{"id":"W4281723131","doi":"10.1021/acs.energyfuels.2c00763","title":"Development of an Efficient System for Blue Energy Production Based on Reverse Electrodialysis (RED) by Optimizing Electrolyte Composition: Experimental and Theoretical Simulations","year":2022,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Membrane-based Ion Separation Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea","keywords":"Electrolyte; Reversed electrodialysis; Chemistry; Electrodialysis; Membrane; Diffusion; Ion exchange; Adsorption; Ionic radius; Inorganic chemistry; Chemical engineering; Analytical Chemistry (journal); Ion; Chromatography; Thermodynamics; Physical chemistry; Electrode; Organic chemistry","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":[0.0002170712,0.0001662112,0.0002094467,0.0001972672,0.0002798538,0.00001950137,0.00009116862,0.00004684086,0.00007434245],"category_scores_gemma":[0.000005240782,0.0001880234,0.00005252416,0.0002207334,0.00003279894,0.00005641432,0.00001803191,0.00007098558,1.650424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003187018,"about_ca_system_score_gemma":0.00004106081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003824952,"about_ca_topic_score_gemma":0.000001100469,"domain_scores_codex":[0.9987795,0.0001131094,0.0003493584,0.0002908133,0.0002731703,0.0001940275],"domain_scores_gemma":[0.999534,0.00005997493,0.0000747672,0.0002154483,0.00004803266,0.00006781895],"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":[0.00008853638,0.0000820756,1.333684e-7,0.00001564457,0.00001857979,3.435686e-7,0.00009232015,0.4213821,0.5681943,0.009683845,0.0001308196,0.0003113194],"study_design_scores_gemma":[0.0002663402,0.0001502998,2.400642e-7,0.00001024621,0.0000147826,0.000002684754,0.00006008721,0.3337348,0.663663,0.00001215485,0.001963848,0.0001216203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6503317,0.0001597966,0.3484999,0.00006387081,0.0001248085,0.000226085,0.00003534701,0.0003612609,0.000197238],"genre_scores_gemma":[0.9821092,0.000002322595,0.01689104,0.00006289756,0.00004660591,0.0004342388,0.0003992749,0.00003672666,0.00001771492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3317775,"threshold_uncertainty_score":0.7667373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006271024792274034,"score_gpt":0.2223633405351254,"score_spread":0.2160923157428514,"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."}}