{"id":"W4290787314","doi":"10.1021/acs.nanolett.2c01969","title":"Utilizing Gradient Porous Graphene Substrate as the Solid-Contact Layer To Enhance Wearable Electrochemical Sweat Sensor Sensitivity","year":2022,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Research Grants Council, University Grants Committee; Hong Kong Government; National Natural Science Foundation of China","keywords":"Graphene; Materials science; Substrate (aquarium); Layer (electronics); Nanotechnology; Wearable computer; Porosity; Sensitivity (control systems); Electrochemistry; Optoelectronics; Wearable technology; Chemical engineering; Electrode; Chemistry; Composite material; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002869156,0.0002539516,0.0002618635,0.00007083697,0.0003370921,0.00006037374,0.0001666982,0.00004082738,0.0000688443],"category_scores_gemma":[0.00004094275,0.0002307241,0.00008669667,0.0002811806,0.00002799726,0.00009557007,0.00006706409,0.0002569967,0.00004780233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724963,"about_ca_system_score_gemma":0.00001127191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001545005,"about_ca_topic_score_gemma":0.00002722308,"domain_scores_codex":[0.9983683,0.000127359,0.0002480313,0.0003377015,0.0002730949,0.0006455604],"domain_scores_gemma":[0.9993623,0.00012287,0.00004403601,0.0003383599,0.00002054611,0.0001118564],"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":[0.00003988275,0.00001238376,0.00004068542,0.00001190786,0.00003174433,0.00008783265,0.0002370211,0.09948463,0.8992255,0.0000493851,0.0005966079,0.0001823559],"study_design_scores_gemma":[0.0001313533,0.00004703699,0.000266055,0.00001728671,0.00001720251,0.0001484641,0.0001341632,0.0004379379,0.9952177,0.00004182648,0.003228307,0.0003126682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955726,0.00008556151,0.001663003,0.0009522026,0.0005826739,0.0001759194,0.00001118734,0.0003676696,0.000589155],"genre_scores_gemma":[0.9961445,0.00002035305,0.0005660671,0.002844735,0.0001528003,0.00006186709,0.000009998595,0.00006317982,0.0001365399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09904669,"threshold_uncertainty_score":0.9408658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015572621904981,"score_gpt":0.2354904667029095,"score_spread":0.2253347404838597,"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."}}