{"id":"W2805318417","doi":"10.1007/s00604-018-2851-1","title":"Synthesis and characterization of manganese diselenide nanoparticles (MnSeNPs): Determination of capsaicin by using MnSeNP-modified glassy carbon electrode","year":2018,"lang":"en","type":"article","venue":"Microchimica Acta","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Population and Public Health; Ministry of Science and Technology, Taiwan; King Saud University","keywords":"Manganese; Characterization (materials science); Glassy carbon; Materials science; Diselenide; Electrode; Nanoparticle; Carbon fibers; Capsaicin; Nanotechnology; Chemical engineering; Chemistry; Electrochemistry; Metallurgy; Composite number; Cyclic voltammetry; Composite material; Physical chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006772161,0.0001711249,0.0002718311,0.00008130449,0.00003672823,0.0000126926,0.00009373322,0.0001108592,0.000005691576],"category_scores_gemma":[0.00003629571,0.0001652625,0.00004061464,0.0001762641,0.0001103647,0.00006700285,0.00002095459,0.00007657368,4.793141e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004531333,"about_ca_system_score_gemma":0.000008818021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005778115,"about_ca_topic_score_gemma":0.00001949866,"domain_scores_codex":[0.9990786,0.00003482402,0.0003225636,0.000189299,0.0001130333,0.0002616867],"domain_scores_gemma":[0.99955,0.00006411625,0.0001103188,0.0001524704,0.00006658497,0.00005645862],"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":[0.00005240992,0.00003244481,0.0001873579,0.00009236278,0.00001940149,4.056385e-7,0.0001592261,8.380871e-7,0.9983891,0.000003255104,0.000014968,0.001048289],"study_design_scores_gemma":[0.0001534786,0.00005584585,0.0009342721,0.00005694624,0.00005686762,0.00001140489,0.00001163522,0.01881186,0.9796925,0.00001537661,0.00003154217,0.000168238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995252,0.00005698461,0.00008116312,0.00004434639,0.00003039525,0.0001247469,0.00003267661,0.00005311812,0.00005135757],"genre_scores_gemma":[0.9993559,0.00008321578,0.0004267932,0.00001244601,0.00005198197,0.000003234261,0.00001842697,0.00003284367,0.00001522335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01881102,"threshold_uncertainty_score":0.6739209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006031517822977657,"score_gpt":0.195911237817266,"score_spread":0.1898797199942884,"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."}}