{"id":"W4405239420","doi":"10.1039/d4na00463a","title":"Laser writing of metal-oxide doped graphene films for tunable sensor applications","year":2024,"lang":"en","type":"article","venue":"Nanoscale Advances","topic":"Laser-Ablation Synthesis of Nanoparticles","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Materials science; Oxide; Doping; Optoelectronics; Laser; Metal; Graphene oxide paper; Nanotechnology; Metallurgy; Optics","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":[0.0001463305,0.0001333843,0.0002063531,0.0001074353,0.00006261553,0.00003821527,0.0001237002,0.00005139364,0.00006765129],"category_scores_gemma":[0.00003704298,0.0001250409,0.0001114996,0.0003497181,0.00004917394,0.0004257666,0.00001599542,0.00005608286,0.00004396915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001670291,"about_ca_system_score_gemma":0.00001268822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003360709,"about_ca_topic_score_gemma":0.000009570325,"domain_scores_codex":[0.9990662,0.00001294927,0.0003333232,0.0002093195,0.0001481685,0.000230033],"domain_scores_gemma":[0.9991878,0.0004553593,0.00003599898,0.0002008034,0.0000654639,0.00005455043],"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":[0.000009659059,0.0000526852,0.0003723763,0.0007017491,0.0001099583,0.000002201261,0.00005790596,0.03045988,0.9261458,0.002031992,0.001128411,0.03892742],"study_design_scores_gemma":[0.0001456959,0.00001427665,0.000104369,0.00006903581,0.0000501982,0.000002084789,0.0001225155,0.01322122,0.8963702,0.001087434,0.08866108,0.000151863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8438889,0.02092977,0.1260738,0.0002783094,0.0004695528,0.001446615,0.0004435006,0.00160924,0.004860299],"genre_scores_gemma":[0.9664112,0.0003116483,0.03224372,0.0000175236,0.00006737354,0.0004160085,0.00001395802,0.00004505178,0.000473474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1225223,"threshold_uncertainty_score":0.5099022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186589384141484,"score_gpt":0.2520654490322075,"score_spread":0.2401995551907926,"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."}}