{"id":"W3080523027","doi":"10.1016/j.ijbiomac.2020.08.167","title":"Fabrication of spherical lignin nanoparticles using acid-catalyzed condensed lignins","year":2020,"lang":"en","type":"article","venue":"International Journal of Biological Macromolecules","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"State Key Laboratory of Polymer Materials Engineering; Sichuan University; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Lignin; Biorefinery; Chemistry; Softwood; Corn stover; Cellulose; Biorefining; Chemical engineering; Hardwood; Catalysis; Organic chemistry; Biomass (ecology); Pulp and paper industry; Hydrolysis; Materials science; Raw material; Botany; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001551718,0.0002169003,0.0001680021,0.0001263607,0.0001077692,0.0002609499,0.0002331674,0.0003013521,0.0007162968],"category_scores_gemma":[0.0001728029,0.0001522583,0.0002384858,0.00009531934,0.0001301965,0.0001992004,0.0002010883,0.0003266145,0.0003393534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002865231,"about_ca_system_score_gemma":0.0001148355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000339473,"about_ca_topic_score_gemma":0.0009558743,"domain_scores_codex":[0.9998944,0.000009174402,0.000007752546,0.00002699847,0.00003437635,0.00002739268],"domain_scores_gemma":[0.9999177,0.00001805371,0.00001866271,0.00001164265,0.00002090041,0.00001298746],"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":[0.00002243408,0.00001118306,0.00004498614,0.00004434546,0.000004837684,0.00003595437,0.00002170891,0.0003444152,0.9973403,0.0003035654,0.00006999665,0.001756188],"study_design_scores_gemma":[0.000004129697,0.00004305271,0.0002139337,0.000001679311,0.000003440915,0.00002643118,0.000007828818,0.00207085,0.996586,0.00003298876,0.001007133,0.000002497913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716576,0.0007823501,0.02148328,0.0001211121,0.00008763271,0.00004301374,0.0001554332,0.0002470391,0.005422492],"genre_scores_gemma":[0.989171,0.0002138433,0.007865766,0.0000408711,0.000007935838,0.00001886454,0.0001185423,0.00002917738,0.002534008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007162968,"threshold_uncertainty_score":0.002396286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0330036949174351,"score_gpt":0.2577015828703096,"score_spread":0.2246978879528745,"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."}}