{"id":"W2529105348","doi":"10.1021/acsenergylett.6b00429","title":"Remote Molecular Doping of Colloidal Quantum Dot Photovoltaics","year":2016,"lang":"en","type":"article","venue":"ACS Energy Letters","topic":"Quantum Dots Synthesis And Properties","field":"Materials Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Materials Research; National Institute of General Medical Sciences; Office of Naval Research; Institut de Ciències Fotòniques; King Abdullah University of Science and Technology; Georgia Institute of Technology; National Institutes of Health; National Science Foundation","keywords":"Photovoltaics; Doping; Passivation; Quantum dot; Nanotechnology; Materials science; Energy conversion efficiency; Optoelectronics; Photovoltaic system; Layer (electronics); Electrical engineering","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":[0.00009341697,0.0001403849,0.0001821242,0.0001306534,0.0001741809,0.0004891796,0.0003033864,0.0003334421,0.001488047],"category_scores_gemma":[0.000198501,0.0001146536,0.000136456,0.0001275717,0.00024113,0.0002342676,0.0002733907,0.0003458488,0.0004251666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004446339,"about_ca_system_score_gemma":0.0001554824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005405436,"about_ca_topic_score_gemma":0.00101764,"domain_scores_codex":[0.9998934,0.00001159511,0.000005018904,0.00003276499,0.00004413434,0.00001314898],"domain_scores_gemma":[0.9999435,0.00001661899,0.000009205458,0.00001033467,0.00001270015,0.000007599463],"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":[0.00001131884,0.00001080513,0.00006984487,0.00004534173,0.000003045106,0.00002489761,0.00001403318,0.0002268507,0.9942696,0.001580317,0.00008004592,0.003663892],"study_design_scores_gemma":[0.000006736293,0.00005750271,0.0002339061,0.00000448268,0.000004933549,0.00004757414,0.000006997118,0.003089159,0.9921169,0.000167641,0.00426035,0.000003814715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9528704,0.003671558,0.02746568,0.000347879,0.0001076168,0.00006061134,0.0001813438,0.0003782386,0.01491668],"genre_scores_gemma":[0.9840222,0.0008658712,0.01159017,0.00006310137,0.00001591592,0.00002184686,0.00008006171,0.00004298284,0.003297839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001488047,"threshold_uncertainty_score":0.004978061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293572368114969,"score_gpt":0.2093613389624641,"score_spread":0.1964256152813144,"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."}}