{"id":"W2980235446","doi":"10.3390/nano9101445","title":"Reductive and Coordinative Effects of Hydrazine in Structural Transformations of Copper Hydroxide Nanoparticles","year":2019,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Copper-based nanomaterials and applications","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canada Foundation for Innovation","keywords":"Nanochemistry; Materials science; Copper; Nanomaterials; Nanoparticle; Reducing agent; Hydroxide; Oxide; Hydrazine (antidepressant); Catalysis; Chemical engineering; Copper oxide; Amorphous solid; Nanotechnology; Inorganic chemistry; Chemistry; Organic chemistry; Metallurgy","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":[0.0003239092,0.0001656089,0.0005303299,0.0001289404,0.00004398543,0.00002836514,0.0001563319,0.00007659931,0.0003170319],"category_scores_gemma":[0.0000600304,0.0001403732,0.00004172986,0.000204942,0.0001816711,0.0003046804,0.00005297524,0.00002563456,0.0000289312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003184424,"about_ca_system_score_gemma":0.00005309629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001325383,"about_ca_topic_score_gemma":0.00001050794,"domain_scores_codex":[0.9985587,0.0001458506,0.0006500301,0.0002566336,0.0001599965,0.0002287824],"domain_scores_gemma":[0.9991487,0.0002009079,0.0002768129,0.0002413823,0.00008156358,0.0000506912],"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":[0.00009881549,0.00005461276,0.0006841263,0.0002912216,0.000007386982,7.697568e-7,0.001011435,0.0000391279,0.9957343,0.001896207,0.00001452834,0.0001674112],"study_design_scores_gemma":[0.001096291,0.0001819469,0.01416354,0.0001350924,0.00002020385,0.000006374433,0.000111599,0.00005239476,0.9830963,0.0009607055,0.00003238391,0.0001431633],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981295,0.0001540969,0.000006994871,0.00008963203,0.0003285559,0.0008746816,0.0001776739,0.00002780817,0.0002110284],"genre_scores_gemma":[0.9992915,0.0000235079,0.0005140291,0.00001254443,0.00002231902,0.00008270307,0.00001302153,0.00001444277,0.00002594321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01347942,"threshold_uncertainty_score":0.5724252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004587732671652688,"score_gpt":0.2350869396382035,"score_spread":0.2304992069665508,"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."}}