{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056173,0.0001361166,0.0002335735,0.0004813112,0.00007840662,0.00006888162,0.000097737,0.00004843287,0.000002245374],"category_scores_gemma":[0.0001752796,0.0001263144,0.00002892836,0.0007624313,0.0002714306,0.0002080017,0.00007552722,0.0001252211,3.663629e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331269,"about_ca_system_score_gemma":0.00006041103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002362381,"about_ca_topic_score_gemma":0.0001628447,"domain_scores_codex":[0.9989105,0.00006529428,0.0002282628,0.0003684482,0.0001982288,0.0002292196],"domain_scores_gemma":[0.9994274,0.0002324421,0.00004749289,0.0001183764,0.000128363,0.0000458849],"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":[0.0002433456,0.000266701,0.02608133,0.0006812122,0.0004776705,0.000003312739,0.004467826,0.01520346,0.8468982,0.0003793008,0.00002176525,0.1052758],"study_design_scores_gemma":[0.0001852671,0.00003075446,0.1445852,0.00004773423,0.00008559882,0.000003046681,0.00009446912,0.456866,0.3954097,0.002594546,0.000005996793,0.00009163353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719846,0.0001077143,0.02705181,0.00003859323,0.00001338635,0.0005036975,0.0001940562,0.00009036416,0.0000157559],"genre_scores_gemma":[0.997961,0.00009068098,0.001719716,0.000009008034,0.00001323941,0.0001505203,0.00004505969,0.000009103249,0.000001716306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4514885,"threshold_uncertainty_score":0.5150954,"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."}}