{"id":"W3087847574","doi":"10.1109/tmech.2020.3024996","title":"Robotic Prototyping of Paper-Based Field-Effect Transistors with Rolled-Up Semiconductor Microtubes","year":2020,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Nanomaterials and Printing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wafer; Inkwell; Materials science; Printed electronics; Field-effect transistor; Transistor; Rapid prototyping; Substrate (aquarium); Nanotechnology; Flexible electronics; Electrical conductor; Semiconductor; Electrode; Electronics; Optoelectronics; Electrical engineering; Engineering; Composite material; Voltage","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002089843,0.0003899299,0.000222938,0.0002943068,0.0002376196,0.0003455449,0.0005722907,0.0003486933,0.0008110962],"category_scores_gemma":[0.0004554164,0.0002202306,0.0004102826,0.0001506226,0.0002493217,0.0004881744,0.000281379,0.0003233187,0.0003227041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001867947,"about_ca_system_score_gemma":0.0001922572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002934542,"about_ca_topic_score_gemma":0.0006681701,"domain_scores_codex":[0.999806,0.00001967214,0.00001597879,0.00003843758,0.0001059681,0.00001394167],"domain_scores_gemma":[0.9997109,0.0001032047,0.00008324657,0.00005025921,0.00003139435,0.00002095677],"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":[0.00002846096,0.0000322557,0.000224694,0.0001165786,0.00001237919,0.0003605032,0.00007550777,0.002994219,0.9791864,0.001176588,0.0002441064,0.01554841],"study_design_scores_gemma":[0.00002755,0.0003912116,0.001263543,0.00002282186,0.00002318866,0.0005411142,0.00004115612,0.01776075,0.9671898,0.000361078,0.01233533,0.00004246757],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6271459,0.00251548,0.3588223,0.0003792494,0.0005878001,0.0004439737,0.0003335767,0.002045058,0.007726741],"genre_scores_gemma":[0.5507732,0.001307244,0.4430094,0.0000854902,0.00006538865,0.0002429789,0.0001456412,0.00007104785,0.004299488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008110962,"threshold_uncertainty_score":0.002713382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01107558016630123,"score_gpt":0.1970916083868007,"score_spread":0.1860160282204995,"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."}}