{"id":"W4224275407","doi":"10.1002/admt.202101610","title":"A Hybrid Generator with Electromagnetic Transduction for Improving the Power Density of Triboelectric Nanogenerators and Scavenging Wind Energy","year":2022,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto; University of New Brunswick","funders":"National Science Fund for Distinguished Young Scholars; National Natural Science Foundation of China","keywords":"Triboelectric effect; Nanogenerator; Electrical engineering; Power density; Wind power; Capacitor; Energy harvesting; Power (physics); Generator (circuit theory); Electricity generation; Engineering; Voltage; Physics","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":[0.0001830747,0.0002857922,0.0002344626,0.0002729167,0.00009881811,0.0002068585,0.0003739906,0.000336262,0.001082586],"category_scores_gemma":[0.0001971173,0.0001244814,0.0001471371,0.0002156482,0.0001856303,0.0004573881,0.0002777639,0.0001739152,0.000249843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193696,"about_ca_system_score_gemma":0.00005842806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003745791,"about_ca_topic_score_gemma":0.0001028793,"domain_scores_codex":[0.9998957,0.00001913126,0.000006679215,0.00003202246,0.00003274155,0.0000137084],"domain_scores_gemma":[0.9999182,0.00002043428,0.00001936426,0.00001017215,0.00002014521,0.00001172338],"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":[0.00003619148,0.00003693406,0.0001753446,0.00007151235,0.000007479461,0.00006087673,0.00001879583,0.0003205172,0.987686,0.0005032147,0.000170868,0.01091229],"study_design_scores_gemma":[0.00003521052,0.0003679526,0.001191237,0.000009145983,0.00001754767,0.0002245038,0.00002634482,0.01718095,0.9752788,0.0003528907,0.005298452,0.00001693001],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8744258,0.00124015,0.1196853,0.0003640679,0.0002212171,0.0001430858,0.0001145916,0.000510981,0.003294904],"genre_scores_gemma":[0.9603474,0.0002273115,0.03712821,0.0001184437,0.00002800073,0.00006961102,0.00004520966,0.00003154843,0.002004262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001082586,"threshold_uncertainty_score":0.003621638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003865376024193223,"score_gpt":0.1719975894498069,"score_spread":0.1681322134256137,"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."}}