{"id":"W4404973126","doi":"10.1016/j.microc.2024.112324","title":"Construction and application of an electrochemical sensor based on MIL-101-NH2(Fe)-derived Fe–C porous materials doped with reduced graphene oxide for baicalin detection","year":2024,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Flavonoids in Medical Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Biotechnology Research Institute","funders":"Natural Science Foundation of Qingdao; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Graphene; Oxide; Porosity; Materials science; Doping; Electrochemistry; Baicalin; Electrochemical gas sensor; Chemical engineering; Inorganic chemistry; Nanotechnology; Composite material; Chemistry; Optoelectronics; Metallurgy; Physical chemistry; Chromatography; Electrode; Engineering","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":[0.0006342318,0.0002401556,0.0004524979,0.0002532513,0.0001074949,0.00007896302,0.000123032,0.0003052608,0.000045135],"category_scores_gemma":[0.0004560533,0.0001845483,0.00008851266,0.000279514,0.0004091368,0.00008136825,0.00002240686,0.0007293217,0.000003304955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002001772,"about_ca_system_score_gemma":0.0002356299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001328722,"about_ca_topic_score_gemma":0.000001094432,"domain_scores_codex":[0.9978694,0.000102103,0.0005693645,0.0004873391,0.0005468759,0.0004249319],"domain_scores_gemma":[0.9985853,0.0003015667,0.0001362027,0.0002394738,0.0002958919,0.0004415746],"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":[0.00469343,0.0001720172,0.00002056164,0.0003241599,0.00008296884,0.00002782012,0.0000261552,0.000001472136,0.9752871,0.00002419164,0.00007211902,0.01926796],"study_design_scores_gemma":[0.002436353,0.001271919,0.0001177546,0.0003806557,0.0001660714,0.001254786,0.00007997255,0.003086798,0.990448,0.000372532,0.0002031391,0.0001820346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9115765,0.00012317,0.08664542,0.0008677202,0.00007648481,0.0006079663,0.00001838944,0.00006757541,0.0000167823],"genre_scores_gemma":[0.952847,0.00005223843,0.04627314,0.0001978302,0.0004079458,0.00006685276,0.00008843409,0.00005431735,0.00001224302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04127051,"threshold_uncertainty_score":0.7525662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009273423894433796,"score_gpt":0.2929920099974817,"score_spread":0.2837185861030479,"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."}}