{"id":"W2342238862","doi":"10.1021/acs.jpcb.5b04300","title":"Accurate Characterization of Ion Transport Properties in Binary Symmetric Electrolytes Using In Situ NMR Imaging and Inverse Modeling","year":2015,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Advanced Battery Materials and Technologies","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Council Canada; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Electrolyte; Diffusion; Monte Carlo method; Pulsed field gradient; Chemistry; Inverse; Ionic bonding; Characterization (materials science); Ion; Analytical Chemistry (journal); Thermodynamics; Materials science; Physics; Electrode; Mathematics; Physical chemistry; Statistics; Chromatography; Geometry; Nanotechnology","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":[0.0008269433,0.0005058012,0.0004964151,0.0003872027,0.0002357988,0.0006289525,0.0006038964,0.0006819947,0.0004133671],"category_scores_gemma":[0.002151901,0.0002855727,0.0002251813,0.0003272962,0.0004915398,0.001321762,0.0005038987,0.0005188895,0.0002048457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003501673,"about_ca_system_score_gemma":0.0004233293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006182439,"about_ca_topic_score_gemma":0.0010037,"domain_scores_codex":[0.9997241,0.00003428465,0.00002327226,0.00007395457,0.0001272138,0.00001724062],"domain_scores_gemma":[0.9994056,0.0002483219,0.0001160598,0.00008670623,0.0001202789,0.00002298233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006135598,0.00004067471,0.0008466095,0.0001101148,0.00001045484,0.00006527897,0.00005837189,0.01486294,0.9741421,0.001246378,0.00004323128,0.008512631],"study_design_scores_gemma":[0.000008433292,0.0001029737,0.001036339,0.000005974006,0.00001107701,0.0001034171,0.00002641369,0.2013246,0.7958206,0.001089913,0.0004522803,0.000018036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6749262,0.000418582,0.3225809,0.0001349312,0.00002184692,0.00004880634,0.0002287123,0.000327993,0.001312037],"genre_scores_gemma":[0.8609748,0.0004238226,0.1376494,0.00003791449,0.000007920403,0.00004756876,0.0001955525,0.00005185282,0.0006110551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008269433,"threshold_uncertainty_score":0.004373312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02198436475378246,"score_gpt":0.2203579694361851,"score_spread":0.1983736046824027,"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."}}