{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002426667,0.00031597,0.0002350847,0.0001466991,0.0000984314,0.0003263042,0.0003191047,0.0003439304,0.0005118996],"category_scores_gemma":[0.0003417844,0.0001819989,0.0001106873,0.0001088775,0.0003076111,0.0002111543,0.0001434359,0.000195377,0.0002363685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004920909,"about_ca_system_score_gemma":0.0001576944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128263,"about_ca_topic_score_gemma":0.001824585,"domain_scores_codex":[0.9997789,0.00003805078,0.00001311866,0.00007052901,0.00005850296,0.00004088744],"domain_scores_gemma":[0.999891,0.00002924532,0.00002529463,0.000007354976,0.00002640283,0.00002061334],"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":[0.00003015012,0.000003130222,0.0001511825,0.000009824205,0.000001765727,0.000008826843,0.00000596058,0.00004134437,0.9991643,0.00001370742,0.000006437638,0.0005633649],"study_design_scores_gemma":[0.000002810119,0.00002702572,0.0007110299,7.006635e-7,0.000002662247,0.00004666304,0.000005332368,0.0005326046,0.9984391,0.000004725203,0.0002252108,0.000002029199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933489,0.0008920797,0.004777234,0.00005111351,0.000008809478,0.0000178934,0.00006696815,0.0001122336,0.0007248131],"genre_scores_gemma":[0.9939325,0.000362481,0.004960587,0.00005351712,0.000004562545,0.00001604675,0.00007703599,0.00001072311,0.000582359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00128263,"threshold_uncertainty_score":0.003570437,"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."}}