{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000274701,0.0002760764,0.0009512329,0.0002404811,0.00006730352,0.00002364236,0.0004486012,0.0002618628,0.0009838741],"category_scores_gemma":[0.000608288,0.0001927193,0.0009764566,0.001511567,0.0001767968,0.0001526008,0.00004815679,0.0004259604,0.000002781304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004448886,"about_ca_system_score_gemma":0.0002224512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005484368,"about_ca_topic_score_gemma":9.837648e-7,"domain_scores_codex":[0.997236,0.00002753243,0.001247102,0.0003401974,0.0006746755,0.000474452],"domain_scores_gemma":[0.9972407,0.0005285321,0.001132024,0.0003684524,0.0004540937,0.0002762236],"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":[0.0002549963,0.0002619029,0.001606769,0.0000467311,0.00294315,0.000003879183,0.00001745008,0.00003872445,0.9938047,0.0002676945,0.00008771374,0.0006662999],"study_design_scores_gemma":[0.0006430125,0.00007512312,0.0001396396,0.00007798818,0.004806462,0.00002627541,0.00003878936,0.01567672,0.9769777,0.001051322,0.0002484386,0.0002385094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800497,0.0004956633,0.01760959,0.0002852351,0.000006327427,0.00002898706,0.00001586396,0.00001738375,0.001491301],"genre_scores_gemma":[0.9976957,0.0002186567,0.001482335,0.00002856386,0.0001538966,0.000003247241,0.00002444615,0.00002336088,0.000369822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01764602,"threshold_uncertainty_score":0.9999294,"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."}}