{"id":"W3111391551","doi":"10.1039/d0ta08743b","title":"High-performance gas sensor array for indoor air quality monitoring: the role of Au nanoparticles on WO<sub>3</sub>, SnO<sub>2</sub>, and NiO-based gas sensors","year":2020,"lang":"en","type":"article","venue":"Journal of Materials Chemistry A","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":293,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Education, Science and Technology; National Research Foundation of Korea; Ministry of Land, Infrastructure and Transport","keywords":"Non-blocking I/O; Materials science; Nanoparticle; Metal; Nanotechnology; Toxic gas; Optoelectronics; Chemical engineering; Catalysis; Chemistry; Metallurgy; Environmental chemistry; Organic chemistry; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000188237,0.0003339206,0.0004197425,0.0001953995,0.0002125119,0.0003659569,0.000448817,0.0007790561,0.0007152659],"category_scores_gemma":[0.0002303105,0.0002681132,0.0002692773,0.0002244074,0.0002749269,0.0005473822,0.0002811658,0.0003089566,0.0003403987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002465505,"about_ca_system_score_gemma":0.0002010394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007045195,"about_ca_topic_score_gemma":0.001906229,"domain_scores_codex":[0.9997829,0.00002032484,0.000009518014,0.00006408722,0.00009358497,0.00002964571],"domain_scores_gemma":[0.9998803,0.00002025742,0.00002663408,0.00001159544,0.0000470073,0.00001430683],"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":[0.00002956077,0.000007894635,0.0001526195,0.0000256814,0.000004204337,0.00001746326,0.0000113941,0.0001021175,0.9977563,0.00003842256,0.00005172135,0.001802561],"study_design_scores_gemma":[0.000002311908,0.00005606504,0.001130673,0.000002011123,0.000007862282,0.00006805137,0.00001929952,0.001949834,0.9962093,0.00001892846,0.0005310753,0.000004745958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9460081,0.001928276,0.04624301,0.000261979,0.0001257224,0.00004976557,0.0002227375,0.0008013279,0.004359063],"genre_scores_gemma":[0.9523162,0.0005211878,0.04462631,0.000171539,0.00002151973,0.00004591295,0.0001228788,0.00004586752,0.002128536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007790561,"threshold_uncertainty_score":0.002392769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322977258273359,"score_gpt":0.2100036338833607,"score_spread":0.1967738613006271,"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."}}