{"id":"W4378469099","doi":"10.1002/advs.202301243","title":"In Situ Exfoliation Method of Large‐Area 2D Materials","year":2023,"lang":"en","type":"article","venue":"Advanced Science","topic":"Graphene research and applications","field":"Materials Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Vetenskapsrådet; Danmarks Frie Forskningsfond; Villum Fonden; Göran Gustafssons Stiftelser; Göran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk Forskning; Magnus Bergvalls Stiftelse; Gordon and Betty Moore Foundation; International Centre for Advanced Materials","keywords":"Exfoliation joint; Materials science; Photoemission spectroscopy; Electron diffraction; Ultra-high vacuum; Nanotechnology; Crystallinity; Low-energy electron diffraction; Substrate (aquarium); X-ray photoelectron spectroscopy; Diffraction; Chemical engineering; Composite material; Optics; Graphene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002130838,0.00006696414,0.0001332356,0.0002928629,0.0001440285,0.00004077363,0.0004750016,0.00002152841,0.0001553356],"category_scores_gemma":[0.0003855219,0.00005952839,0.00001813846,0.002450611,0.0001988751,0.0006624018,0.0001507601,0.00003866079,0.0002101727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003491197,"about_ca_system_score_gemma":0.000117863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003448787,"about_ca_topic_score_gemma":0.0000351651,"domain_scores_codex":[0.9984906,0.00005557166,0.0002279587,0.0003273789,0.0004489681,0.0004495497],"domain_scores_gemma":[0.9992883,0.0001217589,0.00008504815,0.0003084073,0.0001149922,0.00008151676],"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":[0.00000630151,0.0000213117,0.00006898621,0.000009180334,2.404617e-7,0.000001285344,0.0001457882,0.000263213,0.984632,0.01422074,0.00006505151,0.0005658737],"study_design_scores_gemma":[0.0001688982,0.00002065879,0.009065561,0.00001394194,7.953251e-7,0.000001042845,0.0001623238,0.000228688,0.9754569,0.01450036,0.000316779,0.00006407807],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883255,0.00001387521,0.01013717,0.0003171233,0.0001372222,0.0002074495,0.00003659229,0.00005729316,0.0007678057],"genre_scores_gemma":[0.9814132,0.00002854671,0.01827973,0.00004632474,0.00001218649,0.00007422671,0.000004904946,0.000004919457,0.0001359341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009175153,"threshold_uncertainty_score":0.2701415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03161434111236308,"score_gpt":0.3984550411582512,"score_spread":0.3668407000458881,"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."}}