{"id":"W4398206534","doi":"10.18280/acsm.480209","title":"Evaluation of Selective Laser Melted Ti6Al4V/ST316L Composite and Selective Laser Sintered Polyamide 12 Implants for Orthopedic Applications: Finite Element Analysis, Physical and Mechanical Characterization, in Vitro and in Vivo Biocompatibility","year":2024,"lang":"en","type":"article","venue":"Annales de Chimie Science des Matériaux","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biocompatibility; Materials science; Composite number; Laser; Polyamide; Finite element method; In vivo; Biomedical engineering; Characterization (materials science); Composite material; Titanium alloy; Titanium; Nanotechnology; Optics; Metallurgy; Medicine; Structural engineering; Alloy; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003585693,0.0003765083,0.0003225782,0.0004917184,0.0001152722,0.0002539323,0.0002833933,0.0004655651,0.0008396104],"category_scores_gemma":[0.000357956,0.0003222322,0.0004785825,0.000296862,0.0002971443,0.0001839371,0.000189369,0.0001431983,0.0002090638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002443143,"about_ca_system_score_gemma":0.0003562368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004126673,"about_ca_topic_score_gemma":0.001087758,"domain_scores_codex":[0.9996145,0.00001997835,0.00002731248,0.00004498359,0.0002597524,0.00003344746],"domain_scores_gemma":[0.9996939,0.00009490996,0.00011581,0.00002431587,0.00005593907,0.00001517521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008965775,0.00002517389,0.0006086027,0.0000754275,0.000006382738,0.00004937428,0.00002723386,0.003752596,0.9918061,0.00003801123,0.00001017666,0.003511283],"study_design_scores_gemma":[0.00002281115,0.001419174,0.01470361,0.0000152884,0.00006489363,0.0003544748,0.00007950388,0.04180631,0.9402396,0.00006991673,0.001197506,0.00002684947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924097,0.0003494108,0.006722803,0.000009590363,0.00001120022,0.00001543198,0.00005843084,0.00004934684,0.0003741327],"genre_scores_gemma":[0.9887097,0.0002269994,0.01011281,0.000006527705,0.000003563799,0.00003076633,0.00009213149,0.00001617223,0.0008012092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008396104,"threshold_uncertainty_score":0.002808809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.028650622264302,"score_gpt":0.2942578236577786,"score_spread":0.2656072013934766,"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."}}