{"id":"W4405746023","doi":"10.1002/smll.202409669","title":"Macro‐ and Nano‐Porous Ag Electrodes Enable Selective and Stable Aqueous CO <sub>2</sub> Reduction","year":2024,"lang":"en","type":"article","venue":"Small","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Laboratório Nacional de Nanotecnologia; Agencia Estatal de Investigación; Generalitat de Catalunya; Centro Nacional de Pesquisa em Energia e Materiais; Centres de Recerca de Catalunya; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Electrochemistry; Materials science; Electrode; Aqueous solution; Nano-; Porosity; Carbon dioxide; Macro; Chemical engineering; Reduction (mathematics); Electrochemical reduction of carbon dioxide; Carbon fibers; Nanotechnology; Inorganic chemistry; Chemistry; Catalysis; Organic chemistry; Carbon monoxide; Composite material; 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":[0.00005914398,0.000217996,0.0001313701,0.0001570734,0.0001112002,0.0003463388,0.0002319479,0.0002960756,0.0008464427],"category_scores_gemma":[0.0001393938,0.0001433089,0.0001086231,0.00007675133,0.0002776215,0.0002709745,0.0002357752,0.0001991231,0.0003397069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002039978,"about_ca_system_score_gemma":0.000100545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004655984,"about_ca_topic_score_gemma":0.001119229,"domain_scores_codex":[0.9999129,0.000005047158,0.0000049176,0.00002457646,0.00003366536,0.00001903715],"domain_scores_gemma":[0.9999199,0.00001677791,0.00001995412,0.000009497845,0.00002043334,0.00001352626],"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":[0.00003264604,0.000006705708,0.00007972325,0.00002863855,0.000002469719,0.00003636047,0.000005770475,0.00009840658,0.9965627,0.0001813125,0.000112282,0.002852988],"study_design_scores_gemma":[0.000006029771,0.00004756117,0.001018762,0.000001819761,0.000004879203,0.00009754377,0.000009154615,0.001213847,0.9953505,0.00005958343,0.00218685,0.000003544388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551421,0.002162914,0.02940809,0.0002735845,0.0001503796,0.0000449455,0.0002859915,0.001252813,0.01127919],"genre_scores_gemma":[0.9919689,0.0002779186,0.005848194,0.00003895115,0.00001143631,0.00001085935,0.00005956625,0.0000302725,0.001753971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008464427,"threshold_uncertainty_score":0.002831638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007871168120877218,"score_gpt":0.2200234942862239,"score_spread":0.2121523261653467,"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."}}