{"id":"W4283022460","doi":"10.1021/acsenergylett.2c00283","title":"Electrolytic Methane Production from Reactive Carbon Solutions","year":2022,"lang":"en","type":"article","venue":"ACS Energy Letters","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of British Columbia","funders":"Army Research Office; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Total; Office of Energy Efficiency and Renewable Energy; Killam Trusts; Canada Foundation for Innovation; Canadian Institute for Advanced Research","keywords":"Methane; Chemistry; Anode; Electrochemistry; Cathode; Electrolyte; Inorganic chemistry; Yield (engineering); Carbon fibers; Hydrogen; Chemical engineering; Materials science; Electrode; Organic chemistry; Metallurgy; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001495919,0.0001623738,0.0001711527,0.0001752119,0.0002578203,0.00001575617,0.0002104525,0.00004341199,0.000205873],"category_scores_gemma":[0.00002716626,0.0001837561,0.00009583819,0.0004542495,0.00004769786,0.0001024886,0.0001357823,0.0002667241,0.000005307651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004422522,"about_ca_system_score_gemma":0.00004220088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02367613,"about_ca_topic_score_gemma":0.0004276669,"domain_scores_codex":[0.998525,0.0001641755,0.0002028087,0.0004444518,0.000319608,0.0003440159],"domain_scores_gemma":[0.999309,0.00003200513,0.0001056944,0.0004660678,0.00003141044,0.00005580718],"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":[0.00005240299,0.00006654266,0.00001294483,0.000001197856,0.0001090571,0.00001271724,0.0001861924,0.003440425,0.9730281,0.006711098,0.007983587,0.00839576],"study_design_scores_gemma":[0.0002448474,0.00008559314,0.0001252197,0.000004182635,0.00009802338,0.00006604702,0.0002071543,0.0002122104,0.7730945,0.003821005,0.2216871,0.000354084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794086,0.0003618865,0.0006116767,0.01142997,0.001123043,0.0001061142,0.00001295461,0.0005688008,0.006376945],"genre_scores_gemma":[0.9953271,0.00003279431,0.0001229039,0.002022763,0.0005253232,0.0003433244,0.0002163802,0.000040369,0.001369093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2137035,"threshold_uncertainty_score":0.9828253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118590188933047,"score_gpt":0.2114540633853403,"score_spread":0.2002681614960098,"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."}}