Direct-ink-write cross-linkable bottlebrush block copolymers for on-the-fly control of structural color.

Additive manufacturing capable of controlling and dynamically modulating structures down to the nanoscopic scale remains challenging. By marrying additive manufacturing with self-assembly, we develop a UV (ultra-violet)-assisted direct ink write approach for on-the-fly modulation of structural color by programming the assembly kinetics through photo-cross-linking. We design a photo-cross-linkable bottlebrush block copolymer solution as a printing ink that exhibits vibrant structural color (i.e., photonic properties) due to the nanoscopic lamellar structures formed post extrusion. By dynamically modulating UV-light irradiance during printing, we can program the color of the printed material to access a broad spectrum of visible light with a single ink while also creating color gradients not previously possible. We unveil the mechanism of this approach using a combination of coarse-grained simulations, rheological measurements, and structural characterizations. Central to the assembly mechanism is the matching of the cross-linking timescale with the assembly timescale, which leads to kinetic trapping of the assembly process that evolves structural color from blue to red driven by solvent evaporation. This strategy of integrating cross-linking chemistry and out-of-equilibrium processing opens an avenue for spatiotemporal control of self-assembled nanostructures during additive manufacturing.

Liquid-metal-based three-dimensional microelectrode arrays integrated with implantable ultrathin retinal prosthesis for vision restoration.

Electronic retinal prostheses for stimulating retinal neurons are promising for vision restoration. However, the rigid electrodes of conventional retinal implants can inflict damage on the soft retina tissue. They also have limited selectivity due to their poor proximity to target cells in the degenerative retina. Here we present a soft artificial retina (thickness, 10 µm) where flexible ultrathin photosensitive transistors are integrated with three-dimensional stimulation electrodes of eutectic gallium-indium alloy. Platinum nanoclusters locally coated only on the tip of these three-dimensional liquid-metal electrodes show advantages in reducing the impedance of the stimulation electrodes. These microelectrodes can enhance the proximity to the target retinal ganglion cells and provide effective charge injections (72.84 mC cm-2) to elicit neural responses in the retina. Their low Young's modulus (234 kPa), owing to their liquid form, can minimize damage to the retina. Furthermore, we used an unsupervised machine learning approach to effectively identify the evoked spikes to grade neural activities within the retinal ganglion cells. Results from in vivo experiments on a retinal degeneration mouse model reveal that the spatiotemporal distribution of neural responses on their retina can be mapped under selective localized illumination areas of light, suggesting the restoration of their vision.

Ion trap and release dynamics enables nonintrusive tactile augmentation in monolithic sensory neuron.

An iontronic-based artificial tactile nerve is a promising technology for emulating the tactile recognition and learning of human skin with low power consumption. However, its weak tactile memory and complex integration structure remain challenging. We present an ion trap and release dynamics (iTRD)-driven, neuro-inspired monolithic artificial tactile neuron (NeuroMAT) that can achieve tactile perception and memory consolidation in a single device. Through the tactile-driven release of ions initially trapped within iTRD-iongel, NeuroMAT only generates nonintrusive synaptic memory signals when mechanical stress is applied under voltage stimulation. The induced tactile memory is augmented by auxiliary voltage pulses independent of tactile sensing signals. We integrate NeuroMAT with an anthropomorphic robotic hand system to imitate memory-based human motion; the robust tactile memory of NeuroMAT enables the hand to consistently perform reliable gripping motion.

Addressing diffusion behavior and impact in an epoxy-amine cure system using molecular dynamics simulations.

To deepen understanding of diffusion-controlled crosslinking, molecular dynamics (MD) simulations are carried out by taking the diffusion image of 3,3'-diamino diphenyl sulfone (3,3'-DDS) and polyethersulfone (PES) with epoxy resin varying temperatures from 393.15 to 473.15 K over crosslinking conversion of 0-85%. The diffusion of PES and 3,3'-DDS into the bulk increased with increasing the temperature as a result of enhanced mobility of the molecules when the difference between the glass-transition temperature (Tg) and the curing temperature. Beyond the onset points of the converged crosslinking conversion ratio of 3,3'-DDS and PES, their diffusion properties are obviously restricted with crosslinking conversion ratio. At low crosslinking conversion ratios (> 10%), the diffusion coefficients of triglycidyl p-aminophenol (TGAP) were 1.1 times higher than those of diglycidyl ether of bisphenol F (DGEBF) because of the lower molecular weight of TGAP. On the other hand, the diffusion coefficients of TGAP decreased when the crosslinking ratio was up to ~ 60% because, compared with DGEBF, it had more functional groups available to react with the curing agent. At higher crosslinking ratios, the diffusion coefficients of both resins converged to zero as a result of their highly crosslinked structures.

Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations.

Epoxy resin is an of the most widely used adhesives for various applications owing to its outstanding properties. The performance of epoxy systems varies significantly depending on the composition of the base resin and curing agent. However, there are limitations in exploring numerous formulations of epoxy resins to optimize adhesive properties because of the expense and time-consuming nature of the trial-and-error process. Herein, molecular dynamics (MD) simulations and machine learning (ML) methods were used to overcome these challenges and predict the adhesive properties of epoxy resin. Datasets for diverse epoxy adhesive formulations were constructed by considering the degree of crosslinking, density, free volume, cohesive energy density, modulus, and glass transition temperature. A linear correlation analysis demonstrated that the content of the curing agents, especially dicyandiamide (DICY), had the greatest correlation with the cohesive energy density. Moreover, the content of tetraglycidyl methylene dianiline (TGMDA) had the highest correlation with the modulus, and the content of diglycidyl ether of bisphenol A (DGEBA) had the highest correlation with the glass transition temperature. An optimized artificial neural network (ANN) model was constructed using test sets divided from MD datasets through error and linear regression analyses. The root mean square error (RMSE) and correlation coefficient (R2) showed the potential of each model in predicting epoxy properties, with high linear correlations (0.835-0.986). This technique can be extended for optimizing the composition of other epoxy resin systems.

Molecular dynamics simulation study on the effect of perfluorosulfonic acid side chains on oxygen permeation in hydrated ionomers of PEMFCs.

We prepared two types of perfluorosulfonic acid (PFSA) ionomers with Aquivion (short side chain) and Nafion (long side chain) on a Pt surface and varied their water contents (2.92 ≤ λ ≤ 13.83) to calculate the solubility and permeability of O2 in hydrated PFSA ionomers on a Pt surface using full atomistic molecular dynamics (MD) simulations. The solubility and permeability of O2 molecules in hydrated Nafion ionomers were greater than those of O2 molecules in hydrated Aquivion ionomers at the same water content, indicating that the permeation of O2 molecules in the ionomers is affected not only by the diffusion coefficient of O2 but also by the solubility of O2. Notably, O2 molecules are more densely distributed in regions where water and hydronium ions have a lower density in hydrated Pt/PFSA ionomers. Radial distribution function (RDF) analysis was performed to investigate where O2 molecules preferentially dissolve in PFSA ionomers on a Pt surface. The results showed that O2 molecules preferentially dissolved between hydrophilic and hydrophobic regions in a hydrated ionomer. The RDF analysis was performed to provide details of the O2 location in hydrated PFSA ionomers on a Pt surface to evaluate the influence of O2 solubility in ionomers with side chains of different lengths. The coordination number of C(center)-O(O2) and O(side chain)-O(O2) pairs in hydrated Nafion ionomers was higher than that of the same pairs in hydrated Aquivion ionomers with the same water content. Our investigation provides detailed information about the properties of O2 molecules in different PFSA ionomers on a Pt surface and with various water contents, potentially enabling the design of better-performing PFSA ionomers for use in polymer electrolyte membrane fuel cells.

Prediction of Lap Shear Strength and Impact Peel Strength of Epoxy Adhesive by Machine Learning Approach.

In this study, an artificial neural network (ANN), which is a machine learning (ML) method, is used to predict the adhesion strength of structural epoxy adhesives. The data sets were obtained by testing the lap shear strength at room temperature and the impact peel strength at -40 °C for specimens of various epoxy adhesive formulations. The linear correlation analysis showed that the content of the catalyst, flexibilizer, and the curing agent in the epoxy formulation exhibited the highest correlation with the lap shear strength. Using the analyzed data sets, we constructed an ANN model and optimized it with the selection set and training set divided from the data sets. The obtained root mean square error (RMSE) and R2 values confirmed that each model was a suitable predictive model. The change of the lap shear strength and impact peel strength was predicted according to the change in the content of components shown to have a high linear correlation with the lap shear strength and the impact peel strength. Consequently, the contents of the formulation components that resulted in the optimum adhesive strength of epoxy were obtained by our prediction model.