{"id":"W4411387571","doi":"10.1016/j.polymer.2025.128695","title":"Thermally sensitive and tunable water-soluble polymer molds for the preparation of porous hydrogels","year":2025,"lang":"en","type":"article","venue":"Polymer","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Polytechnique Montréal","funders":"Institut TransMedTech; Fonds de recherche du Québec – Nature et technologies; Canada First Research Excellence Fund; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Self-healing hydrogels; Porosity; Polymer; Chemical engineering; Materials science; Polymer chemistry; Polymer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001160683,0.0002839998,0.00009181537,0.000170265,0.00006248676,0.0001813121,0.0001479918,0.0001411981,0.0007872094],"category_scores_gemma":[0.0001532516,0.0001553737,0.0001774598,0.00009222067,0.0002008982,0.0002139291,0.0001910371,0.0003152448,0.0002501534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000144923,"about_ca_system_score_gemma":0.00009861178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001457454,"about_ca_topic_score_gemma":0.0003558529,"domain_scores_codex":[0.9999298,0.000005717628,0.000006773283,0.00001811553,0.00002501932,0.00001449656],"domain_scores_gemma":[0.9998467,0.00002760105,0.00008538491,0.00001683069,0.000008887982,0.00001451383],"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":[0.000009047014,0.000003669956,0.00003114534,0.00002222758,0.000001584046,0.00001470723,0.000007701789,0.0001194476,0.9987987,0.0001098165,0.00001487802,0.0008670618],"study_design_scores_gemma":[0.000002432106,0.00001974478,0.000202277,0.000001959336,0.000001871049,0.00002457299,0.000002172865,0.0004789003,0.998646,0.00001625295,0.0006017937,0.000002095185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421373,0.001417242,0.05205579,0.00008172161,0.00007451545,0.00007225786,0.0003882726,0.0004836426,0.003289221],"genre_scores_gemma":[0.983753,0.0005158022,0.01399768,0.00002354574,0.00001230837,0.00003893904,0.0001154011,0.00005145947,0.001491961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007872094,"threshold_uncertainty_score":0.002633452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008845098122167577,"score_gpt":0.2312953274746681,"score_spread":0.2224502293525006,"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."}}