{"id":"W4411857102","doi":"10.1038/s41598-025-03359-z","title":"Production and characterization of magnetic Biochar derived from pyrolysis of waste areca nut husk for removal of methylene blue dye from wastewater","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Indian Institute of Technology Bombay; King Saud University; Indian Institute of Science","keywords":"Biochar; BET theory; Pyrolysis; Husk; Thermogravimetric analysis; Nuclear chemistry; Aqueous solution; Fourier transform infrared spectroscopy; Chemistry; Specific surface area; Adsorption; Pulp and paper industry; Materials science; Chemical engineering; Organic chemistry; Botany","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":[0.0001621741,0.0002745327,0.0002630682,0.0003629688,0.0001517571,0.000292478,0.0001431681,0.0002244784,0.000421868],"category_scores_gemma":[0.0001709427,0.0001317391,0.00033268,0.0002976618,0.0001339111,0.0001635465,0.0001375779,0.0002363086,0.000179816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001516466,"about_ca_system_score_gemma":0.0001504669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008305123,"about_ca_topic_score_gemma":0.001912617,"domain_scores_codex":[0.9999024,0.00001218573,0.00001050202,0.00001701604,0.00004358574,0.00001429892],"domain_scores_gemma":[0.9999119,0.00001435654,0.00002311938,0.000008375728,0.00003030892,0.00001190161],"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":[0.00002314354,0.00001158879,0.0001747057,0.00002929842,0.000002636709,0.00001946964,0.000004725545,0.00006608434,0.9990352,0.000007014005,0.000005053993,0.0006211334],"study_design_scores_gemma":[0.000006896513,0.0001923113,0.007770329,0.000006808825,0.00001845974,0.0001019512,0.00002936287,0.0008261784,0.9904474,0.00001502545,0.0005808571,0.000004549496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970626,0.000420854,0.001815458,0.00002292423,0.0000120351,0.00002449959,0.0002101027,0.00002432289,0.0004072377],"genre_scores_gemma":[0.9948143,0.0003618903,0.003462246,0.00001662431,0.000004268691,0.00001930187,0.0003327873,0.00001944298,0.0009692152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008305123,"threshold_uncertainty_score":0.001651406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008590408887075648,"score_gpt":0.2127284247273007,"score_spread":0.204138015840225,"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."}}