{"id":"W2789386880","doi":"10.1016/j.talanta.2018.03.023","title":"Particle detection on microfluidic chips by differential resistive pulse sensing (RPS) method","year":2018,"lang":"en","type":"article","venue":"Talanta","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Particle (ecology); Microfluidics; Resistive touchscreen; Amplitude; Voltage; Particle size; Noise (video); Analytical Chemistry (journal); SIGNAL (programming language); Electrolyte; Detection limit; Signal-to-noise ratio (imaging); Pulse (music); Range (aeronautics); Nanoparticle; Sensitivity (control systems); Optoelectronics; Nanotechnology; Electrode; Chromatography; Materials science; Optics; Electronic engineering; Physics; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008100677,0.0001582886,0.0001716306,0.00004408572,0.0001643856,0.00001516716,0.00005414556,0.00006351995,0.00005479215],"category_scores_gemma":[0.000008332237,0.0001448363,0.00005095369,0.0001376835,0.00004696512,0.00005824855,0.00001335213,0.0001061485,0.0001092841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003348122,"about_ca_system_score_gemma":0.000003136944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004803067,"about_ca_topic_score_gemma":0.00005921551,"domain_scores_codex":[0.9992355,0.00002773594,0.0001584992,0.0001921249,0.00011215,0.0002740141],"domain_scores_gemma":[0.9997095,0.00004114676,0.00002006709,0.0001467717,0.00002656073,0.00005590541],"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":[0.00008946397,0.00002195097,0.00009252061,0.00002040799,0.00007504236,0.000009556952,0.0004888874,0.000004021663,0.9785896,0.00001392039,0.002916908,0.01767773],"study_design_scores_gemma":[0.0003321196,0.000116679,0.0015057,0.00003027983,0.00004551222,0.00000787703,0.00005731772,0.001049315,0.9934292,0.00006396207,0.00318306,0.0001789506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684472,0.0005630116,0.02881294,0.00003786936,0.0005530852,0.0001141737,0.00003530809,0.0002779954,0.001158415],"genre_scores_gemma":[0.999067,0.0002565531,0.00006995971,0.00003391147,0.0002949568,0.000003864622,0.00001443299,0.00003139904,0.0002279044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03061982,"threshold_uncertainty_score":0.5906254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152933018103845,"score_gpt":0.2313620742649722,"score_spread":0.2198327440839338,"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."}}