{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T15:04:36Z","timestamp":1789398276593,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Romanian Ministry of Research, Innovation and Digitalization","award":["30N\/2023"],"award-info":[{"award-number":["30N\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Polymers"],"abstract":"<jats:p>Predicting how polycaprolactone\u2013poly(ethylene glycol) (PCL-blend-PEG) implant coatings lose mass in simulated body fluid (SBF) would shorten costly 16-week immersion campaigns, but published studies typically provide only three to five compositions, too few for flexible data-driven models. We re-analysed the 16-week mass-loss curves of dip-coated PCL, PCL-blend-PEG 3:1, PCL-blend-PEG 1:3, and PEG films on titanium using a fully specified Bayesian workflow. A mechanistically motivated two-process Weibull model, combining a fast PEG dissolution term and a slow PCL hydrolysis term with composition-dependent time scales, was compared against a parsimonious single effective-Weibull baseline under a prespecified decision rule with exact leave-one-composition-out (LOCO) and future-time refits. Both models converged cleanly (maximum R^\u22641.005, zero divergences), yet the two-process model did not outperform the baseline: pooled leave-one-composition-out mean absolute error was 43.7 percentage points for the two-process model versus 37.6 for the baseline, and pooled temporal error 13.2 versus 11.8 percentage points, respectively. The PEG parameters of the two-process model were strongly correlated (r=0.956), precluding separate identification of PEG and PCL kinetics. Empirical coverage of the nominal 90% LOCO intervals was only 21.4% for the two-process model and 3.6% for the baseline. Composition-wise, residuals localise the failure: PEG release inside blends is matrix-retarded rather than instantaneous. We report the negative result transparently, quantify the additional data needed, and release the full reproducible pipeline. These findings define the current predictive limits imposed by the available experimental data and provide guidance for future experimental design.<\/jats:p>","DOI":"10.3390\/polym18182237","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:15:04Z","timestamp":1789395304000},"page":"2237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Two-Process Weibull Modelling of PCL-Blend-PEG Coating Degradation in Simulated Body Fluid: Composition-Held-Out Validation, Identifiability Limits and Exploratory Electrochemical Correlation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6562-6575","authenticated-orcid":false,"given":"Vlad-Cristian","family":"Stoica","sequence":"first","affiliation":[{"name":"Independent Researcher, 031777 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0539-4160","authenticated-orcid":false,"given":"Anita Ioana","family":"Visan","sequence":"additional","affiliation":[{"name":"Lasers Department, National Institute for Lasers, Plasma and Radiation Physics, 409 Atomistilor Street, Magurele, 077190 Ilfov, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Prosolov, K.A., Komarova, E.G., Kazantseva, E.A., Luginin, N.A., Kashin, A.D., Uvarkin, P.V., and Sharkeev, Y.P. (2024). Enhanced Corrosion Resistance and Mechanical Durability of the Composite PLGA\/CaP\/Ti Scaffolds for Orthopedic Implants. Polymers, 16.","DOI":"10.3390\/polym16060826"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1016\/j.progpolymsci.2010.04.002","article-title":"The Return of a Forgotten Polymer\u2014Polycaprolactone in the 21st Century","volume":"35","author":"Woodruff","year":"2010","journal-title":"Prog. Polym. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijpharm.2004.01.044","article-title":"Poly-\u03b5-caprolactone Microspheres and Nanospheres: An Overview","volume":"278","author":"Sinha","year":"2004","journal-title":"Int. J. Pharm."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3487","DOI":"10.1021\/acs.molpharmaceut.0c00515","article-title":"Poly(caprolactone)-Based Coatings on 3D-Printed Biodegradable Implants: A Novel Strategy to Prolong Delivery of Hydrophilic Drugs","volume":"17","author":"Stewart","year":"2020","journal-title":"Mol. Pharm."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.polymdegradstab.2012.05.038","article-title":"Hydrolytic and Enzymatic Degradation of a Poly(\u03b5-caprolactone) Network","volume":"97","author":"Vidaurre","year":"2012","journal-title":"Polym. Degrad. Stab."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1007\/s11095-013-1173-6","article-title":"Lysozyme Release and Polymer Erosion Behavior of Injectable Implants Prepared from PLGA-PEG Block Copolymers and PLGA\/PLGA-PEG Blends","volume":"31","author":"Milacic","year":"2014","journal-title":"Pharm. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yin, H.-M., Qian, J., Zhang, J., Lin, Z.-F., Li, J.-S., Xu, J.-Z., and Li, Z.-M. (2016). Engineering Porous Poly(lactic acid) Scaffolds with High Mechanical Performance via a Solid State Extrusion\/Porogen Leaching Approach. Polymers, 8.","DOI":"10.3390\/polym8060213"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Visan, A.I., Popescu-Pelin, G., Gherasim, O., Mihailescu, A., Socol, M., Zgura, I., Chiritoiu, M., Sima, L.E., Antohe, F., and Ivan, L. (2020). Long-Term Evaluation of Dip-Coated PCL-Blend-PEG Coatings in Simulated Conditions. Polymers, 12.","DOI":"10.3390\/polym12030717"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1038\/s41578-024-00708-8","article-title":"Design of Functional and Sustainable Polymers Assisted by Artificial Intelligence","volume":"9","author":"Tran","year":"2024","journal-title":"Nat. Rev. