{"id":"W3214755665","doi":"10.1039/d1lc00879j","title":"Immuno-biosensor on a chip: a self-powered microfluidic-based electrochemical biosensing platform for point-of-care quantification of proteins","year":2021,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canada Research Chairs; Canadian Institutes of Health Research; University of Calgary; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Biosensor; Microfluidics; Fluidics; Nanotechnology; Bioanalysis; Lab-on-a-chip; Chip; Electrochemistry; Materials science; Optofluidics; Chemistry; Computer science; Electrode; Electrical engineering; Engineering","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":[0.00008960404,0.0002055965,0.0003219551,0.0001234605,0.00005486934,0.00002244132,0.00008120626,0.000193271,0.00001076666],"category_scores_gemma":[0.0001197472,0.0001984881,0.0002145021,0.0003299028,0.0000338714,0.00003585124,0.00000937574,0.000211002,0.000009260752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009606731,"about_ca_system_score_gemma":0.00004510808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005377589,"about_ca_topic_score_gemma":0.000004582221,"domain_scores_codex":[0.9988676,0.00002211379,0.0003824446,0.0002774675,0.000177174,0.0002731472],"domain_scores_gemma":[0.9992226,0.0001184823,0.00008827099,0.0003396345,0.0001711604,0.00005980666],"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":[0.0002504094,0.0001442697,0.000006850839,0.0004245844,0.0000643397,0.000002022123,0.0001343896,0.00003312837,0.995656,0.001048599,0.00005880783,0.002176571],"study_design_scores_gemma":[0.0008377904,0.0004076258,0.0002502802,0.0002977697,0.00003716982,0.000004302309,0.00008525595,0.008394195,0.9884462,0.0001177179,0.0009175969,0.0002040822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942735,0.000748273,0.003218283,0.0002168304,0.000167259,0.0005410563,0.00006920458,0.0002243135,0.000541309],"genre_scores_gemma":[0.9958393,0.00006929266,0.003768999,0.0000772924,0.00007085155,0.00001633664,0.00008826797,0.00004742758,0.00002223901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008361066,"threshold_uncertainty_score":0.8094108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183506932375496,"score_gpt":0.2254072663093724,"score_spread":0.2135721969856174,"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."}}