{"id":"W4401617495","doi":"10.1016/j.enrev.2024.100110","title":"Facet engineering of semiconductors for boosting photo-charge separation in solar fuel production","year":2024,"lang":"en","type":"article","venue":"Energy Reviews","topic":"TiO2 Photocatalysis and Solar Cells","field":"Energy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Anhui Provincial Key Research and Development Plan; Science and Technology Program of Suzhou; National Natural Science Foundation of China; Key Technologies Research and Development Program; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; University of Science and Technology of China","keywords":"Boosting (machine learning); Facet (psychology); Semiconductor; Materials science; Engineering physics; Optoelectronics; Photovoltaic system; Separation (statistics); Environmental science; Engineering; Electrical engineering; Computer science; Psychology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000145321,0.0003681654,0.0003176999,0.0003518879,0.0001731156,0.0005976326,0.000285273,0.0004885624,0.00118963],"category_scores_gemma":[0.0001394108,0.0002250985,0.0004376109,0.0004126936,0.0002209931,0.0004992363,0.0003101341,0.0007883827,0.0005112494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004575276,"about_ca_system_score_gemma":0.0002462232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006589254,"about_ca_topic_score_gemma":0.001387288,"domain_scores_codex":[0.9998888,0.00001151292,0.000007828627,0.0000257975,0.00004540094,0.00002061528],"domain_scores_gemma":[0.9999684,0.000009672647,0.000005975757,0.000002793054,0.00001009159,0.000003066396],"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":[0.00009436168,0.00007792538,0.0003496856,0.004950566,0.00007346435,0.0004225115,0.0001371034,0.002769383,0.8493011,0.02312115,0.002839828,0.115863],"study_design_scores_gemma":[0.00002309233,0.0003569949,0.001170307,0.00032031,0.0001015821,0.001009453,0.0001073144,0.00632653,0.8028073,0.004881775,0.1828584,0.00003704149],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1669681,0.6920891,0.09022351,0.001140984,0.0009988312,0.00016633,0.0004711162,0.0004923277,0.04744968],"genre_scores_gemma":[0.5850605,0.3723388,0.03227079,0.0003749652,0.0001653541,0.00008876906,0.0003417341,0.00007930321,0.009279782],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00118963,"threshold_uncertainty_score":0.003979683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011947675093888,"score_gpt":0.2841670276868259,"score_spread":0.254047550935887,"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."}}