Establishing a non-destructive method for spatially assessing advanced glycation end-products (AGEs) is a potentially useful step toward investigating the mechanistic role of AGEs in bone quality. To test the hypothesis that the shape of the amide I in the Raman spectroscopy (RS) analysis of bone matrix changes upon AGE accumulation, we incubated paired cadaveric cortical bone in ribose or glucose solutions and in control solutions for 4 and 16 weeks, respectively, at 37°C. Acquiring 10 spectra per bone with a 20X objective and a 830 nm laser, RS was sensitive to AGE accumulation (confirmed by biochemical measurements of pentosidine and fluorescent AGEs). Hyp/Pro ratio increased upon glycation using either 0.1 M ribose, 0.5 M ribose or 0.5 M glucose. Glycation also decreased the amide I sub-peak ratios (cm ) 1668/1638 and 1668/1610 when directly calculated using either second derivative spectrum or local maxima of difference spectrum, though the processing method (eg, averaged spectrum vs individual spectra) to minimize noise influenced detection of differences for the ribose-incubated bones. Glycation however did not affect these sub-peak ratios including the matrix maturity ratio (1668/1690) when calculated using indirect sub-band fitting. The amide I sub-peak ratios likely reflected changes in the collagen I structure.
Here, we report a new type of reconfigurable anticounterfeiting coating enabled by integrating the scientific principles of photonic crystal and shape memory polymer (SMP). The autonomous infusion of uncured oligomers in a polydimethylsiloxane (PDMS) stamp into a templated macroporous SMP photonic crystal coating, which was confirmed by quantitative X-ray photoelectron spectroscopy analysis, can program an iridescent pattern on the transparent SMP membrane with deformed macropores. By manipulation of the unconventional all-room-temperature shape memory effects exhibited by the shape memory copolymer comprising polyethylene glycol diacrylate and ethoxylated trimethylolpropane triacrylate, the iridescent pattern can be easily and instantaneously concealed and revealed by immersing in common household liquids (e.g., ethanol and water). Systematic experiments and theoretical simulations using scanning electron microscopy, atomic force microscopy, optical spectroscopy, scalar wave approximation modeling, and contact angle measurements reveal the major mechanism underlying the anticounterfeiting concealing and revealing processes: the compressive deformation of periodic macropores induced by capillary pressure created by solvent evaporation. Apparent water contact angle measurements show that the infusion of hydrophobic PDMS oligomers into hydrophilic macroporous SMP coatings leads to a large increase in water contact angle from ∼35° to ∼70°, which significantly changes the capillary pressure and the final configuration of the SMP photonic crystals. In addition to rendering a facile anticounterfeiting mechanism, the novel oligomer-infusion-induced chromogenic effects and modification of surface wettability might lead to important applications in developing new chromogenic sensors for noninvasively monitoring molecular diffusion at solid–solid interfaces and durable superhydrophobic and/or superomniphobic coatings.
Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2) of ∼0.99 and a mean squared error of ∼2.9 × 10–5. Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.
Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell–Stephan (MS) diffusion rates. Here we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.
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