{"id":"W2265898808","doi":"10.1149/ma2015-02/45/1797","title":"Upconverting Nanoparticles for Sensing","year":2015,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Photon upconversion; Autofluorescence; Fluorescence; Förster resonance energy transfer; Excited state; Luminescence; Materials science; Nanoparticle; Near-infrared spectroscopy; Nanotechnology; Optoelectronics; Nanosensor; Photochemistry; Chemistry; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003060948,0.0006413648,0.0003422192,0.0004258637,0.0002715616,0.0005254423,0.0006437034,0.001105114,0.003420278],"category_scores_gemma":[0.000358805,0.0003428906,0.000481039,0.0002415158,0.0003201358,0.000830912,0.0006010245,0.001654078,0.002091347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00067301,"about_ca_system_score_gemma":0.0001745582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002812915,"about_ca_topic_score_gemma":0.000497951,"domain_scores_codex":[0.9995295,0.00005840331,0.00002660453,0.0001487849,0.0001949343,0.00004187764],"domain_scores_gemma":[0.999877,0.00003194409,0.00002074095,0.00002263801,0.00003460316,0.00001314706],"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":[0.00002714435,0.00004609186,0.00005775208,0.0003058356,0.00001345773,0.00009092604,0.00004655506,0.0003675057,0.9609569,0.003654156,0.001812853,0.03262081],"study_design_scores_gemma":[0.000009565839,0.0001155457,0.0001255365,0.00002920714,0.00001411527,0.0002988302,0.00001353781,0.002771005,0.9551352,0.0006575057,0.04081368,0.00001628086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1909406,0.0789652,0.6389762,0.003053457,0.003433134,0.0009252023,0.0009276761,0.004830776,0.07794777],"genre_scores_gemma":[0.6274961,0.02382889,0.3024724,0.002251157,0.0003840592,0.0005670935,0.001158205,0.0003944825,0.04144764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003420278,"threshold_uncertainty_score":0.01144195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02533062615736585,"score_gpt":0.2882121570069097,"score_spread":0.2628815308495439,"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."}}