{"id":"W2514437433","doi":"10.1021/acs.chemmater.6b02726","title":"Methods and Protocols for Electrochemical Energy Storage Materials Research","year":2016,"lang":"en","type":"article","venue":"Chemistry of Materials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Waterloo Institute for Nanotechnology, University of Waterloo; BASF; Natural Resources Canada; U.S. Department of Energy","keywords":"Battery (electricity); Characterization (materials science); Dielectric spectroscopy; Electrochemical energy storage; Electrochemistry; Materials science; Electrochemical cell; X-ray photoelectron spectroscopy; Energy storage; Electrode; Computer science; Nanotechnology; Fabrication; Analytical Chemistry (journal); Chemical engineering; Chemistry; Supercapacitor; Physical chemistry; Engineering; Physics","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":[0.003561179,0.003808543,0.002868223,0.005512321,0.002831532,0.001475532,0.004005459,0.001860373,0.04242031],"category_scores_gemma":[0.002799276,0.002372497,0.001208482,0.004855574,0.0008547409,0.001615584,0.001956112,0.005225834,0.04783714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007228791,"about_ca_system_score_gemma":0.001877204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000693068,"about_ca_topic_score_gemma":0.002328871,"domain_scores_codex":[0.9959862,0.000726416,0.00060472,0.0006417694,0.00171857,0.0003222621],"domain_scores_gemma":[0.9976574,0.0003504285,0.0001276277,0.0009151172,0.0008315908,0.0001178869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004104706,0.0008350681,0.000481682,0.004374808,0.0001640184,0.001067938,0.0005447447,0.001543253,0.776556,0.01768747,0.07307005,0.1232645],"study_design_scores_gemma":[0.0001542844,0.0003658944,0.0006195588,0.0002916717,0.00008979053,0.001038501,0.00009870902,0.001461856,0.2645643,0.005227213,0.7259411,0.0001471848],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01572926,0.01973049,0.8376927,0.001234653,0.004375831,0.02486784,0.02775549,0.01323856,0.05537516],"genre_scores_gemma":[0.03002606,0.02782237,0.7437871,0.001273901,0.0007608362,0.09224044,0.04109133,0.00218939,0.06080861],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04242031,"threshold_uncertainty_score":0.14191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04451646530710045,"score_gpt":0.3900446861153404,"score_spread":0.34552822080824,"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."}}