{"id":"W2165605663","doi":"10.1016/j.colsurfa.2010.09.004","title":"A surface charge characterization device using sedimentation potential for single and mixed particle systems","year":2010,"lang":"en","type":"article","venue":"Colloids and Surfaces A Physicochemical and Engineering Aspects","topic":"Electrostatics and Colloid Interactions","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zeta potential; Surface charge; Sedimentation; Settling; Mineralogy; Characterization (materials science); Particle size; Particle (ecology); Electrophoresis; Conductivity; Analytical Chemistry (journal); Materials science; Fractionation; Chemistry; Thermodynamics; Chromatography; Geology; Nanotechnology; Physics; Physical chemistry; Sediment; Nanoparticle","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004341717,0.0001420538,0.0001648572,0.00001293733,0.0001239351,0.000170397,0.00003088727,0.0000678279,0.000005590981],"category_scores_gemma":[0.00001944978,0.0001449874,0.00002472321,0.00007190085,0.00002782975,0.0001491764,0.00002583982,0.0001138205,3.144124e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001838347,"about_ca_system_score_gemma":0.00001197992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003116068,"about_ca_topic_score_gemma":0.000002095912,"domain_scores_codex":[0.9993542,0.000003199716,0.0001468516,0.000222555,0.00006859601,0.0002045347],"domain_scores_gemma":[0.9996616,0.00005732538,0.00005224118,0.0000718847,0.00005432528,0.0001026114],"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":[0.00002076926,0.00005040654,0.0001140178,0.0002131245,0.00002995637,6.255924e-7,0.0001008047,0.0002405915,0.9984167,0.0006808072,0.00000692155,0.0001252262],"study_design_scores_gemma":[0.0003483462,0.0000261191,0.0001356838,0.00003957677,0.00003303211,0.00001468019,0.00005748845,0.3884988,0.6104784,0.00003038255,0.0001997151,0.0001377955],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998773,0.0001996326,0.0005639357,0.00005228747,0.0001595732,0.0001146816,0.00005547605,0.00005144066,0.00002992312],"genre_scores_gemma":[0.9990475,0.00003161224,0.0006141718,0.000007953199,0.0001078108,0.00001696111,0.00005535913,0.00002156209,0.00009711078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3882582,"threshold_uncertainty_score":0.5912415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008811582271508732,"score_gpt":0.216881616345025,"score_spread":0.2080700340735163,"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."}}