{"id":"W3107852115","doi":"10.1016/j.microc.2020.105786","title":"A caffeic acid sensor based on CuZnO /MWCNTs composite modified electrode","year":2020,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Marianopolis College","funders":"National Natural Science Foundation of China","keywords":"Fourier transform infrared spectroscopy; Dielectric spectroscopy; X-ray photoelectron spectroscopy; Materials science; Cyclic voltammetry; Differential pulse voltammetry; Analytical Chemistry (journal); Detection limit; Electrochemical gas sensor; Scanning electron microscope; Nuclear chemistry; Coprecipitation; Electrode; Electrochemistry; Chemical engineering; Chemistry; Inorganic chemistry; Chromatography; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002534986,0.0007043409,0.000548295,0.000512464,0.0002466588,0.0002832605,0.001459617,0.001047475,0.0007466911],"category_scores_gemma":[0.0002959743,0.0003346172,0.0003963138,0.0004702079,0.0002574913,0.0006572473,0.0003077231,0.0004502428,0.0003225958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002815038,"about_ca_system_score_gemma":0.0002040739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008628692,"about_ca_topic_score_gemma":0.001999032,"domain_scores_codex":[0.9995473,0.00003705714,0.00003018677,0.0001665025,0.0001571693,0.00006182786],"domain_scores_gemma":[0.9998407,0.000023694,0.00003173445,0.00001812899,0.00006065473,0.00002500033],"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":[0.00003541505,0.000008167102,0.00007403782,0.00005224107,0.000006883977,0.00006159836,0.000009633601,0.00002003744,0.9980398,0.00003107568,0.00004424679,0.00161686],"study_design_scores_gemma":[0.000003251683,0.00009056272,0.0006099011,0.000002363056,0.00001254514,0.0001260316,0.00001002083,0.0004704625,0.9977238,0.00001181479,0.0009332274,0.000006183364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9045171,0.005423547,0.0831536,0.0005497875,0.0006193737,0.0001941587,0.000728088,0.001272962,0.003541362],"genre_scores_gemma":[0.9553301,0.001757658,0.03719477,0.000169457,0.00007794893,0.00006329405,0.0004276011,0.0000325503,0.004946738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001459617,"threshold_uncertainty_score":0.002497971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008478204261472147,"score_gpt":0.191022073889859,"score_spread":0.1825438696283868,"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."}}