{"id":"W2068522335","doi":"10.1021/ic070169e","title":"Tuning the Selectivity/Specificity of Fluorescent Metal Ion Sensors Based on N<sub>2</sub>S<sub>2</sub> Pyridine-Containing Macrocyclic Ligands by Changing the Fluorogenic Subunit:  Spectrofluorimetric and Metal Ion Binding Studies","year":2007,"lang":"en","type":"article","venue":"Inorganic Chemistry","topic":"Molecular Sensors and Ion Detection","field":"Chemistry","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; McMaster University","keywords":"Chemistry; Potentiometric titration; Fluorescence; Metal; Metal ions in aqueous solution; Pyridine; Crystallography; Stoichiometry; Titration; Thioether; Absorption (acoustics); Selectivity; Ion; Ligand (biochemistry); Stereochemistry; Inorganic chemistry; Medicinal chemistry; Physical chemistry; Receptor; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001537213,0.0006877631,0.0007091285,0.0002425745,0.0008885607,0.0001103264,0.0003995818,0.0003625444,0.00003244237],"category_scores_gemma":[0.000644185,0.0005383067,0.0003688157,0.001741238,0.0002692971,0.0001322058,0.0002486292,0.001164166,0.000008180166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005747908,"about_ca_system_score_gemma":0.00008305585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001776037,"about_ca_topic_score_gemma":0.00001180639,"domain_scores_codex":[0.9963168,0.0000998614,0.000815563,0.0008958147,0.0008931521,0.0009787893],"domain_scores_gemma":[0.9974365,0.0007510651,0.0006536032,0.0007445414,0.0002209394,0.0001933859],"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":[0.000232783,0.0001326272,0.0005474674,0.0002030394,0.0003755421,0.00003384818,0.0005922439,0.00005936237,0.9947636,0.000006431224,0.00004578735,0.003007294],"study_design_scores_gemma":[0.0009679529,0.0000968581,0.0001732605,0.0001652756,0.0003336762,0.0001369517,0.003063094,0.002189739,0.9922607,0.00001300728,0.00006103057,0.0005384805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996601,0.00218743,0.0002163893,0.0001299836,0.0002163429,0.0002490713,0.0000305225,0.0001754117,0.0001938046],"genre_scores_gemma":[0.9986402,0.0005086208,0.00001576683,0.00005438545,0.0005682269,0.00001785736,0.00004836215,0.0001159787,0.00003062206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0025029,"threshold_uncertainty_score":0.9997069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134936841647871,"score_gpt":0.2252678362633374,"score_spread":0.2139184678468587,"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."}}