{"id":"W4412804138","doi":"10.1016/j.ifset.2025.104132","title":"Enhancing the stability of quercetin by loading into dual-layered hydrogel-emulsion gel using coaxial 3D food printing","year":2025,"lang":"en","type":"article","venue":"Innovative Food Science & Emerging Technologies","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Coaxial; Emulsion; Dual (grammatical number); Materials science; Composite material; Self-healing hydrogels; Chemical engineering; Polymer chemistry; Mechanical engineering; Engineering","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":[0.0001716465,0.0003270292,0.0001731577,0.0002149023,0.0001359218,0.0003566388,0.0002679343,0.0004216205,0.0006730831],"category_scores_gemma":[0.0002119136,0.0001880467,0.0003899611,0.0001607138,0.000175554,0.0004145044,0.0002988535,0.0004587666,0.0002595227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002868634,"about_ca_system_score_gemma":0.000157686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007225237,"about_ca_topic_score_gemma":0.001221849,"domain_scores_codex":[0.999874,0.00001084727,0.00001033954,0.00003880573,0.00003663846,0.00002933501],"domain_scores_gemma":[0.9998696,0.00003073065,0.000048104,0.00001259701,0.00002432622,0.00001462129],"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":[0.00001442632,0.000006160872,0.00001916566,0.00001434395,0.000001674072,0.00001634008,0.00000739344,0.0001049762,0.9990004,0.00003733956,0.000009788591,0.0007681081],"study_design_scores_gemma":[0.000003053634,0.00003375527,0.0001552275,0.000001345462,0.000004848128,0.00001711762,0.000003504603,0.001091304,0.9983845,0.0000095524,0.0002917296,0.000004033027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691262,0.001134711,0.02716402,0.0001153768,0.00005902341,0.00004293962,0.0002028101,0.00026957,0.001885258],"genre_scores_gemma":[0.9821526,0.0005385045,0.01541626,0.00005821192,0.00001150636,0.0000428244,0.00008526865,0.00004690261,0.00164796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007225237,"threshold_uncertainty_score":0.002251685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02577291866994648,"score_gpt":0.3104490314570806,"score_spread":0.2846761127871341,"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."}}