{"id":"W2531715055","doi":"10.1063/1.4964717","title":"Surface micromachining of polydimethylsiloxane for microfluidics applications","year":2016,"lang":"en","type":"article","venue":"Biomicrofluidics","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Electrical, Communications and Cyber Systems; National Institute of Allergy and Infectious Diseases; National Heart, Lung, and Blood Institute; New York University; Division of Chemical, Bioengineering, Environmental, and Transport Systems; York University; American Heart Association; Division of Civil, Mechanical and Manufacturing Innovation; National Institutes of Health; National Science Foundation","keywords":"Polydimethylsiloxane; Photoresist; Materials science; Surface micromachining; Reactive-ion etching; Photolithography; Microfluidics; Nanotechnology; Etching (microfabrication); Plasma etching; Optoelectronics; Fabrication; Layer (electronics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003754345,0.0006584542,0.0002980292,0.0004081595,0.0002540813,0.0003191875,0.0003500726,0.0002365716,0.003667007],"category_scores_gemma":[0.0004883526,0.0003087111,0.0003539275,0.000259336,0.0002027067,0.0002569484,0.0002654532,0.0004752251,0.001740955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004412048,"about_ca_system_score_gemma":0.0005240871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005712683,"about_ca_topic_score_gemma":0.001578014,"domain_scores_codex":[0.9996243,0.00003402548,0.00003072434,0.00007893497,0.0001921678,0.00003984984],"domain_scores_gemma":[0.9997134,0.00007217177,0.00004653414,0.00005408768,0.00009503154,0.00001890313],"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":[0.000019503,0.000008300916,0.000101823,0.0001487601,0.000006416464,0.00004025356,0.00001992578,0.0003495751,0.9823503,0.0005535486,0.0004287422,0.01597272],"study_design_scores_gemma":[0.000006565831,0.00008275515,0.0008416717,0.00001155411,0.00000863548,0.0001099224,0.000007506235,0.002916256,0.9737133,0.0001591519,0.02213307,0.000009621703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2307754,0.02021798,0.6985823,0.0007855078,0.001841229,0.0007271512,0.002598491,0.005033897,0.03943805],"genre_scores_gemma":[0.5633929,0.006779292,0.4108662,0.0004073672,0.0002006603,0.0003848053,0.001907016,0.0002525642,0.01580933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003667007,"threshold_uncertainty_score":0.01226735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101405265012655,"score_gpt":0.2242395459822749,"score_spread":0.2140990194810094,"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."}}