{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008961355,0.00009168335,0.0003159047,0.0001187594,0.0000649352,0.00002614074,0.0001553075,0.00004896897,0.000002259766],"category_scores_gemma":[0.0003193537,0.0000447833,0.0001825309,0.0004010626,0.00006805104,0.0001038859,0.00001711833,0.0001051879,5.340014e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001635911,"about_ca_system_score_gemma":0.000005965116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002757585,"about_ca_topic_score_gemma":0.000002445786,"domain_scores_codex":[0.999198,0.00004203575,0.0004421495,0.00006321522,0.0001577477,0.00009686006],"domain_scores_gemma":[0.9988957,0.0006225909,0.0002121615,0.0001133162,0.0001293854,0.00002682728],"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":[0.0007075218,0.00006052151,0.000300511,0.0004047084,0.000958477,8.630771e-8,0.001781896,0.0726411,0.9073088,0.0007701247,0.00003073271,0.01503548],"study_design_scores_gemma":[0.001127778,0.0001043769,0.0009317898,0.0001019485,0.002426021,0.0000134382,0.0008877886,0.4781938,0.5098571,0.006131933,0.00007380979,0.0001502636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9243602,0.0004722805,0.07464126,0.0003057398,0.00004651145,0.0001473137,0.000003664703,0.00000348813,0.00001953257],"genre_scores_gemma":[0.9989254,0.00005097079,0.0008261442,0.00004747133,0.0001322247,0.000008124014,0.000002934544,0.000005956159,7.909834e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4055527,"threshold_uncertainty_score":0.182621,"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."}}