{"id":"W4408498844","doi":"10.1021/acs.nanolett.5c00270","title":"Automated Electroosmotic Digital Optofluidics for Rapid and Label-Free Protein Detection","year":2025,"lang":"en","type":"article","venue":"Nano Letters","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Natural Science Foundation of Chongqing; Beijing Municipal Natural Science Foundation; Beijing Institute of Technology; National Natural Science Foundation of China; China Association for Science and Technology","keywords":"Optofluidics; Nanotechnology; Chemistry; Microfluidics; Computer science; Materials science; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00007154624,0.0001398982,0.000129498,0.0001613882,0.000082251,0.00007522533,0.0001483704,0.0001106885,7.035918e-7],"category_scores_gemma":[0.0001215688,0.0001449192,0.00003139516,0.0002297737,0.00004441636,0.00009973315,0.0000328616,0.0001099791,0.000002324093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008549436,"about_ca_system_score_gemma":0.00001141396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002469669,"about_ca_topic_score_gemma":9.993063e-7,"domain_scores_codex":[0.9993228,0.000006245838,0.0001416607,0.0001701284,0.00005185148,0.0003073025],"domain_scores_gemma":[0.9996977,0.00004002508,0.00001666381,0.0002088268,0.00001815299,0.00001857634],"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":[0.000008517174,0.000006122318,0.00001608432,0.00008816727,0.00004115291,9.552793e-7,0.00001240458,0.000004769573,0.9242789,0.00009991869,0.01553281,0.05991027],"study_design_scores_gemma":[0.0006257661,0.00007883982,0.00004074499,0.0000495005,0.00001652848,0.000005834255,0.000009575588,0.0007922122,0.9860983,0.0003220492,0.01181601,0.0001445577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631373,0.002506344,0.02749529,0.00179457,0.0001361651,0.0004341155,0.00001039561,0.004334805,0.0001509939],"genre_scores_gemma":[0.998413,0.00007549204,0.001022624,0.000234511,0.00002141479,0.0001074636,0.00000690535,0.00002585169,0.00009271468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06181955,"threshold_uncertainty_score":0.5909634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003575476828388195,"score_gpt":0.1921626341728928,"score_spread":0.1885871573445046,"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."}}