{"id":"W4410419494","doi":"10.1016/j.carbon.2025.120444","title":"Detailed in situ TEM/EELS analysis of laser-induced reduction of graphene oxide","year":2025,"lang":"en","type":"article","venue":"Carbon","topic":"Graphene research and applications","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Graphene; In situ; Oxide; Materials science; Laser; Reduction (mathematics); Nanotechnology; Chemical engineering; Chemistry; Metallurgy; Optics; Physics","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":[0.0003173718,0.00006060721,0.0002421348,0.0006564872,0.00001937758,0.000007215714,0.0001574039,0.00004490113,0.0000187062],"category_scores_gemma":[0.00006602793,0.00005713706,0.00009192183,0.002542318,0.00006598842,0.00003677057,0.00003549231,0.00005077654,0.000001167237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002554906,"about_ca_system_score_gemma":0.00006322783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006223808,"about_ca_topic_score_gemma":0.0006649435,"domain_scores_codex":[0.999164,0.00006997718,0.0002770646,0.0001845269,0.0001582026,0.0001462607],"domain_scores_gemma":[0.999413,0.00004732583,0.0000897659,0.000315245,0.00009897085,0.00003572577],"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":[0.00001941624,0.00006149345,0.008411383,0.00002145768,0.00004716678,3.207434e-7,0.00003775013,0.000145905,0.9899261,0.001110304,0.000008859744,0.0002098164],"study_design_scores_gemma":[0.0001582039,0.00001606774,0.07884948,0.00001956729,0.0001036777,8.877094e-8,0.000108386,0.0002977551,0.9191892,0.001213445,0.000004801203,0.0000393911],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976165,0.00005278554,0.00004337267,0.00008468005,0.00003253235,0.0001481494,0.000009443876,0.00001395145,0.00199863],"genre_scores_gemma":[0.9997065,0.00001912608,0.0001674662,0.00000419253,0.000005070577,0.00003953642,0.000008489138,0.000002983928,0.00004664185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07073699,"threshold_uncertainty_score":0.2329982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804434349497285,"score_gpt":0.2929704178481095,"score_spread":0.2749260743531367,"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."}}