{"id":"W4321783891","doi":"10.1016/j.petlm.2023.02.002","title":"Surfactant and nanoparticle synergy: Towards improved foam stability","year":2023,"lang":"en","type":"article","venue":"Petroleum","topic":"Pickering emulsions and particle stabilization","field":"Materials Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Mitacs; Petroleum Technology Research Centre","keywords":"Pulmonary surfactant; Micromodel; Enhanced oil recovery; Materials science; Nanoparticle; Chemical engineering; Porous medium; Bubble; Foaming agent; Porosity; Pressure drop; Composite material; Nanotechnology","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":[0.0003833683,0.0004235228,0.0004175219,0.0003840112,0.0001824308,0.0004192355,0.0002366181,0.0008276812,0.0009662691],"category_scores_gemma":[0.0003509869,0.0002029272,0.0003347238,0.0001954198,0.0002572496,0.0006863375,0.0004716601,0.0004671338,0.0004491356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002680547,"about_ca_system_score_gemma":0.0002245302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002952525,"about_ca_topic_score_gemma":0.0005933486,"domain_scores_codex":[0.9996449,0.00006337014,0.00002857117,0.00009011937,0.0001239105,0.00004910567],"domain_scores_gemma":[0.9998261,0.00005384149,0.00003369102,0.00001241372,0.00005053215,0.00002331531],"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":[0.00005360319,0.00002571017,0.0001103577,0.0001145919,0.00001074715,0.00005996378,0.00002116626,0.0002472048,0.9918807,0.0002479875,0.00007105551,0.007156945],"study_design_scores_gemma":[0.000007311509,0.0002237967,0.0002168717,0.000007707638,0.00001954994,0.0001163511,0.00001056085,0.001519546,0.9946553,0.0001100816,0.003107058,0.000005873518],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241375,0.02327372,0.04498453,0.0007983245,0.0001496564,0.00006442056,0.0001122233,0.0004237472,0.006055866],"genre_scores_gemma":[0.9725115,0.004305085,0.01970536,0.0001759966,0.00005451788,0.00004313085,0.00009243994,0.00004488969,0.003067076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009662691,"threshold_uncertainty_score":0.003232539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425878588880323,"score_gpt":0.2549759157572083,"score_spread":0.2307171298684051,"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."}}