{"id":"W2512104978","doi":"10.1002/anie.201606603","title":"Profiling Metal Oxides with Lipids: Magnetic Liposomal Nanoparticles Displaying DNA and Proteins","year":2016,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Liposome; Calcein; Lipid bilayer; Chemistry; Nanoparticle; Biomolecule; Biosensor; Nanomedicine; Membrane; Chemical engineering; Nanotechnology; Materials science; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001288581,0.0003989793,0.000141367,0.000267323,0.0001385933,0.0003460866,0.0002011539,0.0002911074,0.000431918],"category_scores_gemma":[0.0001866914,0.000163902,0.0001421891,0.0001621262,0.0001689835,0.0002541654,0.0002811781,0.0001856508,0.0002944171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003356092,"about_ca_system_score_gemma":0.000197014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006198505,"about_ca_topic_score_gemma":0.001112193,"domain_scores_codex":[0.9999002,0.00001492499,0.000007280545,0.00002595942,0.00003258038,0.00001908173],"domain_scores_gemma":[0.9999415,0.00001035775,0.0000187257,0.000003900305,0.00001617554,0.00000935556],"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":[0.00001406189,0.000002217568,0.00008139675,0.00001325996,0.00000147968,0.000007517247,0.000005376679,0.0000199051,0.9993886,0.00001349209,0.000005419935,0.000447349],"study_design_scores_gemma":[0.000001991177,0.00002903508,0.0003689678,0.000001759501,0.000006009256,0.00003708229,0.00001034856,0.0004587105,0.9986283,0.00001104181,0.0004453006,0.000001582719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830357,0.0009547376,0.01426872,0.00007218909,0.0000178026,0.00008088713,0.0002211632,0.000115439,0.001233367],"genre_scores_gemma":[0.9779559,0.001010448,0.01775828,0.00006214794,0.000007840171,0.00007073395,0.0003144689,0.00003298956,0.002787078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006198505,"threshold_uncertainty_score":0.002434969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007831255275267065,"score_gpt":0.23889851369587,"score_spread":0.2310672584206029,"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."}}