{"id":"W2903018510","doi":"10.1016/j.jcis.2018.11.101","title":"Tailored N-doped porous carbon nanocomposites through MOF self-assembling for Li/Na ion batteries","year":2018,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Materials science; Nanocomposite; Anode; Carbon fibers; Lithium (medication); Chemical engineering; Graphite; Pyrolysis; Battery (electricity); Nanotechnology; Electrochemistry; Lithium-ion battery; Electrode; Porosity; Doping; Energy storage; Composite number; Composite material; Chemistry; Optoelectronics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005935151,0.0002195226,0.0001511879,0.0002070458,0.0001839741,0.0002300933,0.0001815129,0.0002857342,0.0008430069],"category_scores_gemma":[0.0001346214,0.0001377444,0.0001462146,0.00009538098,0.0001411631,0.000259997,0.0001846794,0.0002310108,0.000200907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002982014,"about_ca_system_score_gemma":0.0001315534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009664205,"about_ca_topic_score_gemma":0.004042658,"domain_scores_codex":[0.9999369,0.000003728206,0.00000449716,0.00001571066,0.00002307291,0.00001599394],"domain_scores_gemma":[0.9999381,0.000009577316,0.0000158177,0.000005515568,0.00001605062,0.00001486689],"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":[0.00003547159,0.00001500417,0.00008739524,0.00003871611,0.000004356804,0.00003961696,0.00001618624,0.0001986728,0.9972013,0.0001325212,0.00007736567,0.002153456],"study_design_scores_gemma":[0.000007877541,0.0000498169,0.0009830558,0.0000038404,0.00000758556,0.00004540126,0.0000146484,0.002174341,0.9952057,0.00002661994,0.001473867,0.000007230416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932827,0.0005356377,0.003102373,0.00005614171,0.00005280951,0.00002332102,0.00007761704,0.0001606813,0.002708714],"genre_scores_gemma":[0.996515,0.0001567856,0.002078559,0.00001972701,0.000008969077,0.00001462449,0.00003576068,0.00001964497,0.00115082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009664205,"threshold_uncertainty_score":0.002820194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329830048932656,"score_gpt":0.2764184897325893,"score_spread":0.2631201892432627,"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."}}