Moisture Sorption Response of Paper Subjected to Ramp Humidity Changes: Modeling and Experiments

Efficacy of hydroxypropyl-guar drops in improving tear film index and ocular surface dynamics using two treatment methods under a controlled desiccating environment

AIM

This study aimed to assess the efficacy of hp-guar eye drops on tear film index and ocular surface dynamics under desiccating conditions using protection and relief treatment modalities.

METHODOLOGY

The 12 normal, non-dry eye participants were subjected to adverse environmental conditions using a Controlled Environment Chamber (CEC) where the relative humidity (RH) was 5% and the ambient temperature was 21 °C. The participants were screened for ocular symptoms, tear osmolarity, ocular surface temperature (OST), tear production using the Ocular Surface Disease Index questionnaire (OSDI), OcuSense TearLab Osmometer, FLIR System ThermaCAM P620, and Schirmer strips. Tear production was calculated by the Tear Function Index test (TFI).

RESULTS

The mean tear film osmolarity decreased significantly from 296 mOsm/L at 40% RH to 285 mOsm/L at 5% RH (p = 0.01). Conflicting responses were seen for osmolarity in protection and relief. Mean tear osmolarity was significantly higher in the protection method in comparison to the relief method (p = 0.005). The mean TFI increased from 557 at 40% to 854 at 5% (p = 0.02). A significant increase in TFI was observed in the relief method in comparison with both 40% (p = 0.001) and 5% (p = 0.04). In the relief method, the mean TFI score went up to 1139 when hp-guar was installed. A significant improvement in ocular comfort was experienced in both the protection (p = 0.041) and relief (p = 0.010) methods at 5% RH. The instillation of hp-guar drops in the relief method resulted in a significant reduction in OST. The mean OST dropped to 33.01 ºC, significantly lower than the recorded OST for both normal (p = 0.040) and dry (p = 0.014) environmental conditions.

CONCLUSION

Hp-guar drops significantly improve tear film parameters under a desiccating environment, however, tear film parameters respond differently to the management modalities. In the protection method, tear film osmolarity was protected against a dry environment, while in the relief mode, an improvement in tear production and a decrease in ocular surface temperature were seen. Hp-guar performance could be maximized for the management of exposure to adverse environments by using a treatment protocol that targets the most affected parameters in each group of patients. Using CEC has the potential to provide researchers with a readily available method to evaluate the efficiency of tear supplementation.

Transport of Organic Volatiles through Paper: Physics-Informed Neural Networks for Solving Inverse and Forward Problems

Transport of volatile organic compounds (VOCs) through porous media with active surfaces takes place in many important applications, such as in cellulose-based materials for packaging. Generally, it is a complex process that combines diffusion with sorption at any time. To date, the data needed to use and validate the mathematical models proposed in literature to describe the mentioned processes are scarce and have not been systematically compiled. As an extension of the model of Ramarao et al. (Dry Technol 21(10):2007–2056, 2003) for the water vapor transport through paper, we propose to describe the transport of VOCs by a nonlinear Fisher–Kolmogorov–Petrovsky–Piskunov equation coupled to a partial differential equation (PDE) for the sorption process. The proposed PDE system contains specific material parameters such as diffusion coefficients and adsorption rates as multiplication factors. Although these parameters are essential for solving the PDEs at a given time scale, not all of the required parameters can be directly deduced from experiments, particularly diffusion coefficients and sorption constants. Therefore, we propose to use experimental concentration data, obtained for the migration of dimethyl sulfoxide (DMSO) through a stack of paper sheets, to infer the sorption constant. These concentrations are considered as the outcome of a model prediction and are inserted into an inverse boundary problem. We employ Physics-Informed Neural Networks (PINNs) to find the underlying sorption constant of DMSO on paper from this inverse problem. We illustrate how to practically combine PINN-based calculations with experimental data to obtain trustworthy transport-related material parameters. Finally we verify the obtained parameter by solving the forward migration problem via PINNs and finite element methods on the relevant time scale and show the satisfactory correspondence between the simulation and experimental results.