{"id":"W2783521869","doi":"10.1063/1.4990796","title":"Effects of cross-linking on partitioning of nanoparticles into a polymer brush: Coarse-grained simulations test simple approximate theories","year":2018,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Polymer Surface Interaction Studies","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Nanoparticle; Polymer; Materials science; Monomer; Polymer brush; Molecular dynamics; Brush; Phase (matter); Volume fraction; Chemical physics; Nanotechnology; Composite material; Chemistry; Computational chemistry; Polymerization","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":[0.0003199847,0.0001175401,0.0002957071,0.00002567325,0.0001336085,0.00003197422,0.0002332847,0.00003062651,0.00002835595],"category_scores_gemma":[0.0005849705,0.0000775755,0.00009043192,0.000173318,0.0008113254,0.0002615485,0.00008929329,0.0001214918,0.000008789316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002753347,"about_ca_system_score_gemma":0.00003722609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002246491,"about_ca_topic_score_gemma":0.000001254883,"domain_scores_codex":[0.9988162,0.00008230927,0.0005062057,0.00008469135,0.0003387728,0.0001718546],"domain_scores_gemma":[0.9965115,0.002208558,0.0006759659,0.0001713509,0.0003901524,0.00004250839],"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":[0.0002222713,0.0001684793,0.000521256,0.00004761397,0.00003503486,4.573494e-7,0.002349231,0.0002355098,0.9951572,0.0009397212,0.0000280995,0.0002950891],"study_design_scores_gemma":[0.0003691014,0.0001850295,0.0001262753,0.0001641385,0.00006612517,0.000003248663,0.0001040515,0.0006031218,0.9716607,0.02663753,0.000009081424,0.00007155869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981872,0.0001492843,0.001226715,0.0001046358,0.0001613719,0.0000663848,0.0000114072,0.00001297336,0.00008004586],"genre_scores_gemma":[0.9993789,0.00000404165,0.0003002344,0.00006861424,0.0002267461,0.000001412493,6.462313e-7,0.00001243723,0.0000069365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02569781,"threshold_uncertainty_score":0.3163437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156051953449177,"score_gpt":0.3104008596616574,"score_spread":0.2947956643167397,"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."}}