{"id":"W2612642337","doi":"10.1063/1.4983074","title":"Methods to improve harvested energy and conversion efficiency of viscoelastic dielectric elastomer generators","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Dielectric materials and actuators","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Elastomer; Dielectric; Energy harvesting; Viscoelasticity; Dielectric elastomers; Materials science; Work (physics); Voltage; Nonlinear system; Mechanical energy; Energy transformation; Energy conversion efficiency; Computer science; Power (physics); Mechanical engineering; Composite material; Electrical engineering; Optoelectronics; Physics; Engineering; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001883788,0.0001304216,0.0003170525,0.00008586829,0.00007398189,0.00006694758,0.0001901052,0.00004878426,0.000007586036],"category_scores_gemma":[0.00002506961,0.0001087123,0.00005220186,0.000113644,0.00002969408,0.0001136065,0.00004013755,0.00008763026,0.000001215939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002572252,"about_ca_system_score_gemma":0.00002660418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004251654,"about_ca_topic_score_gemma":3.307437e-7,"domain_scores_codex":[0.9992944,0.00001159623,0.000293514,0.00008835486,0.0001454794,0.0001666825],"domain_scores_gemma":[0.9993439,0.00005614137,0.0002665071,0.0001617281,0.00006484161,0.0001069288],"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":[0.00004681199,0.00003198255,0.000010878,0.00005792522,0.000064835,0.000001652652,0.00009898087,0.005800063,0.9198437,0.001263853,0.0001947178,0.07258458],"study_design_scores_gemma":[0.0003817238,0.0001451569,0.0003070832,0.00001885359,0.00005432761,0.000002437149,0.00001037889,0.01243087,0.9846324,0.0005652892,0.001319849,0.0001315697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8942949,0.00009527373,0.1035985,0.00000441147,0.0005368029,0.00005683265,0.000002843318,0.00001205137,0.00139836],"genre_scores_gemma":[0.9947327,0.00006219115,0.004913918,0.00001977404,0.0002361784,0.000001925648,3.445273e-7,0.00002078246,0.0000121541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1004378,"threshold_uncertainty_score":0.443316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006315673424585392,"score_gpt":0.2373532140787617,"score_spread":0.2310375406541763,"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."}}