{"id":"W4409156819","doi":"10.1063/5.0263344","title":"Mechanical interaction between a hydrogel and an embedded cell in biomicrofluidic applications","year":2025,"lang":"en","type":"article","venue":"Biomicrofluidics","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Mathematical Sciences; Basic and Applied Basic Research Foundation of Guangdong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Self-healing hydrogels; Poromechanics; Microfluidics; Materials science; Biomedical engineering; Nanotechnology; Finite element method; Tissue engineering; Biocompatibility; Extracellular matrix; Porous medium; Porosity; Chemistry; Composite material; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001193663,0.0002395489,0.0001487261,0.0001484731,0.0002720495,0.0003621548,0.0002353693,0.0005456348,0.0009399815],"category_scores_gemma":[0.0002252415,0.0001494687,0.0001601541,0.000101904,0.0004839321,0.0003201261,0.0003960937,0.000176636,0.0001466277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004043917,"about_ca_system_score_gemma":0.0004314157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009195099,"about_ca_topic_score_gemma":0.001137847,"domain_scores_codex":[0.9999298,0.00001610669,0.000003379722,0.00001305799,0.00002333452,0.00001436828],"domain_scores_gemma":[0.9999404,0.00003255241,0.00001165226,0.000004868755,0.000003986835,0.000006514139],"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":[0.00005318849,0.00005835488,0.000654803,0.000145724,0.00001396536,0.0004018598,0.00008625558,0.08725192,0.8892239,0.008738528,0.0002071907,0.01316428],"study_design_scores_gemma":[0.00004812932,0.0003144036,0.002101859,0.0000497373,0.00003235018,0.0005082975,0.0001439446,0.5497339,0.4306772,0.004944725,0.01140067,0.00004475122],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7494085,0.002568793,0.2364917,0.0006219276,0.0001319426,0.0000688092,0.0001092681,0.0002474212,0.01035148],"genre_scores_gemma":[0.9727985,0.0006396783,0.02290285,0.00007654363,0.00001985196,0.00004461124,0.00003052085,0.00001926322,0.003468234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009399815,"threshold_uncertainty_score":0.003144622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700156725868607,"score_gpt":0.3080615057108445,"score_spread":0.2910599384521584,"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."}}