{"id":"W2789601408","doi":"10.1016/j.dyepig.2018.02.026","title":"Revealing dye and dye-drug aggregation into nano-entities using NMR","year":2018,"lang":"en","type":"article","venue":"Dyes and Pigments","topic":"Molecular spectroscopy and chirality","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Institut National de la Recherche Scientifique","funders":"","keywords":"Chemistry; Molecule; Drug delivery; Nano-; Drug; Small molecule; Proton NMR; Congo red; Absorption (acoustics); Nanotechnology; Combinatorial chemistry; Organic chemistry; Chemical engineering; Materials science; Adsorption","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008750729,0.0001092152,0.0001105267,0.00002110721,0.0002658752,0.00009036955,0.00005391707,0.00005294227,0.0001864339],"category_scores_gemma":[0.00001179845,0.0001055434,0.00002355674,0.00003688512,0.00009644686,0.0001085908,0.00006845787,0.00006316623,0.000003959827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002753059,"about_ca_system_score_gemma":0.00001098438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004538524,"about_ca_topic_score_gemma":0.00005930486,"domain_scores_codex":[0.9993615,0.00001536069,0.0001419147,0.0002080357,0.0001199511,0.000153289],"domain_scores_gemma":[0.9996974,0.000008966471,0.0000708627,0.0001374436,0.00002698596,0.00005833691],"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":[0.00008270386,0.00007244225,0.06594665,0.0006495321,0.0001189324,0.00001109963,0.004095468,0.000003086243,0.9185688,0.002090642,0.0004504869,0.007910118],"study_design_scores_gemma":[0.0008706998,0.00004988677,0.001721053,0.0004770556,0.00008512715,0.00001736995,0.0006382826,0.001877848,0.9784949,0.006061523,0.009302727,0.0004035485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955191,0.0007419848,0.0003070563,0.00005860467,0.00008746069,0.00003595681,0.00000803784,0.00002506149,0.00321671],"genre_scores_gemma":[0.9973758,0.0001478541,0.00122358,0.0001069449,0.0001779415,0.000002571287,0.00001626364,0.00001117381,0.0009378791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0642256,"threshold_uncertainty_score":0.4303935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124174128855246,"score_gpt":0.2701519734258971,"score_spread":0.2589102321373447,"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."}}