{"id":"W4281637994","doi":"10.3390/nano12111912","title":"Improving the Stability of Lithium Aluminum Germanium Phosphate with Lithium Metal by Interface Engineering","year":2022,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"British Columbia Knowledge Development Fund; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Mitacs","keywords":"Lithium (medication); Germanium; Materials science; Lithium metal; Phosphate; Aluminium; Metal; Interface (matter); Inorganic chemistry; Chemical engineering; Engineering physics; Metallurgy; Composite material; Chemistry; Engineering; Silicon; Physical chemistry; Organic chemistry","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":[0.00007634088,0.0003073326,0.0001345803,0.0002034731,0.0001275449,0.0003014955,0.0003548777,0.0002795841,0.0006696357],"category_scores_gemma":[0.0001565057,0.0001321249,0.0001678453,0.0001959936,0.0001151599,0.0003758412,0.0003476279,0.0002530241,0.000263291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002031579,"about_ca_system_score_gemma":0.0001482572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000342577,"about_ca_topic_score_gemma":0.0008739404,"domain_scores_codex":[0.999938,0.00000410427,0.000004803768,0.00001226506,0.00002664532,0.00001409738],"domain_scores_gemma":[0.9999708,0.000005197051,0.000008541509,0.000002508707,0.000008538877,0.000004279897],"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":[0.0000252269,0.0000108745,0.0002145295,0.0001020306,0.00000512392,0.00006023555,0.00001406129,0.0002406019,0.9937296,0.0003003637,0.00007504562,0.005222405],"study_design_scores_gemma":[0.000003730225,0.00004492875,0.000281826,0.000003102855,0.000006599745,0.00005319336,0.00001376387,0.002038365,0.9954677,0.00005793578,0.002025103,0.000003827479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808695,0.002179985,0.01314019,0.0001378512,0.00005954007,0.00002327728,0.0001384565,0.0002207971,0.003230365],"genre_scores_gemma":[0.9907453,0.0009269109,0.00677146,0.00003539861,0.000008654425,0.00002455032,0.0001248772,0.00002782032,0.001335035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006696357,"threshold_uncertainty_score":0.002240121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00785623508047832,"score_gpt":0.2051367804965432,"score_spread":0.1972805454160649,"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."}}