{"id":"W4253545845","doi":"10.26434/chemrxiv.11853405.v3","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":14,"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":"Characterization (materials science); Layer (electronics); Charge (physics); Chemical physics; Surface charge; Surface (topology); Nanotechnology; Materials science; Chemistry; Chemical engineering; Physics; Physical chemistry; Engineering; Geometry","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":[0.00009595398,0.0002072144,0.0002086128,0.0002175419,0.0003206866,0.0003887767,0.0003522205,0.0003359213,0.002156429],"category_scores_gemma":[0.0003312496,0.000156244,0.0002238898,0.0002004961,0.0003683529,0.0004589372,0.0002742606,0.0005339747,0.0001893435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005945814,"about_ca_system_score_gemma":0.0002695771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002619621,"about_ca_topic_score_gemma":0.001022876,"domain_scores_codex":[0.9999381,0.00000253626,0.000002202655,0.00001469065,0.00002237596,0.00002005906],"domain_scores_gemma":[0.9999164,0.00002279333,0.00001605731,0.000006776733,0.00001779755,0.00002009978],"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":[0.0001299377,0.00009424699,0.005790444,0.00010476,0.00003864519,0.0004020233,0.0002006655,0.05898485,0.9251971,0.005229507,0.0004398811,0.003388053],"study_design_scores_gemma":[0.0001012385,0.0003022727,0.0162369,0.00002707583,0.0000400346,0.0001578952,0.000218579,0.6392274,0.33812,0.002305419,0.003190133,0.0000730097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994332,0.0001922323,0.003470302,0.00009563909,0.00001678669,0.00001697596,0.0003513651,0.0000746596,0.001450066],"genre_scores_gemma":[0.9963457,0.0002163139,0.002345331,0.00003467485,0.000005365588,0.000021876,0.0004196066,0.00002846726,0.0005826693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002619621,"threshold_uncertainty_score":0.00721401,"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."}}