{"id":"W3020571698","doi":"10.1016/j.jobab.2020.04.003","title":"Nanocomposite egg shell powder with in situ generated silver nanoparticles using inherent collagen as reducing agent","year":2020,"lang":"en","type":"article","venue":"Journal of Bioresources and Bioproducts","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":165,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Mongkut's University of Technology North Bangkok; Thailand Research Fund","keywords":"Thermogravimetric analysis; Nanocomposite; Silver nanoparticle; Materials science; Fourier transform infrared spectroscopy; Scanning electron microscope; Thermal stability; Nanoparticle; Antibacterial activity; Nuclear chemistry; Powder diffraction; Chemical engineering; Transmission electron microscopy; In situ; Reducing agent; Nanotechnology; Composite material; Chemistry; Organic chemistry; Bacteria; Crystallography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002512127,0.0001422994,0.0002357828,0.0001031466,0.0001246854,0.0002012698,0.0001030501,0.00004975986,0.0001273485],"category_scores_gemma":[0.00003131117,0.0001010459,0.00003107237,0.0004032258,0.00005948004,0.0002530567,0.00004621108,0.00007184321,0.0000102452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004533795,"about_ca_system_score_gemma":0.00007650095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005870626,"about_ca_topic_score_gemma":0.00001722603,"domain_scores_codex":[0.9987803,0.00008047626,0.0004241915,0.0002482296,0.0002774218,0.0001893503],"domain_scores_gemma":[0.9992027,0.00001012059,0.0003780269,0.00009012155,0.0001522214,0.0001668015],"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":[0.0003985562,0.00007714668,0.001244592,0.0000308981,0.0000132099,0.0000859315,0.002363852,0.0005594658,0.9940125,0.000002621981,0.00007812281,0.001133156],"study_design_scores_gemma":[0.00063171,0.000343473,0.006220582,0.00009623639,0.00003591417,0.0002155234,0.0006332656,0.0003628672,0.987079,0.000002537811,0.004227586,0.0001512854],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970372,0.0007178248,0.00001026737,0.001857464,0.0001661405,0.0001532756,0.00000329651,0.00001337152,0.00004111192],"genre_scores_gemma":[0.9984319,0.00007357071,0.0006710262,0.000384406,0.000340785,8.242933e-7,0.000001864246,0.00001355671,0.00008204838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00693343,"threshold_uncertainty_score":0.4120533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507748041397262,"score_gpt":0.240469819140941,"score_spread":0.2153923387269683,"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."}}