{"id":"W3167188599","doi":"10.1002/aenm.202100503","title":"Mesocrystallizing Nanograins for Enhanced Li<sup>+</sup> Storage","year":2021,"lang":"en","type":"article","venue":"Advanced Energy Materials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Argonne National Laboratory; Office of Energy Efficiency; National Natural Science Foundation of China; Higher Education Discipline Innovation Project; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; National Science Foundation","keywords":"Pseudocapacitance; Materials science; Crystallinity; Electrode; Anode; Battery (electricity); Nanotechnology; Chemical engineering; Capacitance; Supercapacitor; Composite material; Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002034275,0.0004711376,0.0006838629,0.00009705228,0.0001604731,0.0001463749,0.0002901166,0.0001642488,0.0009425969],"category_scores_gemma":[0.00009448794,0.0005268796,0.0000883248,0.0001964607,0.00005296412,0.0005430579,0.0001259302,0.00005789249,0.00003183923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001772829,"about_ca_system_score_gemma":0.00003704933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003882791,"about_ca_topic_score_gemma":0.000005466338,"domain_scores_codex":[0.997605,0.00009179932,0.0007374185,0.000551286,0.0002298933,0.000784617],"domain_scores_gemma":[0.9988275,0.0001394968,0.0001243999,0.0006463471,0.0001416723,0.000120634],"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":[0.00004224748,0.0000253498,4.828688e-7,0.0001680776,0.00007241362,0.00001612809,0.0001414834,0.1917474,0.8024809,0.002259317,0.0004101256,0.002636037],"study_design_scores_gemma":[0.0009365734,0.00004390431,0.000004811691,0.0001070564,0.00002916535,0.00001032572,0.0001352977,0.0003504762,0.9076404,0.002794851,0.08739936,0.000547755],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6915043,0.0007376553,0.3005749,0.0000636461,0.003311567,0.0003734326,0.0005011229,0.001073415,0.001859943],"genre_scores_gemma":[0.9647385,0.0004634296,0.03075943,0.0004585203,0.0005573508,0.0006669997,0.0004984857,0.0002375977,0.001619629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2732342,"threshold_uncertainty_score":0.9999707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009764612156565173,"score_gpt":0.234649874019326,"score_spread":0.2248852618627609,"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."}}