{"id":"W4389593741","doi":"10.1039/d3su00365e","title":"Spent coffee ground–calcium alginate biosorbent for adsorptive removal of methylene blue from aqueous solutions","year":2023,"lang":"en","type":"article","venue":"RSC Sustainability","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional; Consejo Nacional de Ciencia y Tecnología","keywords":"Methylene blue; Calcium alginate; Biocomposite; Adsorption; Aqueous solution; Coffee grounds; Chemistry; Nuclear chemistry; Pulp and paper industry; Waste management; Environmental chemistry; Chemical engineering; Calcium; Materials science; Food science; Organic chemistry; Composite number; Catalysis; Composite material; Photocatalysis; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001064817,0.000295828,0.0004037491,0.0001200827,0.0002785647,0.00003186091,0.0003837075,0.0001851129,0.001602128],"category_scores_gemma":[0.0005232969,0.0002698029,0.0003734489,0.0008295033,0.0006763723,0.0002795637,0.000465303,0.0001814799,0.0003379674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236091,"about_ca_system_score_gemma":0.0001025907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001883044,"about_ca_topic_score_gemma":0.0001485221,"domain_scores_codex":[0.9971427,0.0002111013,0.0005946172,0.000748455,0.0005414219,0.0007617049],"domain_scores_gemma":[0.9984825,0.0002940318,0.0002428081,0.0006200434,0.000133057,0.0002275394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002789572,0.003722632,0.01455769,0.0006389701,0.0005973225,0.0003769849,0.008747959,0.009502141,0.7259881,0.0393743,0.02420535,0.169499],"study_design_scores_gemma":[0.005435077,0.001328654,0.3022536,0.00006877378,0.0003920649,0.0001119427,0.01664558,0.04588968,0.1050681,0.2921819,0.2284592,0.002165435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969827,0.00008024707,0.0232803,0.003390817,0.0003789501,0.001439746,0.0005237807,0.0002713453,0.0008078382],"genre_scores_gemma":[0.994417,0.00001912614,0.001487081,0.0001071623,0.00008772796,0.00007461121,0.0001271677,0.00002963863,0.003650545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.62092,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03183614824063841,"score_gpt":0.287109265819529,"score_spread":0.2552731175788906,"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."}}