{"id":"W2915879734","doi":"10.1016/j.talanta.2019.02.090","title":"Fast, low-pressure chromatographic separation of proteins using hydroxyapatite nanoparticles","year":2019,"lang":"en","type":"article","venue":"Talanta","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Research Foundation","keywords":"Chemistry; Chromatography; Nanoparticle; Cuboid; Packed bed; Resolution (logic); Bovine serum albumin; Porosity; Theoretical plate; Dispersion (optics); Analytical Chemistry (journal); Lysozyme; Denaturation (fissile materials); Nanotechnology; Materials science; Nuclear chemistry; Organic chemistry","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":[0.0003710464,0.000391042,0.0002838211,0.0003257636,0.0003462734,0.0007187285,0.0004823881,0.0006695036,0.0005264718],"category_scores_gemma":[0.0004492321,0.000278636,0.000315427,0.000163059,0.000302933,0.0004790517,0.0004061042,0.0007731782,0.0005412896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005034325,"about_ca_system_score_gemma":0.0005086775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001277031,"about_ca_topic_score_gemma":0.002031809,"domain_scores_codex":[0.9996614,0.00002866941,0.00001847009,0.00008769921,0.0001445772,0.00005911871],"domain_scores_gemma":[0.9997919,0.00007985902,0.00002372902,0.0000185549,0.00005902333,0.00002691219],"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":[0.000033098,0.00001248606,0.00003989611,0.00002720364,0.000002736464,0.00002438858,0.00001780747,0.0000474884,0.9970572,0.0001145566,0.00005809839,0.002565098],"study_design_scores_gemma":[0.000005218464,0.0000300951,0.0002781332,0.000001731622,0.000003474642,0.00004885718,0.00001080068,0.001184905,0.9976166,0.00005128252,0.0007634295,0.000005485221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8892753,0.005915057,0.0975429,0.0007731835,0.0002246835,0.0001349173,0.0002737142,0.0007562923,0.005103924],"genre_scores_gemma":[0.9512737,0.00184276,0.03612464,0.0003882984,0.00005829099,0.0001036433,0.0002253537,0.00008626162,0.00989709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001277031,"threshold_uncertainty_score":0.003652692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005203727841305144,"score_gpt":0.2079481582370755,"score_spread":0.2027444303957704,"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."}}