{"id":"W4406906525","doi":"10.3390/batteries11020051","title":"Sustainable Extraction of Critical Minerals from Waste Batteries: A Green Solvent Approach in Resource Recovery","year":2025,"lang":"en","type":"article","venue":"Batteries","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Collège Shawinigan; University of Alberta; Centre National en Électrochimie et en Technologies Environnementales; University of Calgary; École de Technologie Supérieure; Concordia University","funders":"Concordia University","keywords":"Resource recovery; Extraction (chemistry); Resource (disambiguation); Solvent extraction; Waste management; Environmental science; Natural resource economics; Wastewater; Pulp and paper industry; Environmental economics; Computer science; Chemistry; Environmental engineering; Chromatography; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.000112179,0.0001275897,0.0002106388,0.0001923132,0.00004641186,0.00005648273,0.00008973577,0.00009124784,0.0001825819],"category_scores_gemma":[0.00008400006,0.0001369445,0.0000417856,0.0002397676,0.00006787713,0.0004683202,0.00002794046,0.0001314302,0.000006531435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006067216,"about_ca_system_score_gemma":0.0000304987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002286199,"about_ca_topic_score_gemma":0.0000563912,"domain_scores_codex":[0.999128,0.00003670306,0.0003543075,0.0001722237,0.0001097445,0.0001990511],"domain_scores_gemma":[0.9995366,0.0001735083,0.00003223057,0.0001684194,0.00005974329,0.00002955412],"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":[0.001325606,0.001539819,0.007171706,0.01465301,0.0004209148,0.0001428978,0.01598288,0.07809352,0.5765068,0.01967869,0.2708314,0.01365273],"study_design_scores_gemma":[0.001672376,0.0001809448,0.009725478,0.0005947782,0.0001165113,0.00002256026,0.06154381,0.01891849,0.1848137,0.01099742,0.7103023,0.001111646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8282781,0.001337641,0.05572207,0.002919756,0.0005431797,0.0003696274,0.00005957917,0.0003037206,0.1104663],"genre_scores_gemma":[0.9796184,0.00004580686,0.001015405,0.0002960302,0.00006857186,0.00007566127,0.00004959763,0.00001799852,0.01881257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4394708,"threshold_uncertainty_score":0.5584435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254096519807909,"score_gpt":0.2625637693616008,"score_spread":0.2500228041635217,"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."}}