{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007990431,0.0002414793,0.00007899659,0.0002861649,0.000148889,0.0001872969,0.0003152571,0.0004283659,0.002894322],"category_scores_gemma":[0.0001506871,0.0001309368,0.0001000768,0.0001379937,0.0001297405,0.0002359151,0.000152873,0.0002441734,0.0004622108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003297991,"about_ca_system_score_gemma":0.0001456926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001059814,"about_ca_topic_score_gemma":0.004521552,"domain_scores_codex":[0.9998963,0.000006469913,0.000006299734,0.00003253995,0.00004040092,0.0000180151],"domain_scores_gemma":[0.9999573,0.00001212089,0.000005808061,0.000004661788,0.00001200607,0.000008032165],"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":[0.00004252775,0.00001888641,0.0001954755,0.00003557748,0.000003320574,0.0000459625,0.000009236086,0.000148457,0.9963976,0.0001792218,0.0001378392,0.002785925],"study_design_scores_gemma":[0.000002679877,0.00004259376,0.001419007,0.000004845323,0.00000365002,0.0000563756,0.00001268248,0.001944137,0.9948054,0.00005279331,0.001652161,0.000003631302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807613,0.001432532,0.006739667,0.0001680124,0.0001775783,0.00003481538,0.0007842229,0.0002513808,0.009650334],"genre_scores_gemma":[0.9926788,0.0003342028,0.003381857,0.00006110992,0.000006751965,0.00001622107,0.0002406778,0.00001564461,0.003264729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002894322,"threshold_uncertainty_score":0.009682417,"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."}}