{"id":"W4402580352","doi":"10.1021/acsnano.4c08316","title":"Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification","year":2024,"lang":"en","type":"article","venue":"ACS Nano","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Foundation for Innovation","keywords":"Raman spectroscopy; Materials science; Artificial intelligence; Raman scattering; Laser-induced breakdown spectroscopy; Identification (biology); Polystyrene; Machine learning; Nanotechnology; Biological system; Computer science; Spectroscopy; Composite material; Physics; Optics; Polymer","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":[0.0008831523,0.001121617,0.0006117504,0.001054244,0.0003224983,0.0008388338,0.0007680184,0.001012534,0.000894152],"category_scores_gemma":[0.001615591,0.0004708062,0.0006887282,0.0005153811,0.0004510078,0.0009400998,0.000804583,0.001109801,0.0009362065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005829606,"about_ca_system_score_gemma":0.0005654753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008248118,"about_ca_topic_score_gemma":0.00206549,"domain_scores_codex":[0.998998,0.0001260714,0.00006121404,0.00039116,0.0003448451,0.00007868626],"domain_scores_gemma":[0.9993007,0.0002651725,0.0001470728,0.00008557249,0.000172515,0.00002892999],"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":[0.0001061435,0.0001394753,0.001071927,0.0002041586,0.00005769583,0.0001010486,0.00006629607,0.007637773,0.9268991,0.0008161068,0.0005179672,0.06238232],"study_design_scores_gemma":[0.00001020378,0.0001273372,0.001072966,0.00001256708,0.00002750469,0.0001075419,0.00001842156,0.1577575,0.8370131,0.0005901138,0.003215791,0.0000470144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2739486,0.001395885,0.7137724,0.0005938107,0.0002236945,0.0002717937,0.0005038371,0.005718903,0.003571171],"genre_scores_gemma":[0.4339933,0.0009260431,0.5594646,0.0004485837,0.0000866699,0.0003666302,0.0006920007,0.0002609691,0.00376122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001121617,"threshold_uncertainty_score":0.00467056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0209921156239275,"score_gpt":0.2670272731375647,"score_spread":0.2460351575136372,"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."}}