{"id":"W4408287282","doi":"10.2139/ssrn.5172873","title":"Bio-Inspired Graphene Oxide Sponges for Enhanced Adsorption of Legacy and Emerging Contaminants from Water","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Graphene and Nanomaterials Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Graphene; Adsorption; Oxide; Nanotechnology; Contaminated water; Contamination; Sponge; Materials science; Water contamination; Chemical engineering; Chemistry; Environmental chemistry; Metallurgy; Geology; Ecology; Biology; Engineering; Organic chemistry; Paleontology","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":[0.0001237104,0.0003202088,0.0002514297,0.0001783908,0.0001407484,0.0003231249,0.0002188994,0.0004771538,0.001655556],"category_scores_gemma":[0.0001858573,0.0001655377,0.0003211195,0.0001318563,0.0002097827,0.0003105794,0.0003953409,0.0004321894,0.0005267424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829419,"about_ca_system_score_gemma":0.0001181037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003911656,"about_ca_topic_score_gemma":0.001122233,"domain_scores_codex":[0.9999237,0.000008285954,0.000003572394,0.00001485724,0.0000267052,0.00002281637],"domain_scores_gemma":[0.9999281,0.00002511297,0.00001167928,0.000007394634,0.00001169308,0.00001597355],"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":[0.00006356871,0.00002396928,0.00006874007,0.0001276951,0.00001027763,0.00008353894,0.00002004581,0.0003259562,0.9957814,0.0002574243,0.0003220137,0.002915199],"study_design_scores_gemma":[0.00001366619,0.0001527912,0.000499017,0.000006257231,0.00001422515,0.00006675379,0.00002730957,0.004112307,0.9924154,0.0001215749,0.002559353,0.0000112636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976445,0.002994031,0.009344225,0.0004670631,0.0003625192,0.00005142392,0.0004224165,0.000332931,0.00958046],"genre_scores_gemma":[0.9912598,0.001126782,0.003506804,0.0001687734,0.00003423768,0.0000220189,0.0001454701,0.00003623075,0.00369978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001655556,"threshold_uncertainty_score":0.005538344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006845027141205737,"score_gpt":0.2276624042394557,"score_spread":0.22081737709825,"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."}}