{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001943289,0.0004819859,0.0002764071,0.000496938,0.0003331587,0.0003863889,0.000298915,0.0004780067,0.0008762843],"category_scores_gemma":[0.0001690032,0.0001866175,0.0004895772,0.0003480323,0.0001415226,0.0002354908,0.000313684,0.00033989,0.0003422564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003714205,"about_ca_system_score_gemma":0.000302202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003496437,"about_ca_topic_score_gemma":0.006720738,"domain_scores_codex":[0.9998146,0.00001934044,0.00001607568,0.00002504028,0.00007758942,0.00004737368],"domain_scores_gemma":[0.9999468,0.000007030456,0.000007228325,0.000004225293,0.00001597096,0.00001862492],"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":[0.00006782709,0.00003479283,0.00007543033,0.0001077146,0.000009794527,0.00006135987,0.00002554749,0.0001243949,0.9953654,0.00006775939,0.00006369219,0.003996135],"study_design_scores_gemma":[0.000008009533,0.0001513023,0.0006873693,0.000006287475,0.00002096556,0.00007231516,0.00002590848,0.001502526,0.9956889,0.00001832603,0.001806473,0.00001164971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858332,0.003714554,0.007284705,0.0002028678,0.0001039098,0.00005516348,0.0001512637,0.0001639887,0.002490351],"genre_scores_gemma":[0.9909759,0.00144269,0.003391432,0.0000401482,0.000009637121,0.00001684545,0.0001066764,0.00001590883,0.004000782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003496437,"threshold_uncertainty_score":0.006952167,"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."}}