{"id":"W2417585285","doi":"10.1002/adfm.201601272","title":"Graphene Nanopores for Protein Sequencing","year":2016,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Human Genome Research Institute; National Institutes of Health; National Science Foundation","keywords":"Nanopore; Nanopore sequencing; Graphene; Hydrostatic pressure; Biophysics; Peptide; Transmembrane protein; Materials science; Amino acid; Ionic bonding; Molecular dynamics; Ion channel; Nanotechnology; DNA sequencing; Ion; DNA; Biology; Biochemistry; Chemistry; Computational chemistry; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00009821446,0.0001620047,0.000202357,0.00006463915,0.00009590018,0.000009543861,0.00005647007,0.00005194784,0.0002274073],"category_scores_gemma":[0.00002395429,0.0001115176,0.00005624909,0.00007228547,0.00003534369,0.000199002,0.00000849641,0.00001667872,0.00003357394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005517868,"about_ca_system_score_gemma":0.00001432232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001926494,"about_ca_topic_score_gemma":0.000004147514,"domain_scores_codex":[0.9991974,0.000006229616,0.0002474778,0.0001812225,0.0001084669,0.0002592386],"domain_scores_gemma":[0.9996968,0.00004351471,0.00003477976,0.0001156241,0.00007038479,0.00003888802],"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":[0.00009167378,0.000004826478,0.00001680128,0.00007510717,0.00004784167,0.000001524747,0.00001462072,0.0002792119,0.9943882,0.003228724,0.0003869502,0.001464553],"study_design_scores_gemma":[0.0006465781,0.00005932901,0.0002856825,0.00008590414,0.00001360892,0.000002469379,0.00001516022,0.000001916874,0.9777712,0.0135194,0.007409695,0.0001890843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9357755,0.0004094424,0.05982627,0.0001575501,0.002030162,0.0006314509,0.0003194676,0.0005136243,0.0003365821],"genre_scores_gemma":[0.9955146,0.000109923,0.002706247,0.00003086898,0.0002825587,0.0005860397,0.00003283727,0.00004022815,0.0006967203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05973912,"threshold_uncertainty_score":0.4547557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624549712251555,"score_gpt":0.2030209461179631,"score_spread":0.1867754489954475,"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."}}