{"id":"W3159146981","doi":"10.1021/acsaelm.1c00271","title":"A True Random Number Generator Based on Ionic Liquid Modulated Memristors","year":2021,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Department of Science and Technology of Sichuan Province; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Memristor; Ionic liquid; Neuromorphic engineering; Materials science; Nanotechnology; Computer science; Ultrashort pulse; Topology (electrical circuits); Artificial neural network; Electronic engineering; Electrical engineering; Artificial intelligence; Chemistry; Physics; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002075281,0.0002922543,0.0004120034,0.00004165666,0.0001221217,0.00005264765,0.0001442105,0.0001327383,0.0007558568],"category_scores_gemma":[0.00002111378,0.0002957471,0.00005296247,0.0002219993,0.0000171825,0.00004882815,0.00003017072,0.0002098729,0.000156664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002122154,"about_ca_system_score_gemma":0.00009145948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001344168,"about_ca_topic_score_gemma":0.000002328714,"domain_scores_codex":[0.9984012,0.00006182792,0.0003593087,0.0003611697,0.0001656293,0.0006508198],"domain_scores_gemma":[0.9993746,0.00007913269,0.00005761937,0.0003815786,0.00002996343,0.00007710772],"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":[0.0002962266,0.00002015402,2.735077e-7,0.00004335364,0.00003235038,0.00001370381,0.00001872693,0.09145702,0.906234,0.001477879,0.0002039144,0.0002023686],"study_design_scores_gemma":[0.001542767,0.00005143998,0.000003880877,0.0000203787,0.00002348783,0.000009698055,0.000004542464,0.0007633903,0.9940007,0.0004178861,0.002843121,0.0003187341],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954434,0.0001405171,0.002313253,0.00004048586,0.0003918784,0.0002563732,0.000009912214,0.0004955971,0.000908592],"genre_scores_gemma":[0.9988747,0.00006426222,0.00008064318,0.0003422322,0.0002881081,0.00008283913,0.00008057118,0.00008409722,0.0001025115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09069364,"threshold_uncertainty_score":0.9999495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005742239491315662,"score_gpt":0.2076659335809522,"score_spread":0.2019236940896365,"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."}}