{"id":"W2922086394","doi":"10.1002/admt.201800384","title":"Direct Micropatterning of Phase Separation Membranes Using Hydrogel Soft Lithography","year":2019,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Suncor Energy Incorporated; ConocoPhillips","keywords":"Membrane; Materials science; Micropatterning; Microscale chemistry; Chemical engineering; Permeation; Phase (matter); Chromatography; Nanotechnology; 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":[0.0001186979,0.0002994404,0.0002093606,0.0002088003,0.0001591476,0.0002530295,0.0002576487,0.000300214,0.0009861201],"category_scores_gemma":[0.000237619,0.0002107118,0.0002996823,0.00009750979,0.0002491061,0.0003410342,0.000342537,0.0004002773,0.0005293561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002764901,"about_ca_system_score_gemma":0.0001771722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002420828,"about_ca_topic_score_gemma":0.0005241613,"domain_scores_codex":[0.999878,0.00001020649,0.000009926594,0.00002926014,0.00004858352,0.00002398124],"domain_scores_gemma":[0.9998038,0.00007371302,0.00005406194,0.00003474924,0.00001982444,0.0000138224],"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":[0.000006023025,0.000003880573,0.00002701776,0.00002828363,0.000001936448,0.00002292469,0.000006709033,0.00007704928,0.9980339,0.00008193574,0.0000207042,0.001689616],"study_design_scores_gemma":[0.00000237077,0.00002090407,0.0002305142,0.000001887739,0.00000196621,0.00004092998,0.000004609847,0.0005759815,0.9982844,0.00003559784,0.0007982067,0.000002592582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8487061,0.002072816,0.1441723,0.000242466,0.0002263401,0.0001523584,0.0004370872,0.0005015731,0.003488973],"genre_scores_gemma":[0.9064475,0.001460588,0.0873673,0.0001538027,0.00006377185,0.0001455131,0.0002313922,0.00007590875,0.004054349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009861201,"threshold_uncertainty_score":0.003298938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009784566390613965,"score_gpt":0.2828561352086015,"score_spread":0.2730715688179875,"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."}}