{"id":"W2486459361","doi":"10.1039/c6cc04970b","title":"DNA micelles as nanoreactors: efficient DNA functionalization with hydrophobic organic molecules","year":2016,"lang":"en","type":"article","venue":"Chemical Communications","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Qatar National Research Fund; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Nanoreactor; DNA; Micelle; Oligonucleotide; Nanopore; Surface modification; Conjugated system; Chemistry; Polymer; Molecule; Combinatorial chemistry; Drug delivery; DNA nanotechnology; Reactivity (psychology); Nanotechnology; Biophysics; Materials science; Organic chemistry; Nanoparticle; Biochemistry; Biology","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":[0.0004065777,0.0006754482,0.0003164209,0.0002846045,0.0001622832,0.0004365932,0.0004120417,0.0006019182,0.0007049079],"category_scores_gemma":[0.0004262515,0.0003253766,0.0003421618,0.0001334681,0.0002508532,0.0004987897,0.0003886883,0.0005267469,0.0008349263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003653608,"about_ca_system_score_gemma":0.0003242154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003632315,"about_ca_topic_score_gemma":0.0006011996,"domain_scores_codex":[0.9997043,0.00006534933,0.00002899063,0.0000717269,0.00007776843,0.00005181502],"domain_scores_gemma":[0.999797,0.0000471613,0.00005657074,0.00001339742,0.00004757908,0.00003826085],"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":[0.0000156204,0.00001129641,0.00001337519,0.00003888925,0.000003215518,0.00002250952,0.00001667409,0.00008111184,0.9977024,0.0001616722,0.00004199888,0.001891196],"study_design_scores_gemma":[0.00001146886,0.00006034418,0.00003161753,0.000002496223,0.000003827979,0.00003387754,0.000002335391,0.0005785908,0.9973024,0.00002934186,0.001939706,0.000004065942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8352171,0.01985024,0.1337372,0.001317968,0.0004496074,0.0004980917,0.0003852447,0.001449606,0.007095035],"genre_scores_gemma":[0.9144689,0.006414843,0.06673166,0.000479162,0.0001218905,0.0002620076,0.0002931764,0.0002430276,0.01098534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007049079,"threshold_uncertainty_score":0.002650917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00881099915439716,"score_gpt":0.2462079832003748,"score_spread":0.2373969840459777,"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."}}