{"id":"W4409149565","doi":"10.1002/cjce.25699","title":"Efficient elevated temperature hydrogen direct purification and separation technology","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Hydrogen Storage and Materials","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tsinghua University; National Natural Science Foundation of China; U.S. Department of Energy","keywords":"Separation (statistics); Hydrogen; Materials science; Chemistry; Chemical engineering; Chromatography; Process engineering; Computer science; Engineering; Machine learning; Organic chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001117247,0.0001755942,0.0001787174,0.0002338908,0.0001463849,0.0003962969,0.0003667185,0.0002202312,0.001350223],"category_scores_gemma":[0.0001054397,0.0001296683,0.0002040412,0.0002684762,0.0002347405,0.000453967,0.0003087004,0.0003815119,0.0005557961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004309702,"about_ca_system_score_gemma":0.0003646431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007711683,"about_ca_topic_score_gemma":0.001066214,"domain_scores_codex":[0.9998205,0.00001460768,0.000006116814,0.00002837491,0.0001056168,0.00002486069],"domain_scores_gemma":[0.9999536,0.00000699253,0.00001108878,0.000006461663,0.00001796724,0.000003824391],"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":[0.00003634213,0.00001890358,0.0002950561,0.0001294701,0.000009260505,0.00005445235,0.00001977427,0.0009142446,0.9807063,0.001750414,0.0002604804,0.01580527],"study_design_scores_gemma":[0.00000849429,0.00009453324,0.000717881,0.000003987676,0.00001184959,0.0001197943,0.00001691003,0.007313815,0.9828647,0.0002051222,0.008637529,0.000005457602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8460066,0.004957549,0.1248115,0.0002739744,0.0001015729,0.00009693528,0.0003282952,0.0009842522,0.02243937],"genre_scores_gemma":[0.9766758,0.000888548,0.01819199,0.000033449,0.00001538049,0.00002019582,0.0001452806,0.00001488249,0.00401447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001350223,"threshold_uncertainty_score":0.004516959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003943055136109925,"score_gpt":0.2059822213878071,"score_spread":0.2020391662516972,"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."}}