Machine learning to predict morphology, topography and mechanical properties of sustainable gelatin-based electrospun scaffolds.

ABSTRACT: Electrospinning is an outstanding manufacturing technique for producing nano-micro-scaled fibrous scaffolds comparable to biological tissues. However, the solvents used are normally hazardous for the health and the environment, which compromises the sustainability of the process and the industrial scaling. This novel study compares different machine learning models to predict how green solvents affect the morphology, topography and mechanical properties of gelatin-based scaffolds. Gelatin-based scaffolds were produced with different concentrations of distillate water (dH₂O), acetic acid (HAc) and dimethyl sulfoxide (DMSO). 2214 observations, 12 machine learning approaches, including Generalised Linear Models, Generalised Additive Models, Generalised Additive Models for Location,

SUBMITTER: Roldan E 

PROVIDER: S-EPMC11385233 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature