{"id":"W2897724776","doi":"10.1039/c8mh01126e","title":"Etching silver nanoparticles using DNA","year":2018,"lang":"en","type":"article","venue":"Materials Horizons","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Ostwald ripening; Nanoparticle; Etching (microfabrication); Materials science; DNA; Nanotechnology; Silver nanoparticle; Chemical engineering; Chemistry; Biochemistry; Layer (electronics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017505,0.0001320574,0.0001411484,0.00003490492,0.0001647086,0.00005437941,0.0001095675,0.0001045681,0.00002018278],"category_scores_gemma":[0.00004815559,0.0001128562,0.00005005252,0.00007929633,0.0001411818,0.000004704518,0.0001058646,0.00002508583,0.00001576636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001115301,"about_ca_system_score_gemma":0.00002182563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002601378,"about_ca_topic_score_gemma":0.00002089182,"domain_scores_codex":[0.9991639,0.00006099305,0.0001841909,0.0002739601,0.00008200592,0.0002349501],"domain_scores_gemma":[0.9995077,0.000003792637,0.00007393207,0.0002933358,0.00007171087,0.00004958947],"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":[0.0000360519,0.0000190221,0.0000674508,0.000003404129,0.00002412128,0.000001741812,0.00001278643,4.808332e-7,0.9986534,0.00004930793,0.0003112429,0.0008210568],"study_design_scores_gemma":[0.00007300563,0.0001985017,0.00008549618,0.00001373264,0.00002825598,0.00001504431,0.00002558964,0.00001086964,0.9925504,0.0001587896,0.006675045,0.0001652519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984667,0.00002826674,0.0008105208,0.00005522001,0.0002117504,0.00006947057,0.00002441045,0.00005996182,0.0002737202],"genre_scores_gemma":[0.9890646,0.00001636891,0.009814647,0.0001370473,0.0008038752,0.000002747378,0.00002983528,0.00002134929,0.0001095603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00940211,"threshold_uncertainty_score":0.4602141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0155340387120345,"score_gpt":0.2856609665286063,"score_spread":0.2701269278165718,"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."}}