{"id":"W4313225534","doi":"10.3390/polym15010136","title":"Copper-Doped Carbon Nanodots with Superior Photocatalysis, Directly Obtained from Chromium-Copper-Arsenic-Treated Wood Waste","year":2022,"lang":"en","type":"article","venue":"Polymers","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Technologique des Résidus Industriels; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Science Foundation of Shandong Province; Northeast Forestry University; Mitacs; Canada Research Chairs","keywords":"Photocatalysis; Materials science; Chromium; Copper; Nanodot; Arsenic; Nanomaterials; Carbon fibers; Absorption (acoustics); Chemical engineering; Doping; Nuclear chemistry; Nanotechnology; Metallurgy; Composite material; Chemistry; Catalysis; Organic chemistry; Composite number; Optoelectronics","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":[0.00005836363,0.000258172,0.0001100486,0.0002275522,0.0001259006,0.0001917605,0.0001999139,0.0003324068,0.000500375],"category_scores_gemma":[0.0001017506,0.0001415815,0.0001097659,0.000210622,0.0001137842,0.000178759,0.0001101606,0.000169336,0.000185148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003730687,"about_ca_system_score_gemma":0.0001699421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006720574,"about_ca_topic_score_gemma":0.002659999,"domain_scores_codex":[0.999945,0.000003641292,0.000004698576,0.00001973283,0.00001900176,0.000008014307],"domain_scores_gemma":[0.9999471,0.000007927037,0.00001254226,0.000005500563,0.00001584766,0.00001109228],"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":[0.00001030741,0.000007934549,0.00007420736,0.0000346928,0.000002292847,0.00003730822,0.000005252576,0.00009684548,0.9982942,0.000121026,0.00003637876,0.001279584],"study_design_scores_gemma":[0.000002299598,0.00002990833,0.0003826742,0.000001426502,0.000003326928,0.00004695913,0.000004754632,0.0008952474,0.9975258,0.00001594776,0.001089671,0.000002040373],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802024,0.00117422,0.01533408,0.00007945554,0.00004794193,0.00004147572,0.0003728169,0.0002183851,0.002529343],"genre_scores_gemma":[0.9824865,0.0004530881,0.01431184,0.00003804344,0.000005335899,0.00002182107,0.0002771881,0.00002082062,0.002385379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006720574,"threshold_uncertainty_score":0.002706766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031907249650226,"score_gpt":0.22297796779605,"score_spread":0.2126588952995477,"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."}}