{"id":"W2903809126","doi":"10.1080/02678292.2018.1555723","title":"Hydrogen-bonded LC nanocomposites: characterisation of nanoparticle-LC interactions by solid-state NMR and FTIR spectroscopies","year":2018,"lang":"en","type":"article","venue":"Liquid Crystals","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fourier transform infrared spectroscopy; Materials science; Nanocomposite; Nanoparticle; Hydrogen bond; Solid-state; Center (category theory); Solid-state nuclear magnetic resonance; Polymer science; Nanotechnology; Chemical engineering; Physical chemistry; Molecule; Organic chemistry; Chemistry; Crystallography; Physics; Nuclear magnetic resonance; Engineering","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":[0.00005375848,0.000172165,0.0002297738,0.00004394231,0.0001727057,0.00003470803,0.0001375507,0.00006130282,0.0003822567],"category_scores_gemma":[0.00001921883,0.0001757662,0.00005346187,0.0001264982,0.0002301382,0.0002284052,0.00008558024,0.0001035949,0.00001513779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004478238,"about_ca_system_score_gemma":0.00001806354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002073697,"about_ca_topic_score_gemma":0.000009838987,"domain_scores_codex":[0.9989208,0.00001395541,0.0003973262,0.0002967951,0.0001228908,0.0002482501],"domain_scores_gemma":[0.9991683,0.00005341295,0.000245204,0.0003496995,0.0000962311,0.00008713907],"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":[0.00007732455,0.00009477977,0.0001211694,0.00004170855,0.00002465527,5.378661e-7,0.0004502306,0.000001936096,0.9981427,0.0002523862,0.0005285582,0.0002639676],"study_design_scores_gemma":[0.0002095465,0.0001377988,0.00003750455,0.0000695841,0.00002055286,0.00001247757,0.000121403,0.000240223,0.9747373,0.0009090645,0.02333403,0.000170462],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958528,0.0001204595,0.001129169,0.0003652969,0.0000134958,0.0001652228,0.0001692408,0.0001940614,0.001990207],"genre_scores_gemma":[0.9964143,0.0001170926,0.001853984,0.0001057888,0.00008956849,0.0001138383,0.00006582258,0.00002910639,0.00121051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02340539,"threshold_uncertainty_score":0.7167536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009854846379109036,"score_gpt":0.2882896352006541,"score_spread":0.278434788821545,"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."}}