{"id":"W4280579630","doi":"10.1021/acs.accounts.2c00066","title":"Polymer-Tethered Nanoparticles: From Surface Engineering to Directional Self-Assembly","year":2022,"lang":"en","type":"article","venue":"Accounts of Chemical Research","topic":"Advanced Polymer Synthesis and Characterization","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Jilin University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; State Key Laboratory of Supramolecular Structure and Materials; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Nanoparticle; Polymer; Nanotechnology; Copolymer; Self-assembly; Materials science; Nanomaterials; Micelle; Polymerization; Macromolecule; Surface modification; Nanostructure; Chemistry; Aqueous solution; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002528207,0.0001183396,0.0001824519,0.00005758707,0.0001125605,0.00002882579,0.0004326914,0.00006150932,0.002034992],"category_scores_gemma":[0.0002018129,0.0001307684,0.00005643514,0.0003810317,0.00002770437,0.0001148153,0.0003870318,0.0003455947,0.0000327382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123994,"about_ca_system_score_gemma":0.00008611265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001380086,"about_ca_topic_score_gemma":6.84619e-7,"domain_scores_codex":[0.9980156,0.00003268256,0.000232892,0.0003265098,0.0009978639,0.0003943834],"domain_scores_gemma":[0.9988289,0.000537243,0.00005383108,0.0003216676,0.0001183667,0.0001399378],"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":[0.0001053243,0.0001666192,0.0006412562,0.00002168356,0.00003696874,0.000002464751,0.0002494155,0.00007224615,0.9969281,0.00006108752,0.0002964069,0.001418438],"study_design_scores_gemma":[0.0001904849,0.0000137671,0.0001281229,0.00002120593,0.000005811189,0.00000136333,0.000185433,0.0005173731,0.9909196,0.00003437933,0.007852565,0.0001298248],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998307,0.0003050327,0.00001773747,0.000498939,0.00005922195,0.00005647292,0.000151016,0.00007379322,0.0005308085],"genre_scores_gemma":[0.9988637,0.00001006187,0.0004956495,0.00003578138,0.000130352,0.00006258152,0.00004766437,0.00003650544,0.0003177015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007556158,"threshold_uncertainty_score":0.9988773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295675821451825,"score_gpt":0.2977497308909955,"score_spread":0.2747929726764773,"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."}}