{"id":"W2990337880","doi":"10.1039/c9ra08618h","title":"<i>Centella asiatica</i> phenolic extract-mediated bio-fabrication of silver nanoparticles: characterization, reduction of industrially relevant dyes in water and antimicrobial activities against foodborne pathogens","year":2019,"lang":"en","type":"article","venue":"RSC Advances","topic":"Medicinal Plants and Neuroprotection","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"","keywords":"Silver nanoparticle; Fourier transform infrared spectroscopy; Zeta potential; Dynamic light scattering; Silver nitrate; Chemistry; Absorption (acoustics); Nuclear chemistry; Centella; Analytical Chemistry (journal); Nanoparticle; Materials science; Chemical engineering; Nanotechnology; Chromatography","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":[0.0001888864,0.0001199752,0.0003278558,0.0001663427,0.0000242011,0.000006244876,0.00003603005,0.0001064832,0.0000288033],"category_scores_gemma":[0.0001203912,0.00008832134,0.00002791686,0.0001968438,0.00009197746,0.0002629112,0.00001624786,0.0001631158,0.000004856536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002372231,"about_ca_system_score_gemma":0.00003976658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001012538,"about_ca_topic_score_gemma":0.000003071231,"domain_scores_codex":[0.9989219,0.00006489235,0.0004364132,0.0002224409,0.0001916054,0.0001627979],"domain_scores_gemma":[0.9994231,0.00005338033,0.0002433513,0.0001429918,0.00009063206,0.00004654763],"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":[0.0005483994,0.0001715298,0.008292662,0.0002443974,0.00001584984,0.000002817757,0.0006521545,0.00001657566,0.9757847,0.000007950665,0.000007148318,0.01425578],"study_design_scores_gemma":[0.00134062,0.0004021173,0.02452749,0.0003680506,0.00003664594,0.00002464602,0.0003375712,0.000156444,0.9718278,0.00001373915,0.000871359,0.00009349867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985301,0.0001173997,0.00005860317,0.0003259784,0.0002746,0.0005560907,0.00002589261,0.00001894445,0.00009232189],"genre_scores_gemma":[0.9979389,0.00156164,0.0001082852,0.00006030772,0.00009825621,0.000007748535,0.0001382797,0.00001432227,0.00007224091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01623483,"threshold_uncertainty_score":0.360164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206663172519707,"score_gpt":0.2286844074455135,"score_spread":0.2166177757203164,"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."}}