{"id":"W2598822387","doi":"10.1016/j.jcis.2017.03.095","title":"Strategies for liquid-liquid extraction of oxide particles for applications in supercapacitor electrodes and thin films","year":2017,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Supercapacitor Materials and Fabrication","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Chemical engineering; Supercapacitor; Carbon nanotube; Extraction (chemistry); Particle (ecology); Electrode; Nanotechnology; Oxide; Particle size; Capacitance; Chromatography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002900002,0.0004419332,0.0003260439,0.0003256214,0.0003725681,0.0005455004,0.0004307615,0.0004350352,0.001561716],"category_scores_gemma":[0.000265048,0.0002597715,0.0003388972,0.0001920769,0.0002783665,0.0006757578,0.0005451491,0.0007084528,0.0007881648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002773183,"about_ca_system_score_gemma":0.0003329834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005144942,"about_ca_topic_score_gemma":0.001833873,"domain_scores_codex":[0.9998739,0.00001309604,0.0000130062,0.00002909327,0.00004468864,0.00002621603],"domain_scores_gemma":[0.9999108,0.00003599809,0.00001535075,0.000008983057,0.00001865511,0.00001014596],"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":[0.0000322232,0.00003361593,0.0000890994,0.0001069346,0.000007162516,0.00009293454,0.00003549924,0.0001298465,0.9923751,0.0005109612,0.0001253563,0.006461256],"study_design_scores_gemma":[0.00001028274,0.00007178212,0.0001604229,0.000008281567,0.000007587884,0.00003782905,0.00002395327,0.0009868753,0.9962751,0.0001567492,0.002256246,0.000004915992],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7737965,0.007555655,0.2017755,0.0008658653,0.0002583095,0.0005766778,0.0005263375,0.0004209484,0.01422414],"genre_scores_gemma":[0.9132412,0.003446826,0.07196225,0.0003406748,0.00005652678,0.0003203042,0.000506007,0.00009710262,0.01002914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001561716,"threshold_uncertainty_score":0.005224407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02481133319359963,"score_gpt":0.3208880515478035,"score_spread":0.2960767183542038,"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."}}