{"id":"W2094258404","doi":"10.1007/s10973-014-4240-2","title":"Using XANES to obtain mechanistic information for the hydrolysis of CuCl2 and the decomposition of Cu2OCl2 in the thermochemical Cu–Cl cycle for H2 production","year":2014,"lang":"en","type":"article","venue":"Journal of Thermal Analysis and Calorimetry","topic":"Chemical Looping and Thermochemical Processes","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; Ontario Tech University","funders":"Argonne National Laboratory; Fuel Cell Technologies Program; Griffith University; University of Ontario Institute of Technology; University of Chicago","keywords":"XANES; Chemistry; Decomposition; Copper; Hydrolysis; Chemical decomposition; Chlorine; Inorganic chemistry; Thermochemical cycle; Absorption (acoustics); Thermal decomposition; Analytical Chemistry (journal); Hydrogen production; Spectroscopy; Hydrogen; Materials science; 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":[0.0004162377,0.0004616966,0.0001891986,0.0003109103,0.0003373211,0.000485484,0.000382371,0.0004538871,0.001521061],"category_scores_gemma":[0.0004825672,0.000266093,0.0002697969,0.0003290203,0.0003939284,0.000983373,0.0002186248,0.0008806915,0.0002208452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008123197,"about_ca_system_score_gemma":0.0004811126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856055,"about_ca_topic_score_gemma":0.004105378,"domain_scores_codex":[0.999792,0.00002720663,0.00001111722,0.0000529426,0.00007003195,0.00004670075],"domain_scores_gemma":[0.9998485,0.00004561567,0.00003573122,0.000021716,0.00003626173,0.00001220366],"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":[0.0003910946,0.00005204057,0.000629337,0.0001934449,0.00001651074,0.00007506109,0.0001001166,0.000768859,0.9934984,0.0006660043,0.0001939721,0.00341517],"study_design_scores_gemma":[0.00001640292,0.00006101311,0.00116407,0.000006229435,0.000007110731,0.0000322288,0.00004409252,0.003671291,0.993849,0.000113924,0.001025809,0.000008888727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821113,0.001318928,0.01150653,0.0002804193,0.00004655403,0.00005807584,0.0005964587,0.0001253542,0.003956426],"genre_scores_gemma":[0.9951592,0.0003349648,0.003346276,0.00004551939,0.000005435755,0.00002314835,0.0002150384,0.00002001293,0.0008503867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001856055,"threshold_uncertainty_score":0.005893826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007756193571791921,"score_gpt":0.2444591112254538,"score_spread":0.2367029176536619,"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."}}