{"id":"W3013882886","doi":"10.1186/s11671-019-3225-2","title":"Transparent PAN:TiO2 and PAN-co-PMA:TiO2 Nanofiber Composite Membranes with High Efficiency in Particulate Matter Pollutants Filtration","year":2020,"lang":"en","type":"article","venue":"Nanoscale Research Letters","topic":"Electrospun Nanofibers in Biomedical Applications","field":"Materials Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Postdoctoral Research Foundation of China; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Particulates; Pollutant; Filtration (mathematics); Nanochemistry; Materials science; Membrane; Nanofiber; Composite number; Chemical engineering; Nanotechnology; Environmental chemistry; Composite material; Chemistry; Engineering; Organic chemistry","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":[0.0003851826,0.0006664136,0.0003309412,0.0003966665,0.0002325897,0.0003203652,0.0003236833,0.0009425037,0.0004148404],"category_scores_gemma":[0.0002637076,0.0002442473,0.000448636,0.000254592,0.0001740469,0.000790059,0.0002798786,0.0003491001,0.0002651309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003475192,"about_ca_system_score_gemma":0.0001745258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007814566,"about_ca_topic_score_gemma":0.001017324,"domain_scores_codex":[0.999739,0.00003047906,0.0000174425,0.00006410495,0.0001047121,0.00004422737],"domain_scores_gemma":[0.999835,0.00002165964,0.00006334648,0.00001274602,0.00004393343,0.00002333652],"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":[0.0000258213,0.0000141464,0.00004358394,0.00004707009,0.000004746314,0.00002736686,0.000007946852,0.000103508,0.9989328,0.00002672034,0.00003754889,0.0007287165],"study_design_scores_gemma":[0.000008725571,0.0000774522,0.001087193,0.000004426427,0.00001309676,0.00007046603,0.000009311843,0.001367054,0.9965701,0.00001768834,0.0007658618,0.000008784919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725896,0.00436636,0.02058169,0.0001613629,0.00009851394,0.00004863432,0.0001799303,0.0003406833,0.001633149],"genre_scores_gemma":[0.9814527,0.001848764,0.01304883,0.000066548,0.00003222117,0.00006182923,0.0001190295,0.00003625122,0.003333888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009425037,"threshold_uncertainty_score":0.002521455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03096021700010704,"score_gpt":0.3051903952517863,"score_spread":0.2742301782516793,"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."}}