{"id":"W2769824046","doi":"10.1039/c7lc00675f","title":"Geo-material surface modification of microchips using layer-by-layer (LbL) assembly for subsurface energy and environmental applications","year":2017,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; CMC Microsystems; University of Calgary","keywords":"Microfluidics; Surface modification; Layer (electronics); Layer by layer; Nanotechnology; Materials science; Engineering; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001931504,0.0003238277,0.0001725614,0.0001804009,0.000142274,0.0002884235,0.0003076398,0.0002563303,0.0006242174],"category_scores_gemma":[0.0002482171,0.0002039078,0.0002006163,0.0001192588,0.0002289603,0.0002643182,0.0003129826,0.0003569225,0.0004453634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668376,"about_ca_system_score_gemma":0.0001660385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003520586,"about_ca_topic_score_gemma":0.001031267,"domain_scores_codex":[0.9998481,0.00002194264,0.000009891153,0.00003395957,0.0000602369,0.00002580526],"domain_scores_gemma":[0.9998806,0.00003186063,0.00004234208,0.00002060837,0.00001584982,0.00000881654],"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":[0.000004770804,0.000005490365,0.00005331662,0.000023157,0.000001902215,0.00001486769,0.000009657574,0.00009992874,0.9975689,0.0001071482,0.00003832321,0.002072469],"study_design_scores_gemma":[0.000001225888,0.00002970874,0.0003050545,0.000001059575,0.000003112335,0.00003730531,0.000004819503,0.001556748,0.9964969,0.00001621077,0.001545331,0.000002635438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.820835,0.002459143,0.1703676,0.0003966799,0.0001224224,0.00009931118,0.0002773794,0.001076996,0.004365397],"genre_scores_gemma":[0.9052153,0.0009030727,0.09051444,0.0001213592,0.00002303084,0.00006334015,0.0001665047,0.00005463956,0.002938443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006242174,"threshold_uncertainty_score":0.002088189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04664207115271388,"score_gpt":0.2815852685246633,"score_spread":0.2349431973719494,"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."}}