{"id":"W2921329644","doi":"10.1002/adma.201805580","title":"A Facet‐Specific Quantum Dot Passivation Strategy for Colloid Management and Efficient Infrared Photovoltaics","year":2019,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Quantum Dots Synthesis And Properties","field":"Materials Science","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"King Abdullah University of Science and Technology; Ministry of Science, ICT and Future Planning; Daegu Gyeongbuk Institute of Science and Technology; Fonds Wetenschappelijk Onderzoek","keywords":"Photovoltaics; Passivation; Materials science; Nanocrystal; Quantum dot; Facet (psychology); Colloid; Nanotechnology; Photoluminescence; Band gap; Optoelectronics; Absorption (acoustics); Energy conversion efficiency; Infrared; Semiconductor; Photovoltaic system; Optics; Chemical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000590778,0.0002400647,0.0003923677,0.00007770706,0.0001603744,0.0002808475,0.000188066,0.00007678787,0.0008094425],"category_scores_gemma":[0.00003785004,0.00019465,0.00003907793,0.00008620942,0.00006899605,0.000225795,0.00009313984,0.00003006217,0.0002084684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006708769,"about_ca_system_score_gemma":0.00002346722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001181473,"about_ca_topic_score_gemma":0.000001366241,"domain_scores_codex":[0.9982778,0.00007183799,0.000468588,0.0004986681,0.0002853258,0.0003977709],"domain_scores_gemma":[0.9991448,0.00008482641,0.0002210696,0.0003647298,0.0001187525,0.00006585022],"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":[0.0003065947,0.00004508638,0.00000641007,0.0002654725,0.000009724426,0.000001195923,0.000124704,0.0006309598,0.9867781,0.01055629,0.0005394585,0.0007359717],"study_design_scores_gemma":[0.0009512829,0.000216906,0.0004132722,0.0001022172,0.00001487354,0.000002595912,0.0004262107,0.0001544226,0.963337,0.003004593,0.03108477,0.0002918331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951153,0.0004479321,0.0005873045,0.00005109715,0.001128117,0.00166399,0.0002249065,0.0001078318,0.0006735623],"genre_scores_gemma":[0.9943659,0.0002709483,0.003416021,0.00008169431,0.00005890689,0.0003697985,0.00002800104,0.00003827722,0.001370481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03054531,"threshold_uncertainty_score":0.8862828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02250484660215188,"score_gpt":0.2477808774267515,"score_spread":0.2252760308245996,"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."}}