{"id":"W3021483695","doi":"10.1016/j.carbpol.2020.116393","title":"Mapping the surface potential, charge density and adhesion of cellulose nanocrystals using advanced scanning probe microscopy","year":2020,"lang":"en","type":"article","venue":"Carbohydrate Polymers","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates; CMC Microsystems","keywords":"Surface charge; Nanocellulose; Charge density; Kelvin probe force microscope; Streaming current; Nanotechnology; Adhesion; Materials science; Electrostatic force microscope; Scanning probe microscopy; Microscopy; Chemical engineering; Nanoscopic scale; Biocompatibility; Surface force; Cellulose; Composite material; Chemistry; Electrokinetic phenomena; Atomic force microscopy; Optics; Organic chemistry; Physical 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":[0.0001047916,0.000184756,0.0001804334,0.0003037094,0.0001927114,0.0003282464,0.0002846978,0.0003331333,0.001200972],"category_scores_gemma":[0.0002101354,0.0001402566,0.0001368873,0.0003015286,0.0001808664,0.0003186469,0.0001604537,0.0003458966,0.0001614599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003736387,"about_ca_system_score_gemma":0.0001526663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001710104,"about_ca_topic_score_gemma":0.002469076,"domain_scores_codex":[0.9999015,0.000007983576,0.000003668144,0.00002505749,0.0000427098,0.00001905087],"domain_scores_gemma":[0.9998899,0.00004892659,0.00001502392,0.00000800938,0.0000272332,0.00001103121],"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":[0.00002780536,0.000009806916,0.0002185214,0.00001154741,0.000002374963,0.00001656045,0.00001094967,0.00006807232,0.9982338,0.00008001733,0.00002609771,0.001294366],"study_design_scores_gemma":[0.000005697555,0.00005164603,0.006368302,0.000001863763,0.000005869978,0.00007413617,0.00003635516,0.004970223,0.9880258,0.00006096694,0.0003923184,0.000006786396],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942744,0.0003163119,0.003841866,0.00005538347,0.00001167067,0.000009229196,0.000139049,0.00003083492,0.001321245],"genre_scores_gemma":[0.9917473,0.0003008245,0.006185438,0.00003431075,0.000009785961,0.00001920879,0.0001206843,0.00001172113,0.001570718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001710104,"threshold_uncertainty_score":0.004017651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054734673132985,"score_gpt":0.2742112508521904,"score_spread":0.2436639041208605,"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."}}