Time series Fourier transform infrared spectroscopy for characterization of water vapor sorption in hydrophilic and hydrophobic polymeric films

This work investigates the nature of the molecular interactions between water vapor and polymers using time series Fourier transform infrared (FTIR) spectroscopy. A detailed analysis of the frequency shifts and relative peak intensities led to the conclusion that polyvinyl alcohol (PVOH) sorbed a large quantity of water vapor molecules, resulting in swelling and dissolving of polymer crystallites. Difference spectra were calculated to investigate spectral changes occurring upon sorption by dividing the spectra of polymers during the sorption time series by the spectrum of the dry sample and subsequently subtracting the water vapor spectrum. Based on the absorbance area of the OH stretching vibration region (4000-3000 cm^-1) in difference spectra, the amount of water sorbed was significantly higher in poly-L-lactic acid (PLLA) and polyvinyl chloride (PVC) than in polyethylene (PE) and polytetrafluoroethylene (PTFE), increasing with the hydrophilicity of the surface. The OH stretching band of difference spectra shifted from 3499 cm^-1 for PVC, to 3416 cm^-1 for PE and finally to 3387 cm^-1 for PTFE, indicating a more strengthened hydrogen-bonding network in the PTFE matrix upon water vapor sorption.

Surface Etching of 3D Printed Poly(lactic acid) with NaOH: A Systematic Approach

The article describes a systematic investigation of the effects of an aqueous NaOH treatment of 3D printed poly(lactic acid) (PLA) scaffolds for surface activation. The PLA surface undergoes several morphology changes and after an initial surface roughening, the surface becomes smoother again before the material dissolves. Erosion rates and surface morphologies can be controlled by the treatment. At the same time, the bulk mechanical properties of the treated materials remain unaltered. This indicates that NaOH treatment of 3D printed PLA scaffolds is a simple, yet viable strategy for surface activation without compromising the mechanical stability of PLA scaffolds.

Predictive Modeling of the In Vitro Responses of Preosteoblastic MC3T3-E1 Cells on Polymeric Surfaces Using Fourier Transform Infrared Spectroscopy

Biomaterials' surface properties elicit diverse cellular responses in biomedical and biotechnological applications. Predicting the cell behavior on a polymeric surface is an ongoing challenge due to its complexity. This work proposes a novel modeling methodology based on attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Spectra were collected on wetted polymeric surfaces to incorporate both surface chemistry and information on water-polymer interactions. Results showed that predictive models built with spectra from wetted surfaces ("wet spectra") performed much better than models built using spectra acquired from dry surfaces ("dry spectra"), suggesting that the water-polymer interaction is critically important to the prediction of subsequent cell behavior. The best model was seen to predict total area of focal adhesions with coefficient of determination for prediction (R²_P) of 0.94 and root-mean-square errors of prediction (RMSEP) of 4.03 μm² when tested on an independent experimental set. This work offers new insights into our understanding of cell-biomaterial interactions. The presence of carboxyl groups in polymers promoted larger adhesion areas, yet the formation of carbonyl-to-water interaction decreased adhesion areas. Surface wettability, which was related to the water-polymer interaction, was proven to highly influence cell adhesion. The good predictive ability opens new possibilities for high throughput monitoring of cell attachment on polymeric substrates.

Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces

PLSR with ATR-FTIR chemical imaging predicts protein adhesion on polymeric surfaces well (R² = 0.99, RMSECV = 0.16).