{"id":"W4401521327","doi":"10.1002/admt.202400633","title":"AI‐Assisted Plasmonic Enhanced Colorimetric Fluidic Device for Hydrogen Peroxide Detection from Cancer Cells","year":2024,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal; Consejo Nacional de Ciencia y Tecnología; Canada Research Chairs; McGill University","keywords":"Hydrogen peroxide; Plasmon; Fluidics; Nanotechnology; Cancer detection; Materials science; Optoelectronics; Chemistry; Cancer; Medicine; Engineering; Biochemistry; Electrical engineering; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001526619,0.000358737,0.0004010155,0.0002479755,0.000153566,0.00009719836,0.0003188197,0.000500148,0.000006169827],"category_scores_gemma":[0.0001851336,0.0003132673,0.0001598368,0.0005144592,0.0001599995,0.0000255695,0.0001712489,0.000125971,0.000009045829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001068853,"about_ca_system_score_gemma":0.00006741809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005294033,"about_ca_topic_score_gemma":0.0001033283,"domain_scores_codex":[0.9980981,0.00003639237,0.0004216678,0.0008782686,0.0001394287,0.0004261502],"domain_scores_gemma":[0.9990783,0.00006421313,0.0001490383,0.0005313828,0.0001416045,0.00003550518],"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":[0.0001722459,0.0000243674,0.000001427836,0.00006017913,0.0001475045,0.000003629229,0.000005341425,0.00002347757,0.930459,0.00001550647,0.00021751,0.06886982],"study_design_scores_gemma":[0.0002794704,0.0003030057,0.00001221705,0.0001007605,0.0001338036,0.000004562974,0.0001031798,0.00003701624,0.9791533,0.001221173,0.01824529,0.0004062473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749194,0.006168935,0.01546945,0.0002847953,0.0005997909,0.0005643082,0.0004645594,0.001513405,0.00001532157],"genre_scores_gemma":[0.9822153,0.004368659,0.01221354,0.0001207517,0.0001372748,0.0004799546,0.0002432275,0.00006613333,0.0001551867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06846358,"threshold_uncertainty_score":0.9999319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008906395096859,"score_gpt":0.2879317495560343,"score_spread":0.2778426856050658,"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."}}