{"id":"W3001916699","doi":"10.1002/adhm.201901627","title":"Plasmonic Copper Sulfide Nanoparticles Enable Dark Contrast in Optical Coherence Tomography","year":2020,"lang":"en","type":"article","venue":"Advanced Healthcare Materials","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Horizon 2020 Framework Programme; Ministerio de Economía y Competitividad; China Postdoctoral Science Foundation; European Commission; National Key Research and Development Program of China; Instituto de Salud Carlos III; Comunidad de Madrid","keywords":"Plasmon; Optical coherence tomography; Nanoparticle; Materials science; Plasmonic nanoparticles; Contrast (vision); Nanotechnology; Transparency (behavior); Copper sulfide; Light scattering; Optics; Optoelectronics; Copper; Scattering; Computer science; Physics","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":[0.0001535252,0.0003184352,0.0001180174,0.0001862146,0.0001359172,0.0002879995,0.0001580578,0.0002614714,0.000553276],"category_scores_gemma":[0.0001927449,0.0001755267,0.00009926852,0.0001245016,0.0002871332,0.0002053751,0.0002676547,0.0002221663,0.00021032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002579159,"about_ca_system_score_gemma":0.0001672531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004611522,"about_ca_topic_score_gemma":0.0009680534,"domain_scores_codex":[0.9998821,0.00002772801,0.000008003911,0.00002369211,0.00003561624,0.00002285837],"domain_scores_gemma":[0.9999101,0.00002030023,0.00002478165,0.000009595295,0.00001922885,0.00001598512],"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":[0.00007363152,0.00001713194,0.00008963258,0.0000579183,0.000002956709,0.0000696496,0.0000310321,0.0002896975,0.9949917,0.0004929054,0.000191088,0.003692628],"study_design_scores_gemma":[0.000005604581,0.0001107556,0.0002228528,0.00000266006,0.00000397048,0.00005028983,0.00001026502,0.001240859,0.9967307,0.00004412003,0.00157468,0.000003306163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751148,0.001103548,0.01558325,0.0001436984,0.00004791046,0.00004838089,0.00009251881,0.0002785658,0.007587399],"genre_scores_gemma":[0.9879025,0.0004531234,0.007455325,0.00004548528,0.000009308701,0.00001748909,0.00006860929,0.00002280696,0.004025348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000553276,"threshold_uncertainty_score":0.001871288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02376821330391822,"score_gpt":0.2695787496106483,"score_spread":0.24581053630673,"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."}}