{"id":"W2790715683","doi":"10.1002/chem.201705818","title":"Metallothermic Reduction of Silica Nanoparticles to Porous Silicon for Drug Delivery Using New and Existing Reductants","year":2018,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"Silicon Nanostructures and Photoluminescence","field":"Materials Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Porosity; Melting point; Chemical engineering; Reducing agent; Porous silicon; Materials science; Nanoparticle; Metal; Morphology (biology); Drug delivery; Chemistry; Nanotechnology; Metallurgy; Composite material","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":[0.0005433668,0.000143923,0.0001895247,0.00002742752,0.0002309241,0.0001044096,0.0002346888,0.00002193981,0.0001976716],"category_scores_gemma":[0.0002264814,0.0001310001,0.00004489588,0.0001263456,0.0001686809,0.0001287517,0.00007279897,0.00008183232,0.000005174558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003339286,"about_ca_system_score_gemma":0.0001091929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002716458,"about_ca_topic_score_gemma":8.749394e-7,"domain_scores_codex":[0.9988343,0.0000693599,0.0003937136,0.0002732748,0.0001459355,0.0002833995],"domain_scores_gemma":[0.9990424,0.00002705423,0.0002805936,0.000186587,0.0002118368,0.0002515356],"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":[0.0001396369,0.00001248216,0.0000441651,0.00003727857,0.00000764377,0.00000800867,0.0007125284,0.00001088475,0.9953446,0.000001325278,0.0004609417,0.003220508],"study_design_scores_gemma":[0.000398369,0.00007327818,0.0002712679,0.0001235718,0.00003304282,0.001167978,0.0002953369,0.0002544566,0.9967908,0.00006048362,0.0003852903,0.0001460692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990616,0.0002495764,0.00003710588,0.00006041307,0.0002963666,0.0001073947,0.00001189231,0.00002201872,0.0001536768],"genre_scores_gemma":[0.9947065,0.000009418366,0.004065244,0.00003770082,0.0008941295,6.330546e-7,8.168351e-7,0.00002870979,0.0002568904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004355098,"threshold_uncertainty_score":0.5342029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03598848981027677,"score_gpt":0.2718599013339199,"score_spread":0.2358714115236431,"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."}}