{"id":"W2471933653","doi":"10.1016/j.applthermaleng.2016.07.026","title":"Heat transfer enhancement of nanofluids using iron nanoparticles decorated carbon nanotubes","year":2016,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"King Abdulaziz City for Science and Technology; King Fahd University of Petroleum and Minerals","keywords":"Nanofluid; Materials science; Carbon nanotube; Heat transfer; Heat transfer enhancement; Chemical engineering; Nanoparticle; Heat transfer coefficient; Differential scanning calorimetry; Heat exchanger; Iron oxide nanoparticles; Iron oxide; Heat capacity; Enhanced heat transfer; Composite material; Nanotechnology; Thermodynamics; Metallurgy","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":[0.0001157476,0.00024765,0.0001377744,0.0001668317,0.0001776211,0.0001863338,0.0001409411,0.0002874983,0.0006801442],"category_scores_gemma":[0.0001755628,0.0001125132,0.000210876,0.00006481953,0.0001923003,0.0003092637,0.0001300079,0.0002438531,0.0001227333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003131742,"about_ca_system_score_gemma":0.00008912018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003703368,"about_ca_topic_score_gemma":0.0004979481,"domain_scores_codex":[0.9999404,0.000007772358,0.000002895319,0.00001308413,0.00001532917,0.00002052369],"domain_scores_gemma":[0.9999197,0.00002648517,0.00001821859,0.000006202294,0.00001990527,0.000009537654],"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":[0.00007945102,0.00001791504,0.00005256994,0.00003167609,0.000001785411,0.00002794105,0.00002234796,0.0002115601,0.9985009,0.0001532551,0.00004954859,0.0008510348],"study_design_scores_gemma":[0.00000459118,0.00003907292,0.0002359686,0.000001298632,0.000002582017,0.000008044036,0.000004369297,0.001690747,0.9978049,0.00001364151,0.0001922255,0.000002333263],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966646,0.0003021725,0.00159248,0.00005250309,0.000031886,0.000007190869,0.00002109274,0.0000369255,0.001291138],"genre_scores_gemma":[0.9981901,0.00008259886,0.000741174,0.00001242664,0.000007855543,0.000005440255,0.00001475248,0.000006169133,0.0009394191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006801442,"threshold_uncertainty_score":0.002275348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008525108169734154,"score_gpt":0.1843832248681581,"score_spread":0.175858116698424,"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."}}