{"id":"W4408785496","doi":"10.1021/acs.energyfuels.4c06271","title":"Optimal Design of a Hybrid Liquid Air Energy Storage System Utilizing Waste Heat Recovery for Hydrogen and Power Production","year":2025,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Mitacs","keywords":"Process engineering; Waste heat; Environmental science; Waste management; Production (economics); Waste heat recovery unit; Energy storage; Hydrogen production; Power (physics); Energy (signal processing); Hydrogen storage; Energy recovery; Hydrogen; Chemistry; Heat exchanger; Thermodynamics; Engineering; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004049331,0.0008805585,0.001353355,0.0007129734,0.0007232574,0.001890174,0.0008167847,0.001217214,0.0033139],"category_scores_gemma":[0.0003737177,0.0008727068,0.0008715899,0.0004345636,0.0005697881,0.0007859074,0.0006709066,0.0004634102,0.0004014238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001201268,"about_ca_system_score_gemma":0.00202098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007078222,"about_ca_topic_score_gemma":0.00789715,"domain_scores_codex":[0.9997875,0.000036182,0.000009933706,0.00005745513,0.00004774786,0.00006123268],"domain_scores_gemma":[0.9998341,0.00005337902,0.00003956413,0.000006108863,0.00004813294,0.0000187139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001993402,0.00009280219,0.0006844589,0.0002126702,0.00005319757,0.0002111148,0.00004538576,0.9706556,0.01714711,0.001390439,0.0004652867,0.008842564],"study_design_scores_gemma":[0.00003580524,0.0001552899,0.0002897022,0.00000638582,0.00002887168,0.00001464161,0.00003949443,0.9969814,0.001739768,0.0002834061,0.0004157229,0.00000957336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5491487,0.002217879,0.4130805,0.0006635603,0.0001647461,0.0004938978,0.0004608835,0.0006055855,0.03316425],"genre_scores_gemma":[0.9814088,0.0002532171,0.01526733,0.00002833449,0.000009545503,0.0002176273,0.00008008282,0.00001879543,0.002716325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007078222,"threshold_uncertainty_score":0.01407409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223286619164188,"score_gpt":0.2175044395494625,"score_spread":0.2052715733578206,"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."}}