{"id":"W4224957134","doi":"10.1021/acsanm.2c00694","title":"Laccase-Functionalized Hexagonal Boron Nitride-Coated Sponges for the Removal and Degradation of Anthracene","year":2022,"lang":"en","type":"article","venue":"ACS Applied Nano Materials","topic":"Graphene research and applications","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Brock University; Canada Research Chairs; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ryerson University","keywords":"Anthracene; Degradation (telecommunications); Environmental remediation; Materials science; Sponge; Adsorption; Chemical engineering; Polycyclic aromatic hydrocarbon; Mesoporous material; Melamine; Boron nitride; Chemistry; Organic chemistry; Catalysis; Contamination","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":[0.0001529813,0.0003938295,0.0002892386,0.0001779242,0.0001082561,0.0001612068,0.0002230769,0.0003519169,0.0006364484],"category_scores_gemma":[0.0001601029,0.0001900727,0.000310453,0.0001019049,0.0001550138,0.0002811125,0.000200209,0.0002915835,0.0003250414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001922633,"about_ca_system_score_gemma":0.0001248485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004922051,"about_ca_topic_score_gemma":0.001216999,"domain_scores_codex":[0.9998642,0.0000175242,0.00001375068,0.00002769673,0.00004893821,0.0000278845],"domain_scores_gemma":[0.9998596,0.0000227687,0.00004500998,0.00001511133,0.00002982557,0.00002767558],"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":[0.00001648496,0.000004866827,0.00002936721,0.00002852534,0.000003248592,0.00002507091,0.00000411224,0.0000483377,0.9994205,0.00001692708,0.00001490917,0.0003876837],"study_design_scores_gemma":[0.000003540448,0.00007067822,0.0004522257,0.000001718858,0.000005934148,0.00006042806,0.000006218115,0.001102432,0.9978386,0.000007751791,0.0004450533,0.000005422884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814765,0.001733695,0.01440917,0.0001027893,0.00008153659,0.00003869227,0.0002354322,0.000204017,0.001718121],"genre_scores_gemma":[0.9895258,0.0007721095,0.007527591,0.0000582476,0.00001725265,0.00002668413,0.0001897527,0.00002580918,0.001856825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006364484,"threshold_uncertainty_score":0.002129138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02755027001098174,"score_gpt":0.2754835553405728,"score_spread":0.2479332853295911,"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."}}