{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001766949,0.0004113282,0.0001713709,0.0003098445,0.0001554483,0.0003435889,0.0002747689,0.0006683978,0.001167565],"category_scores_gemma":[0.0004402001,0.0002230518,0.000270619,0.0002316736,0.0003265628,0.0002953049,0.000273336,0.000525,0.0007113575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000438998,"about_ca_system_score_gemma":0.0002707547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008027201,"about_ca_topic_score_gemma":0.001360339,"domain_scores_codex":[0.9997355,0.0000267385,0.00001691309,0.0000828071,0.00009531084,0.00004262198],"domain_scores_gemma":[0.9997197,0.00008437273,0.00008585864,0.00002486961,0.00004931453,0.00003585612],"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":[0.00001460262,0.000004728349,0.00003342782,0.00002347005,0.000001698785,0.00001505435,0.000006594291,0.00005039539,0.9980335,0.00003750217,0.00001800127,0.001761043],"study_design_scores_gemma":[0.000003855731,0.00003377465,0.0001342345,0.000001987999,0.000001824093,0.00005512064,0.000002661202,0.0001958616,0.998906,0.00001100149,0.0006506662,0.000002993434],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9609795,0.002634761,0.0298986,0.0002674848,0.0001142463,0.00008085622,0.0001474177,0.0004056587,0.005471522],"genre_scores_gemma":[0.9714962,0.001074224,0.01901333,0.0002061668,0.00002282741,0.00004305075,0.0002110123,0.00005276152,0.007880447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001167565,"threshold_uncertainty_score":0.003905892,"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."}}