{"id":"W7116955540","doi":"10.3390/min16010014","title":"Transforming Bentonite into High Sorption Capacity Organoclays for Gasoline, Diesel, and Kerosene","year":2025,"lang":"en","type":"article","venue":"Minerals","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Sorption; Thermogravimetric analysis; Bentonite; Swelling; Kerosene; Swelling capacity; Hydrocarbon; Specific surface area; Fourier transform infrared spectroscopy","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":[0.0001764799,0.0006847265,0.0002529489,0.000316325,0.0001259165,0.000266296,0.0002763594,0.000340562,0.0005453229],"category_scores_gemma":[0.0002351466,0.0002382092,0.0004304504,0.0003680577,0.0001536601,0.0003242602,0.0002584424,0.0004758528,0.0002621863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002112227,"about_ca_system_score_gemma":0.0001836332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222491,"about_ca_topic_score_gemma":0.004140552,"domain_scores_codex":[0.999828,0.00001495937,0.00002375705,0.00003809179,0.00005060574,0.00004453916],"domain_scores_gemma":[0.9998726,0.00001300968,0.00003941294,0.00000948353,0.00004209104,0.00002339584],"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":[0.0000201963,0.00001452182,0.0000624392,0.00004030394,0.000003844745,0.00002673913,0.000009774318,0.00008794655,0.9992099,0.00001883517,0.000006299968,0.0004991767],"study_design_scores_gemma":[0.00000554474,0.0001561151,0.001158505,0.000005744498,0.00001437609,0.00003795956,0.00002557649,0.0006138076,0.9967422,0.00001352309,0.001219663,0.000006932677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925881,0.0007002826,0.0052906,0.00003544714,0.00003555813,0.00008212755,0.0002282884,0.00008480538,0.0009548201],"genre_scores_gemma":[0.9863599,0.0008748206,0.009784813,0.00003465144,0.000007005495,0.00007470761,0.0004300594,0.00006195658,0.002371985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001222491,"threshold_uncertainty_score":0.002430737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043250691298106,"score_gpt":0.2377413588090828,"score_spread":0.2273088518961018,"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."}}