{"id":"W4238931007","doi":"10.26434/chemrxiv.11853405","title":"Molecular Characterization of the Surface Excess Charge Layer in Droplets","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Pacific Northwest National Laboratory; Natural Sciences and Engineering Research Council of Canada; Compute Canada; Ohio State University","keywords":"Ion; Chemistry; Analytical Chemistry (journal); Chloride; Molecular dynamics; Layer (electronics); Surface charge; Iodide; Chemical physics; Inorganic chemistry; Physical chemistry; Chromatography; Computational chemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009681259,0.0002110271,0.000200184,0.0001828653,0.0003295669,0.000364131,0.0003991626,0.0003314372,0.002798513],"category_scores_gemma":[0.0003116353,0.000155638,0.0002328753,0.0001878868,0.0003604892,0.0004926522,0.0002704327,0.0005141103,0.0002299338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007128107,"about_ca_system_score_gemma":0.0003289874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003411968,"about_ca_topic_score_gemma":0.001310432,"domain_scores_codex":[0.9999415,0.000002558526,0.000002143976,0.00001464941,0.00002075632,0.00001842886],"domain_scores_gemma":[0.9999176,0.00002149792,0.00001464254,0.000007233106,0.00001866456,0.00002022347],"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":[0.000154473,0.0001260093,0.006209955,0.0001318541,0.00004757765,0.0004861536,0.0002631024,0.09686561,0.8821507,0.007631155,0.001000258,0.004933238],"study_design_scores_gemma":[0.00009117527,0.0002246335,0.01165669,0.00002693486,0.00003378569,0.0001093753,0.0001682939,0.7323053,0.2499633,0.002076173,0.003277123,0.00006714641],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930059,0.0001853587,0.00404005,0.0001422694,0.00002110122,0.0000160879,0.0004440113,0.0001026781,0.002042586],"genre_scores_gemma":[0.9959388,0.0001869133,0.002412515,0.00004511372,0.000005640251,0.0000234413,0.0005505661,0.00004173344,0.0007953472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003411968,"threshold_uncertainty_score":0.009361982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01917032655115551,"score_gpt":0.2600329120987945,"score_spread":0.240862585547639,"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."}}