{"id":"W4392622376","doi":"10.1007/s41918-024-00217-w","title":"Emerging Atomically Precise Metal Nanoclusters and Ultrasmall Nanoparticles for Efficient Electrochemical Energy Catalysis: Synthesis Strategies and Surface/Interface Engineering","year":2024,"lang":"en","type":"article","venue":"Electrochemical Energy Reviews","topic":"Nanocluster Synthesis and Applications","field":"Materials Science","cited_by":143,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Institut National de la Recherche Scientifique; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Nanoclusters; Electrocatalyst; Catalysis; Nanoparticle; Nanotechnology; Oxygen evolution; Materials science; Electrochemical energy conversion; Electrochemistry; Metal; Redox; Adsorption; Chemical engineering; Chemistry; Physical chemistry; Electrode; Organic 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004953896,0.0004459391,0.0007308103,0.00008408041,0.0001413095,0.0004143339,0.0003218301,0.0001286546,0.00003593398],"category_scores_gemma":[0.0001960672,0.0003672031,0.0002163314,0.0003236186,0.0001217153,0.0002283894,0.0001267914,0.0001341953,0.000007806318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009440786,"about_ca_system_score_gemma":0.00008164324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002671519,"about_ca_topic_score_gemma":0.00001518963,"domain_scores_codex":[0.99734,0.00008326911,0.0007120425,0.0009477279,0.0002097703,0.0007071372],"domain_scores_gemma":[0.9985933,0.0006521497,0.0001164645,0.0003437926,0.00005161116,0.0002426847],"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":[0.00003703918,0.00005102429,5.701945e-7,0.0002404899,0.00005423734,0.000001139504,0.00007052063,0.0000868697,0.9879506,0.006721048,0.0001311329,0.00465534],"study_design_scores_gemma":[0.0001216899,0.00004377669,6.805176e-7,0.0002531445,0.0002313148,0.00005530104,0.00002919605,0.01765339,0.9437577,0.0001970959,0.03725711,0.0003996651],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8187696,0.09345553,0.08645277,0.000533951,0.0000954652,0.0003329093,0.0000127187,0.0002699935,0.00007705429],"genre_scores_gemma":[0.9877197,0.004023491,0.007321743,0.00006575321,0.0001173921,0.0005744642,0.00001596777,0.00005778218,0.0001037125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1689501,"threshold_uncertainty_score":0.999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008487720598063027,"score_gpt":0.2362469155251399,"score_spread":0.2277591949270769,"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."}}