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4838","DOI":"10.1038\/s41467-023-40459-8","article-title":"Applied Machine Learning as a Driver for Polymeric Biomaterials Design","volume":"14","author":"McDonald","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-Informed Machine Learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41524-018-0081-z","article-title":"A Strategy to Apply Machine Learning to Small Datasets in Materials Science","volume":"4","author":"Zhang","year":"2018","journal-title":"npj Comput. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s41524-023-01000-z","article-title":"Small Data Machine Learning in Materials Science","volume":"9","author":"Xu","year":"2023","journal-title":"npj Comput. Mater."},{"key":"ref_14","first-page":"100916","article-title":"Recent Progress on Machine Learning with Limited Materials Data: Using Tools from Data Science and Domain Knowledge","volume":"11","author":"Zong","year":"2025","journal-title":"J. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijpharm.2005.10.044","article-title":"On the Use of the Weibull Function for the Discernment of Drug Release Mechanisms","volume":"309","author":"Papadopoulou","year":"2006","journal-title":"Int. J. Pharm."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1115\/1.4010337","article-title":"A Statistical Distribution Function of Wide Applicability","volume":"18","author":"Weibull","year":"1951","journal-title":"J. Appl. Mech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0378-5173(83)90064-9","article-title":"Mechanisms of Solute Release from Porous Hydrophilic Polymers","volume":"15","author":"Korsmeyer","year":"1983","journal-title":"Int. J. Pharm."},{"key":"ref_18","first-page":"1593","article-title":"The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo","volume":"15","author":"Hoffman","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e55","DOI":"10.7717\/peerj-cs.55","article-title":"Probabilistic Programming in Python Using PyMC3","volume":"2","author":"Salvatier","year":"2016","journal-title":"PeerJ Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e1516","DOI":"10.7717\/peerj-cs.1516","article-title":"PyMC: A Modern, and Comprehensive Probabilistic Programming Framework in Python","volume":"9","author":"Andreani","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1214\/20-BA1221","article-title":"Rank-Normalization, Folding, and Localization: An Improved R-hat for Assessing Convergence of MCMC (with Discussion)","volume":"16","author":"Vehtari","year":"2021","journal-title":"Bayesian Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s11222-016-9696-4","article-title":"Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC","volume":"27","author":"Vehtari","year":"2017","journal-title":"Stat. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"G\u00e1bor, A., Villaverde, A.F., and Banga, J.R. (2017). Parameter Identifiability Analysis and Visualization in Large-Scale Kinetic Models of Biosystems. BMC Syst. Biol., 11.","DOI":"10.1186\/s12918-017-0428-y"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"160018","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for Scientific Data Management and Stewardship","volume":"3","author":"Wilkinson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1016\/j.biomaterials.2006.01.017","article-title":"How Useful Is SBF in Predicting In Vivo Bone Bioactivity?","volume":"27","author":"Kokubo","year":"2006","journal-title":"Biomaterials"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.21105\/joss.01143","article-title":"ArviZ: A Unified Library for Exploratory Analysis of Bayesian Models in Python","volume":"4","author":"Kumar","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"061006","DOI":"10.1116\/6.0000429","article-title":"Long-Term Hydrolytic Degradation Study of Polycaprolactone Films and Fibers Grafted with Poly(sodium styrene sulfonate): Mechanism Study and Cell Response","volume":"15","author":"Leroux","year":"2020","journal-title":"Biointerphases"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11998-018-0107-y","article-title":"Silane Coupling Agent (SCA) Pretreatment and Polycaprolactone (PCL) Coating for Enhanced Corrosion Resistance for Magnesium","volume":"16","author":"Niu","year":"2019","journal-title":"J. Coat. Technol. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"025005","DOI":"10.1088\/1748-6041\/6\/2\/025005","article-title":"Interaction between a High Purity Magnesium Surface and PCL and PLA Coatings during Dynamic Degradation","volume":"6","author":"Chen","year":"2011","journal-title":"Biomed. Mater."}],"container-title":["Polymers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4360\/18\/18\/2237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:18:35Z","timestamp":1789395515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4360\/18\/18\/2237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":30,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["polym18182237"],"URL":"https:\/\/doi.org\/10.3390\/polym18182237","relation":{},"ISSN":["2073-4360"],"issn-type":[{"value":"2073-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]}}}