{"id":"W3164354100","doi":"10.1002/adfm.202102765","title":"Enabling High‐Performance NASICON‐Based Solid‐State Lithium Metal Batteries Towards Practical Conditions","year":2021,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Institut National de la Recherche Scientifique; Hydro-Québec","funders":"Argonne National Laboratory; Vehicle Technologies Office; Office of Science; Hydro-Québec; U.S. Department of Energy","keywords":"Materials science; Electrolyte; Lithium (medication); Cathode; Fast ion conductor; Lithium metal; Chemical engineering; Anode; Ionic conductivity; Metal; Conductivity; Synchrotron; Electrode; Metallurgy; Physical chemistry; Optics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002440289,0.0004122318,0.0005493016,0.0001187455,0.0002377023,0.0001926742,0.0001282905,0.0001152618,0.007031334],"category_scores_gemma":[0.0002017806,0.0004350613,0.00005669045,0.0002207652,0.0001303024,0.001281256,0.00009954551,0.0001625915,0.0004617972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001644166,"about_ca_system_score_gemma":0.0001450491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002369685,"about_ca_topic_score_gemma":0.000001394889,"domain_scores_codex":[0.9976693,0.0001101956,0.0007473578,0.0004798706,0.0004150074,0.0005782961],"domain_scores_gemma":[0.9988573,0.000186083,0.0001376132,0.0004471909,0.0002545131,0.0001173426],"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":[0.0001351163,0.00006439751,0.00002152223,0.0002065252,0.0001114724,0.00004951327,0.00002239396,0.0577372,0.9396927,0.0007256357,0.0006795698,0.0005539132],"study_design_scores_gemma":[0.0008952331,0.00007291833,0.002065273,0.00009602556,0.00005386426,0.0000812394,0.0000424083,0.0001306276,0.9871338,0.001862691,0.007085979,0.0004799216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663856,0.00007919221,0.02241624,0.0004958027,0.00845496,0.0002678093,0.00077387,0.0005951756,0.0005313207],"genre_scores_gemma":[0.9832299,0.00009701156,0.01304483,0.0009503048,0.0005326363,0.0003168424,0.000972379,0.0001103905,0.0007457416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05760657,"threshold_uncertainty_score":0.9998101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02237037209848818,"score_gpt":0.2747827566737416,"score_spread":0.2524123845752534,"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."}}