{"id":"W4289717353","doi":"10.1039/d2ra03931a","title":"From 0D to 2D: N-doped carbon nanosheets for detection of alcohol-based chemical vapours","year":2022,"lang":"en","type":"article","venue":"RSC Advances","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Grantová Agentura České Republiky; Canada Foundation for Innovation; University of Saskatchewan; Canadian Light Source","keywords":"Vapours; Alcohol; Carbon fibers; Doping; Chemistry; Materials science; Nanotechnology; Organic chemistry; Optoelectronics; Psychology; Psychiatry; Composite material","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":[0.0001526244,0.00008964191,0.0001612275,0.00005292792,0.0001041637,0.00001257709,0.0001967156,0.00002323632,0.0001551295],"category_scores_gemma":[0.000066771,0.0000907616,0.00005902695,0.0001852299,0.00003767125,0.00004626654,0.00005998467,0.00005025369,0.000005580144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007371983,"about_ca_system_score_gemma":0.00005009991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003027648,"about_ca_topic_score_gemma":0.00004298453,"domain_scores_codex":[0.9990794,0.00003024985,0.0002056451,0.0002907452,0.0002255659,0.0001683907],"domain_scores_gemma":[0.9994003,0.0001457916,0.0001081436,0.0002347209,0.0000485067,0.00006256452],"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":[0.0001248564,0.00005404066,0.00002275419,0.00001838314,0.000002263366,3.388968e-7,0.00009702327,0.0007198675,0.9966154,0.0001513771,0.00004494498,0.002148825],"study_design_scores_gemma":[0.0004063392,0.00009866143,0.00006726541,0.00001115978,0.0000140737,7.439546e-7,0.0001845098,0.001792033,0.9748253,0.001194104,0.02129011,0.0001156973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945593,0.0002724019,0.003277602,0.0003594872,0.0004772304,0.0003850834,0.0002473126,0.00006347187,0.0003581106],"genre_scores_gemma":[0.9960196,0.000004479745,0.003047842,0.0001923111,0.0001166473,0.0005434146,0.00002834275,0.00001398161,0.00003337981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02179003,"threshold_uncertainty_score":0.3701151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780247199176711,"score_gpt":0.2878069556682594,"score_spread":0.2700044836764923,"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."}}