{"id":"W2334145160","doi":"10.1557/proc-0937-m03-02","title":"Application of Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) for Analysis of Novel Organic Semiconductor Materials","year":2006,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Luminescence and Fluorescent Materials","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Tetracene; Materials science; Rubrene; Pentacene; Inductively coupled plasma mass spectrometry; Organic semiconductor; Characterization (materials science); Semiconductor; Wafer; Field-effect transistor; Impurity; Inductively coupled plasma; Crystal (programming language); Nanotechnology; Mass spectrometry; Analytical Chemistry (journal); Optoelectronics; Plasma; Thin-film transistor; Transistor; Organic chemistry; Molecule; Chromatography; Chemistry","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":[0.0006382989,0.0002235889,0.0007546368,0.0004330118,0.00006455113,0.00007543185,0.0003938478,0.0001545151,0.0004336828],"category_scores_gemma":[0.0001155912,0.0002016686,0.0001159166,0.0009425214,0.0001473024,0.0003647828,0.00006103035,0.0000381309,0.00001292731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007765896,"about_ca_system_score_gemma":0.00004659264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002850473,"about_ca_topic_score_gemma":0.000007328006,"domain_scores_codex":[0.9980214,0.000007206438,0.0008197416,0.0004713863,0.0003415381,0.0003387441],"domain_scores_gemma":[0.9983896,0.00004661215,0.0008340363,0.0001815955,0.0005006733,0.00004750714],"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":[0.000147014,0.0001852699,0.003664123,0.0002638405,0.00008760596,9.177134e-8,0.0001589248,0.00000571428,0.9933261,0.002001161,0.0001549599,0.00000521506],"study_design_scores_gemma":[0.0006933241,0.00008864882,0.008428752,0.00002805031,0.0004872544,0.000001727072,0.0001233568,0.0006712462,0.9889558,0.0002801062,0.00003525283,0.0002064656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948788,0.00001214146,0.003674547,0.00004653896,0.0002166438,0.0006495863,0.0003826985,0.00006216842,0.00007686771],"genre_scores_gemma":[0.9901416,0.000004088918,0.009312857,0.00001461205,0.0002237312,0.00008836361,0.00009776791,0.0000317366,0.00008523797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00563831,"threshold_uncertainty_score":0.8223809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01821667741795424,"score_gpt":0.2488174822841825,"score_spread":0.2306008048662283,"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."}}