{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007240671,0.0002046421,0.00008782651,0.0001743431,0.0001945892,0.000276209,0.0003229057,0.0003721295,0.001742978],"category_scores_gemma":[0.0003116089,0.0001138252,0.0001084774,0.0001922583,0.0001647497,0.0003289585,0.000130035,0.0003991884,0.0004345545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001670086,"about_ca_system_score_gemma":0.0001187585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004763896,"about_ca_topic_score_gemma":0.002309082,"domain_scores_codex":[0.9999409,0.000004704842,0.000003917727,0.00001252179,0.00002856742,0.00000940446],"domain_scores_gemma":[0.9999024,0.00003417761,0.00001544248,0.00001887679,0.00001981382,0.000009249396],"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":[0.00001045475,0.000008516548,0.00003771697,0.00004438267,0.00000234648,0.00006810234,0.00002612661,0.0001249454,0.9947484,0.000287571,0.0005200913,0.004121385],"study_design_scores_gemma":[0.000006922891,0.00004187327,0.0003581146,0.000009341656,0.000003852632,0.00008025162,0.00002954348,0.00286814,0.9916552,0.0001915587,0.004746144,0.000008884227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422448,0.003584029,0.02167183,0.001544774,0.0008383339,0.00006715769,0.0006153753,0.000714094,0.02871963],"genre_scores_gemma":[0.9747762,0.0009643626,0.01687245,0.0002062847,0.00005495485,0.0000205215,0.0001485367,0.00006654867,0.006890061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001742978,"threshold_uncertainty_score":0.005830884,"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."}}