{"id":"W3037046545","doi":"10.3390/nano10071255","title":"Room-Temperature Reduction of Graphene Oxide in Water by Metal Chloride Hydrates: A Cleaner Approach for the Preparation of Graphene@Metal Hybrids","year":2020,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Graphene research and applications","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Centre québécois sur les matériaux fonctionnels","keywords":"Graphene; Oxide; Materials science; Metal; Nanotechnology; Graphene oxide paper; Metal ions in aqueous solution; Inorganic chemistry; Chemical engineering; Metallurgy; Chemistry","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":[0.0001633665,0.0003914034,0.0002208334,0.0002713199,0.000223256,0.0001866019,0.0003880051,0.0003548539,0.001013744],"category_scores_gemma":[0.0001457803,0.0001605183,0.0002112759,0.0001209858,0.000239131,0.0002975567,0.0002925282,0.0006127061,0.0004573212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659843,"about_ca_system_score_gemma":0.0001032738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003006353,"about_ca_topic_score_gemma":0.0009570683,"domain_scores_codex":[0.9998512,0.00002501857,0.00001064705,0.00004180399,0.00004778967,0.00002357492],"domain_scores_gemma":[0.9999582,0.0000107909,0.00001005566,0.000008381308,0.00000660061,0.0000061302],"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":[0.000009674919,0.000005387517,0.00002686117,0.00003035574,0.000003308746,0.00002440898,0.000017904,0.00005476053,0.9989116,0.0001065459,0.00002590237,0.0007833382],"study_design_scores_gemma":[0.00000247927,0.0000311968,0.0001033988,0.000001170881,0.000002863176,0.00002661214,0.000006697924,0.000249065,0.9986711,0.00003294595,0.0008689235,0.000003500344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404156,0.002256898,0.05203843,0.0003403181,0.00009286671,0.000116835,0.0003414869,0.0005185814,0.00387907],"genre_scores_gemma":[0.9806659,0.0004665266,0.01658957,0.00008647761,0.00001603622,0.00006088174,0.0001829499,0.00005551281,0.00187618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001013744,"threshold_uncertainty_score":0.003391325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089556838721249,"score_gpt":0.274888182297289,"score_spread":0.2539926139100764,"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."}}