{"id":"W4253448668","doi":"10.32920/ryerson.14649855.v1","title":"A computational study of surface-directed phase separation in polymer blends under temperature gradient","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Block Copolymer Self-Assembly","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Spinodal decomposition; Wetting; Materials science; Temperature gradient; Spinodal; Surface energy; Phase (matter); Thermodynamics; Atmospheric temperature range; Polymer; Range (aeronautics); Diffusion; Growth rate; Surface (topology); Chemical physics; Chemistry; Composite material; Physics; Mathematics","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":[0.0002276156,0.0003806667,0.0006506686,0.0004290958,0.0005665786,0.0006410033,0.0006776678,0.001405667,0.002007278],"category_scores_gemma":[0.0008793268,0.0003727368,0.0005600018,0.0004236115,0.0006148097,0.0005408638,0.0004064057,0.0005582801,0.0001237592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006213522,"about_ca_system_score_gemma":0.0009109501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0126291,"about_ca_topic_score_gemma":0.007873447,"domain_scores_codex":[0.9999402,0.00001263584,0.00000237242,0.00001158173,0.00001244505,0.00002087197],"domain_scores_gemma":[0.9996569,0.0002163823,0.00003477967,0.00001657811,0.00004219641,0.00003309414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005541223,0.00004498504,0.001273445,0.00004214809,0.00001644812,0.0001181459,0.00001696335,0.9949182,0.001486586,0.001007106,0.0001113108,0.0009093428],"study_design_scores_gemma":[0.000009428271,0.00001420473,0.0002499878,0.000001557959,0.000003133544,0.000005891678,0.000008801761,0.9993209,0.0001980732,0.0001273855,0.00005850756,0.000002123921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758391,0.0001799237,0.01516617,0.0003130589,0.00002708306,0.00004315665,0.0002983923,0.00008615889,0.008046987],"genre_scores_gemma":[0.9920351,0.00007553247,0.005943755,0.00004158338,0.000007835089,0.00006935249,0.0001580313,0.00002380164,0.001645154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0126291,"threshold_uncertainty_score":0.02511114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954355505464719,"score_gpt":0.317063900613998,"score_spread":0.2975203455593508,"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."}}