{"id":"W2969568319","doi":"10.1039/c9ee02295c","title":"A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells: rational control of vertical stratification for high performance","year":2019,"lang":"en","type":"article","venue":"Energy & Environmental Science","topic":"Organic Electronics and Photovoltaics","field":"Engineering","cited_by":194,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carbon Engineering (Canada)","funders":"Division of Electrical, Communications and Cyber Systems; Office of Naval Research; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft; North Carolina State University; National Science Foundation","keywords":"Layer (electronics); Coating; Blade (archaeology); Stratification (seeds); Organic solar cell; Layer by layer; Materials science; Mechanical engineering; Process engineering; Engineering; Nanotechnology; Photovoltaic system; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002102733,0.0001298417,0.0001310732,0.00004371617,0.0001410515,0.0000264418,0.000193071,0.00006269375,0.00006532253],"category_scores_gemma":[0.000012768,0.0001316666,0.00003480148,0.0001494251,0.0001056246,0.0001974034,0.00001327433,0.00006612605,0.000002216282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002253904,"about_ca_system_score_gemma":0.00007453602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007059375,"about_ca_topic_score_gemma":0.00000197796,"domain_scores_codex":[0.9989759,0.00001080293,0.0002183558,0.0002802434,0.0002383945,0.0002763149],"domain_scores_gemma":[0.9996316,0.00008702714,0.00005038681,0.0001544442,0.00001949732,0.0000569809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009992555,0.00005139931,0.0001396714,0.0000101662,0.000006106328,6.939477e-9,0.00002410917,0.4358907,0.5637267,0.00007811944,0.000004839879,0.00005823004],"study_design_scores_gemma":[0.0004721211,0.00008573131,0.0001328584,0.000002164395,0.000007423747,1.574814e-7,0.00001964931,0.5000376,0.4991416,0.000003874597,0.0000188458,0.0000779531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3591511,0.00003195893,0.6403493,0.000006619427,0.00006489165,0.0002923686,0.00005217895,0.00001867059,0.00003299077],"genre_scores_gemma":[0.9813333,0.00001581202,0.01832851,0.00003793057,0.00002317818,0.0000949007,0.0001075876,0.00002563477,0.00003314627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6221823,"threshold_uncertainty_score":0.5369206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005242188496034249,"score_gpt":0.1771024340447459,"score_spread":0.1718602455487117,"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."}}