{"id":"W4308625022","doi":"10.1021/acsaom.2c00066","title":"Complementary Metal-Oxide-Semiconductor-Based Sensing Platform for Trapping, Imaging, and Chemical Characterization of Biological Samples","year":2022,"lang":"en","type":"article","venue":"ACS Applied Optical Materials","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Faculty of Engineering, McGill University","keywords":"Materials science; Substrate (aquarium); Optoelectronics; Micrometer; Rhodamine 6G; CMOS; Microlens; Layer (electronics); Semiconductor; Microheater; Image sensor; Nanotechnology; Optics; Fabrication; Fluorescence; Lens (geology)","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":[0.0004803696,0.0001533807,0.0003606853,0.0000408757,0.0002208414,0.00006428587,0.0001641211,0.00003691469,0.0005241853],"category_scores_gemma":[0.00002774189,0.000132587,0.00003982979,0.0000718042,0.0001753586,0.00005711933,0.0001517901,0.00004100453,0.000003882212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002732389,"about_ca_system_score_gemma":0.00002388671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001397513,"about_ca_topic_score_gemma":2.698195e-7,"domain_scores_codex":[0.9986928,0.0000322068,0.0004841153,0.0003495779,0.0001623676,0.0002789925],"domain_scores_gemma":[0.9993359,0.0002093716,0.0001709809,0.0001813901,0.00003251042,0.00006986495],"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":[0.0001107628,0.00009220036,0.00005147709,0.00002824198,0.000007931065,3.563647e-7,0.00004293904,0.000002804842,0.9782508,0.02068371,0.00002559796,0.0007031921],"study_design_scores_gemma":[0.0004988288,0.00003704837,0.0006755583,0.000006410126,0.00003649017,0.00000430615,0.000124476,0.00004174836,0.9959199,0.001579475,0.0009103832,0.0001653405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975486,0.00001106703,0.0007417274,0.0003256654,0.00007481506,0.0005324201,0.0006901294,0.00005225269,0.00002333241],"genre_scores_gemma":[0.9929006,0.000003418137,0.006224916,0.0002846026,0.00005476422,0.0001616134,0.0003512986,0.00001677409,0.000001982814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01910424,"threshold_uncertainty_score":0.5739461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04535188350446252,"score_gpt":0.2533059725750453,"score_spread":0.2079540890705828,"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."}}