{"id":"W2101486354","doi":"10.1116/1.4893075","title":"Programmed self-assembly of microscale components using biomolecular recognition through the avidin–biotin interaction","year":2014,"lang":"en","type":"article","venue":"Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Alberta Innovates; University of Alberta","keywords":"Avidin; Nanotechnology; Microscale chemistry; Substrate (aquarium); Materials science; Self-assembled monolayer; Silicon; Monolayer; Optoelectronics; Biotin; Chemistry","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":[0.0001425994,0.0002791431,0.0001502019,0.0001763517,0.0001334092,0.0003592089,0.0003275934,0.0002877489,0.0007073157],"category_scores_gemma":[0.0001997398,0.0002188889,0.0002254101,0.0001360696,0.0002910253,0.0002819228,0.0003229355,0.0003481718,0.0004749792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002618741,"about_ca_system_score_gemma":0.0001625869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004192992,"about_ca_topic_score_gemma":0.001066363,"domain_scores_codex":[0.9998461,0.00001494577,0.00001027011,0.00004065812,0.00005498888,0.00003306134],"domain_scores_gemma":[0.9998955,0.00002214111,0.00003225884,0.00001606861,0.00001668085,0.00001737269],"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":[0.00000794381,0.000004500052,0.00005330692,0.00001630054,0.000002492689,0.000014769,0.0000122967,0.0000649593,0.9982704,0.000119361,0.00002012775,0.001413575],"study_design_scores_gemma":[0.000004152019,0.00006690291,0.0006851942,0.000001842384,0.000003909335,0.00006214344,0.00001127847,0.001249617,0.996365,0.00005332271,0.001492081,0.000004553634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.892665,0.001453047,0.1014757,0.0001918228,0.0001045189,0.0001322476,0.0001174438,0.0005068836,0.003353312],"genre_scores_gemma":[0.9336926,0.000611235,0.06194625,0.0001389487,0.0000186359,0.00009476075,0.0001888906,0.00005024457,0.003258458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007073157,"threshold_uncertainty_score":0.002366245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02432541960370697,"score_gpt":0.2427504268719368,"score_spread":0.2184250072682298,"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."}}