{"id":"W2969834347","doi":"10.1002/aelm.201900464","title":"Tribo‐Tunneling DC Generator with Carbon Aerogel/Silicon Multi‐Nanocontacts","year":2019,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University at Buffalo; National Science Foundation","keywords":"Materials science; Aerogel; Optoelectronics; Quantum tunnelling; Diode; Silicon; Nanotechnology; Carbon fibers; Composite material","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":[0.00008215495,0.0001985606,0.000162399,0.0002245174,0.0001523819,0.0001889159,0.0003196656,0.0002160819,0.001280423],"category_scores_gemma":[0.0001292655,0.0001233176,0.0001731453,0.0001878707,0.0002169164,0.0002674935,0.0002291042,0.0003113247,0.0002117208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000319725,"about_ca_system_score_gemma":0.0001309926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003708657,"about_ca_topic_score_gemma":0.0005661108,"domain_scores_codex":[0.9999211,0.000002924802,0.000003962484,0.00002031823,0.00003924712,0.0000125267],"domain_scores_gemma":[0.9998969,0.00002216125,0.00001950795,0.00001598976,0.00002535859,0.00002003221],"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":[0.00002475451,0.00001260347,0.0001337767,0.00003510918,0.000003986079,0.00009977533,0.00001989807,0.0002755724,0.9962037,0.0003513261,0.0001458149,0.002693689],"study_design_scores_gemma":[0.00001028289,0.00005943492,0.0005920512,0.000001902797,0.000004085207,0.00006859437,0.00001085381,0.008447366,0.9896327,0.00006282993,0.001104923,0.000004976187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837353,0.0004412888,0.01171937,0.000142202,0.00009200857,0.00004277976,0.0001205796,0.000386065,0.003320406],"genre_scores_gemma":[0.9933554,0.00007312014,0.005126064,0.00002391129,0.000008000898,0.00001328437,0.00003228023,0.00001739866,0.001350381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001280423,"threshold_uncertainty_score":0.004283428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005544426186134268,"score_gpt":0.2010987055818469,"score_spread":0.1955542793957126,"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."}}