{"id":"W2762733455","doi":"10.1016/j.biortech.2017.10.039","title":"An insight into the adsorption of diclofenac on different biochars: Mechanisms, surface chemistry, and thermodynamics","year":2017,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":268,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des relations internationales et de la Francophonie","keywords":"Biochar; Adsorption; Chemistry; Manure; Zeta potential; Kinetics; Chicken manure; Particle size; Chemical engineering; Specific surface area; Environmental chemistry; Nuclear chemistry; Fertilizer; Pyrolysis; Nanoparticle; Organic chemistry; Catalysis; Physical chemistry; Agronomy","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":[0.0005059849,0.0006390431,0.0007428596,0.0006287498,0.0004120311,0.0009526631,0.0007518518,0.0007792499,0.001872067],"category_scores_gemma":[0.0003775681,0.0004852522,0.0008245277,0.0003330082,0.0007190672,0.001378763,0.0003829605,0.001374668,0.0005054289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008491922,"about_ca_system_score_gemma":0.000628633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001976275,"about_ca_topic_score_gemma":0.002605022,"domain_scores_codex":[0.9997211,0.00003069439,0.00001635848,0.00004385852,0.0001233288,0.00006467679],"domain_scores_gemma":[0.9998763,0.00005694471,0.0000123959,0.00001516049,0.00002967238,0.00000948836],"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":[0.0001078639,0.00007115652,0.0008709274,0.0005272773,0.0000495952,0.0001410965,0.0001079869,0.002367273,0.9690364,0.009979753,0.0003161777,0.01642447],"study_design_scores_gemma":[0.00001682516,0.0003109524,0.004852673,0.00004727301,0.00007625001,0.0005328011,0.0002454126,0.02157709,0.9521704,0.01029302,0.009822545,0.00005465622],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7488065,0.06277033,0.1623662,0.004925947,0.0004737813,0.0001577181,0.001029394,0.0003711014,0.01909902],"genre_scores_gemma":[0.9581742,0.01877001,0.01564839,0.0005160777,0.0001089642,0.00003741465,0.0003655826,0.00004508942,0.006334261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001976275,"threshold_uncertainty_score":0.00626266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422163048996781,"score_gpt":0.2665450545512203,"score_spread":0.2523234240612525,"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."}}