{"id":"W4387358639","doi":"10.1016/j.micromeso.2023.112841","title":"Antibiotic entrapment in antibacterial micelles as a novel strategy for the delivery of challenging antibiotics from silica nanoparticles","year":2023,"lang":"en","type":"article","venue":"Microporous and Mesoporous Materials","topic":"Mesoporous Materials and Catalysis","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Directorate for Biological Sciences; CMC Microsystems","keywords":"Micelle; Mesoporous silica; Drug delivery; Chemical engineering; Ammonium bromide; Biocompatibility; Nanoparticle; Chemistry; Antibacterial agent; Materials science; Nanotechnology; Mesoporous material; Antibiotics; Organic chemistry; Pulmonary surfactant; Aqueous solution; Catalysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001413529,0.0004289392,0.0009249244,0.0001732024,0.0002988515,0.0003089039,0.0004647848,0.0001933668,0.0004212811],"category_scores_gemma":[0.00007235777,0.0003220178,0.0001134617,0.0002576339,0.0003029681,0.0001764481,0.000260754,0.00006650041,0.00007353102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004350704,"about_ca_system_score_gemma":0.0001139522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004134495,"about_ca_topic_score_gemma":0.0002127647,"domain_scores_codex":[0.9968361,0.0001357756,0.001177744,0.0007348065,0.0003188979,0.0007967246],"domain_scores_gemma":[0.9983765,0.0003546621,0.0004974415,0.0005501083,0.0001049716,0.0001163338],"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":[0.0003568873,0.0002093564,0.0001782498,0.0002382043,0.00006437374,0.00002818674,0.001244038,0.0001053727,0.9958414,0.000449552,0.0001443143,0.001140032],"study_design_scores_gemma":[0.001466138,0.0002084715,0.004211322,0.0001497541,0.0001411476,0.00002472463,0.001918481,0.00008786152,0.9905905,0.0003859018,0.0004414022,0.0003743229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950052,0.001315864,0.00003862761,0.000240062,0.001163147,0.0009459734,0.00114924,0.000118314,0.00002360197],"genre_scores_gemma":[0.9968404,0.002008584,0.0003989099,0.00007937657,0.0002758986,0.00001479938,0.0002232571,0.00006786038,0.0000909723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00525096,"threshold_uncertainty_score":0.9999232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231144639053738,"score_gpt":0.2532973766243295,"score_spread":0.2301829127189557,"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."}}