{"id":"W2906629215","doi":"10.1016/j.carbon.2018.12.095","title":"Low-damage nitrogen incorporation in graphene films by nitrogen plasma treatment: Effect of airborne contaminants","year":2018,"lang":"en","type":"article","venue":"Carbon","topic":"Graphene research and applications","field":"Materials Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Nitrogen; Copper; X-ray photoelectron spectroscopy; Materials science; Oxide; Raman spectroscopy; Chemical vapor deposition; Surface modification; Carbon fibers; Plasma; Adsorption; Substrate (aquarium); Graphene oxide paper; Inorganic chemistry; Chemical engineering; Analytical Chemistry (journal); Nanotechnology; Chemistry; Environmental chemistry; Composite material; Metallurgy; Organic chemistry","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":[0.0003310887,0.0001617175,0.0002718223,0.0001457656,0.00006496251,0.00002318783,0.0001939574,0.00006828572,0.00005696895],"category_scores_gemma":[0.00005209046,0.0001268799,0.00006181564,0.000442364,0.000232016,0.0001013437,0.00003915531,0.00005021621,0.00004891266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000565524,"about_ca_system_score_gemma":0.00005512767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008971242,"about_ca_topic_score_gemma":0.0003094101,"domain_scores_codex":[0.9987589,0.0001732017,0.0002537276,0.0002898731,0.0002116153,0.0003127368],"domain_scores_gemma":[0.9992551,0.0001076073,0.0001200643,0.0003329498,0.0000754642,0.0001087993],"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":[0.0001339115,0.0001225376,0.02858803,0.00002226094,0.00001060364,0.000003696835,0.00009326524,0.000001871004,0.9695843,0.0001452751,0.0001162312,0.001178032],"study_design_scores_gemma":[0.001199471,0.001088331,0.003538198,0.00002878805,0.00001729158,0.000001215497,0.00002670518,0.000578667,0.9924737,0.0008967061,0.00004374293,0.0001072158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983432,0.00008989375,0.00001907256,0.00002730364,0.0000516013,0.0005403938,0.0001032685,0.00003537986,0.0007898762],"genre_scores_gemma":[0.9995115,0.00003443279,0.0001057587,0.000007207509,0.00003903245,0.0001875327,0.00007118256,0.0000157359,0.00002760961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02504984,"threshold_uncertainty_score":0.5174013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009179023850541732,"score_gpt":0.2571346461368292,"score_spread":0.2479556222862875,"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."}}