{"id":"W3186233651","doi":"10.1016/j.rser.2021.111503","title":"Performances of a multi-product strategy for bioethanol, lignin, and ultra-high surface area carbon from lignocellulose by PHP (phosphoric acid plus hydrogen peroxide) pretreatment platform","year":2021,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Supercapacitor Materials and Fabrication","field":"Materials Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Lignin; Biofuel; Supercapacitor; Chemistry; Phosphoric acid; Activated carbon; Biomass (ecology); Carbon fibers; Cellulose; Lignocellulosic biomass; Hydrogen peroxide; Adsorption; Specific surface area; Chemical engineering; Pulp and paper industry; Nuclear chemistry; Materials science; Organic chemistry; Capacitance; Electrode; Waste management; Catalysis; Composite material; Agronomy; Composite number","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000602214,0.0003612298,0.0008691497,0.0000473716,0.0002318047,0.0001453572,0.0001652251,0.0001385709,0.00006001331],"category_scores_gemma":[0.000093764,0.0002869714,0.00008906116,0.000253471,0.0001121158,0.0003894127,0.00008127894,0.0000437886,0.000001137712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372695,"about_ca_system_score_gemma":0.0002173104,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01656493,"about_ca_topic_score_gemma":0.000254478,"domain_scores_codex":[0.9977695,0.0001078714,0.0006496237,0.0007251276,0.0001872173,0.0005607019],"domain_scores_gemma":[0.9987789,0.00007870958,0.0002826693,0.000460816,0.0002480791,0.0001508331],"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":[0.00007142017,0.0001141396,0.0001556344,0.0006415705,0.00003787668,0.000008867705,0.0003942372,0.0003707662,0.9956528,0.0001438874,0.0004494156,0.001959371],"study_design_scores_gemma":[0.0008707866,0.0002494881,0.00001382326,0.0001308612,0.0001297075,0.00001024956,0.002301578,0.0006681708,0.9819664,0.0003709029,0.01296354,0.0003244469],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.803583,0.1952717,0.0002351332,0.0000330002,0.0001269215,0.0004625493,0.0001256137,0.0000261964,0.0001358524],"genre_scores_gemma":[0.8992262,0.095909,0.001475821,0.00002988853,0.00008982014,0.0001754943,0.0002317607,0.00003053104,0.002831434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09936272,"threshold_uncertainty_score":0.9999582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278910354197518,"score_gpt":0.2424448807495943,"score_spread":0.2145538453298425,"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."}}