{"id":"W2561035437","doi":"10.1016/j.jelechem.2016.12.038","title":"Quantitative analysis of electrochemical diffusion layers using synchrotron infrared radiation","year":2016,"lang":"en","type":"article","venue":"Journal of Electroanalytical Chemistry","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Ferricyanide; Diffusion; Ferrocyanide; Analytical Chemistry (journal); Diffusion layer; Hydroquinone; Supporting electrolyte; Electrochemistry; Cyclic voltammetry; Infrared; Electrode; Electrolyte; Electrochemical cell; Linear sweep voltammetry; Microelectrode; Inorganic chemistry; Thermodynamics; Optics; Physical chemistry; Chromatography","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":[0.0007800942,0.0004582083,0.0003176166,0.0004788219,0.0003527754,0.0007193225,0.0007206382,0.0005664423,0.001221285],"category_scores_gemma":[0.0009005849,0.0004379746,0.0002576284,0.000477478,0.0003995883,0.0006785812,0.0002851909,0.0009127524,0.0003861746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004359382,"about_ca_system_score_gemma":0.0003177511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006801909,"about_ca_topic_score_gemma":0.001038898,"domain_scores_codex":[0.9995068,0.00009926784,0.0000369068,0.0001121545,0.0001940784,0.00005075969],"domain_scores_gemma":[0.9993889,0.0002479486,0.00008336249,0.00008733216,0.000169501,0.00002286933],"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":[0.0000338301,0.00001645012,0.00014218,0.00002588812,0.000004698912,0.000009271949,0.00002639882,0.00004208132,0.9978369,0.0000956282,0.00003176941,0.001734922],"study_design_scores_gemma":[0.000003658736,0.00002668842,0.001016345,0.000003037014,0.000007313994,0.00002686974,0.00001490493,0.0009986798,0.9973455,0.0000377013,0.0005162746,0.000002953608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8920754,0.002943919,0.09990441,0.0002298744,0.00007251317,0.00007385893,0.0007482379,0.0004820582,0.003469702],"genre_scores_gemma":[0.9165722,0.002413348,0.07435794,0.0001292877,0.00002615937,0.0001430015,0.0007351008,0.0001198462,0.005503189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001221285,"threshold_uncertainty_score":0.004125535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146349264363954,"score_gpt":0.2752741676518795,"score_spread":0.2638106750082399,"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."}}