.0006606326,0.001012992,0.001831212,0.0003639643,0.0004565826,0.0004330604,0.0009434458,0.0003232731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005541006,"about_ca_system_score_gemma":0.0005680621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306953,"about_ca_topic_score_gemma":0.00305422,"domain_scores_codex":[0.9982542,0.0003642905,0.0002542833,0.0002844494,0.0007024154,0.0001401973],"domain_scores_gemma":[0.9992742,0.0003026368,0.0001556118,0.00005902056,0.0001756336,0.00003301292],"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":[0.00004122132,0.00005407604,0.0001362841,0.0001638121,0.00002356876,0.00002822404,0.00002967845,0.0008265129,0.9925717,0.00005735131,0.00002832151,0.006039359],"study_design_scores_gemma":[0.000009633754,0.0002263166,0.0006252432,0.000009240267,0.00003969332,0.00003654589,0.00001858284,0.005248448,0.9931573,0.00002910899,0.0005820141,0.00001790441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6142907,0.007152482,0.372315,0.0002746286,0.0002725114,0.001048218,0.0007646005,0.001031945,0.002850016],"genre_scores_gemma":[0.4977072,0.00325392,0.4956483,0.0001081151,0.0000337181,0.001124194,0.0004319479,0.0001556518,0.001536848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002242086,"threshold_uncertainty_score":0.008640349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324454590662266,"score_gpt":0.3068531473234648,"score_spread":0.2836086014168421,"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."}}