{"id":"W2788068893","doi":"10.1021/acs.analchem.7b05006","title":"Rational Design of Magnetic Micronanoelectrodes for Recognition and Ultrasensitive Quantification of Cysteine Enantiomers","year":2018,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"China Scholarship Council; Government of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Chemistry; Enantiomer; Cysteine; Rational design; Chromatography; Stereochemistry; Nanotechnology; Organic chemistry; Enzyme","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004517658,0.0005410206,0.0005603194,0.0003994624,0.0002367752,0.0005168464,0.0009074287,0.0007638886,0.0003984607],"category_scores_gemma":[0.0007483053,0.0004331434,0.0002735865,0.0002035564,0.0002900679,0.0004820571,0.000391635,0.0004457161,0.0005157111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00051905,"about_ca_system_score_gemma":0.0003798149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000381916,"about_ca_topic_score_gemma":0.001409857,"domain_scores_codex":[0.9996543,0.00004927427,0.00004091905,0.00009426988,0.0001115677,0.00004970299],"domain_scores_gemma":[0.9998252,0.00003961985,0.00003613946,0.00001885292,0.00005291478,0.00002733085],"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":[0.00004765358,0.00003518157,0.0001349526,0.0001140299,0.00001186545,0.00008143906,0.00001806362,0.0006913996,0.9914759,0.0007069042,0.0001132588,0.006569424],"study_design_scores_gemma":[0.0000260095,0.0002314517,0.0005683053,0.00001129604,0.00001420765,0.0001420644,0.00002617934,0.00739062,0.9862708,0.0003026338,0.005002492,0.00001395128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7969338,0.01199056,0.1781226,0.001255216,0.0006559262,0.0006963625,0.0005877877,0.001068628,0.00868918],"genre_scores_gemma":[0.791146,0.004034957,0.2002553,0.0003933314,0.00007435941,0.0005320706,0.0004349153,0.00004908959,0.003080063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009074287,"threshold_uncertainty_score":0.003766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307648455708858,"score_gpt":0.2790263041416042,"score_spread":0.2559498195845156,"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."}}