{"id":"W2783471913","doi":"10.1039/c7nr08725j","title":"Borophene hydride: a stiff 2D material with high thermal conductivity and attractive optical and electronic properties","year":2018,"lang":"en","type":"preprint","venue":"Nanoscale","topic":"Graphene research and applications","field":"Materials Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick; Toronto Public Health","funders":"H2020 European Research Council; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Borophene; Materials science; Zigzag; Hydride; Anisotropy; Band gap; Condensed matter physics; Boron; Electronic structure; Optoelectronics; Nanotechnology; Metal; Optics; Chemistry; Monolayer; Metallurgy; Physics; Geometry","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":[0.0002851225,0.0002743224,0.0003581962,0.00005440108,0.0002738752,0.0002988862,0.0002315073,0.0002053734,0.000210214],"category_scores_gemma":[0.00003830503,0.0001910742,0.00002891723,0.00006787598,0.001065516,0.0001688989,0.0005530364,0.0003651522,0.00002471493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006219901,"about_ca_system_score_gemma":0.0003057308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006392465,"about_ca_topic_score_gemma":0.000120599,"domain_scores_codex":[0.9980785,0.0001204176,0.0001824176,0.0007257354,0.0003333558,0.0005595221],"domain_scores_gemma":[0.9991184,0.0000508505,0.0001025142,0.0004007879,0.0001415073,0.000185876],"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":[0.0003909206,0.0001082295,0.000211213,0.00009450753,0.00002894027,0.000003814332,0.0001219226,0.000004552973,0.9976756,0.0007623337,0.00009078695,0.0005071712],"study_design_scores_gemma":[0.0004478127,0.0004289067,0.01338023,0.00008311915,0.00005310557,0.00002683994,0.00004423019,0.00004707422,0.9816573,0.003272312,0.000244813,0.0003143164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978013,0.0002687783,0.000123525,0.0004944808,0.0001210978,0.0007282996,0.0001672076,0.00007641258,0.0002188677],"genre_scores_gemma":[0.9980646,0.00009287399,0.001024025,0.00002168785,0.000260042,0.0003491296,0.0000232364,0.00003093794,0.000133477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01601837,"threshold_uncertainty_score":0.7791781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02071145955387933,"score_gpt":0.2554689858188068,"score_spread":0.2347575262649275,"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."}}