Heterogeneously wettable surfaces are well-sought in energy, water, health care, separation science, self-cleaning, biology, and other lab-on-chip applications—however, most demonstrations of realizing heterogeneous wettability demand complex processes. Herein, we chemically...

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Numerous complex methods have been reported to generate heterogeneously wettable surfaces in the literature. These surfaces are well-sought in energy, water, health care, separation science, self-cleaning, biology, and other lab-on-chip applications. While most of the demonstrations of heterogeneous wettability rely on a series of complex fabrication protocols, we reveal an unconventional approach to achieving heterogeneous wettability through three simple steps (i.e., patterning, silanizing, and rinsing). Here, we show heterogeneous wettability on a planar substrate harnessing (a) the wetting and dewetting behavior of nano-textured conductive surface patterns of Gallium alloys and (b) interfacial chemical reactivity of the native surface oxides of these alloys in the presence of chloro-silane vapor. Alloys of Gallium (eGaIn and others) have emerged as one of the most promising soft metals for the fabrication of soft functional devices harnessing their surface oxides, mostly Gallium Oxide (Ga2O3). These alloys can be 2D patterned utilizing the wetting behavior of the Ga2O3, which seems impossible due to the high surface tension of the bare metal. We utilized such 2D metal patterns on the planar glass surface and exposed the patterns in chloro-silane vapors to begin our study. Chloro-silanes can alter the surface energy of different substrates (i.e., glass, silicon wafer) to offer hydrophobicity and releases Chlorine vapors which etch Ga2O3 that induces the delamination. A simple DI water rinsing operation reveals a thin hydrophilic layer on the pre-patterned area that we utilized for an open-ended microfluidic demonstration, as well. We confirmed hydrophilicity through contact angle measurements; elemental compositions, and Chlorine's presence through energy dispersive spectroscopic (EDS) analyses. We believe such an unconventional approach of achieving heterogeneous wettability has the potential for fundamental studies related to bioinspired and biomimetic applications.

Precision drug delivery for optimized therapeutic targeting requires knowledge of momentum transport and molecular diffusion of molecules within the patient's interstitial tissue, especially for tumor treatment within the brain. Dispersion in the interstitial space is impacted by delivery method, tissue material properties, individual-specific fluid flow, and particle size of the input solute. Knowledge of a drug's dispersion allows for optimizing solute delivery, concentration, and flow rates to maximize drug distribution and biomarker recovery. For delivering drugs, increased knowledge of drug location after delivery can improve therapeutic treatment by optimizing the dosing of healthy and unhealthy tissue. Finite element methods (FEM) tools, such as COMSOL Multiphysics, can simulate molecular distribution inside -individual specific shapes and porous material properties. Furthermore, an additional unmet need is delivery methods that can be adjusted to manipulate diffusion regions through tissue via techniques such as directed flow. This would be especially valuable in targeted drug delivery within tumors to increase the cancerous surface area covered while limiting damage to surrounding tissues. In this project, the directed flow was induced by perfusing the injected solution at an input probe while withdrawing fluid at an output probe, enabling targeted flow through the desired region. FEM computation faithfully replicated these conditions and could be used to determine the effective concentrations perfused over the region of interest. We leveraged COMSOL Multiphysics to perform a computational study simulating convection-enhanced delivery (CED) with an output probe pulling the concentration profile over the region of interest. This simulation system can be applied to therapeutics targeting, vaccine subcutaneous injection, and waste and media diffusion in tissue engineering.

Melt‐extruded hollow thermoplastic fibers are a new class of materials for advanced applications, i.e., robotic clothes, stretchable touch and twist sensors, and programmable knitted textiles. Under large mechanical deformation (strain), such materials exhibit hyperelasticity. However, fundamental knowledge of strain‐energy density harnessing integrated computational materials engineering (ICME) has remained untapped for hollow fibers. Due to this key knowledge gap, most emerging applications demand iterative and costly approaches to producing and studying hollow fibers. Herein, a data‐driven pathway harnessing the ICME is introduced to study, predict, and optimize the hyperelastic behavior of hollow fibers. MCalibration software is used to calibrate the material properties of the fibers utilizing the Three‐Network Model (TNM) and emulated experimental stress–strain behavior in a finite‐element‐based multiphysics environment (COMSOL) with 99.99% confidence. The feasibility of predicting strain‐energy density is shown to modulate shape and geometries for new understandings without running cost‐incurring melt extrusions or experiments. Furthermore, the strain‐energy density is leveraged as a tool to attach fibers on fabrics that otherwise need iterations. As a proof‐of‐concept, fibers to fabrics are attached for wearable microfluidic demonstrations. This ICME approach can be extended to design the next generation of pre‐programmed, undiscovered functional devices fabricated from hyperelastic hollow fibers.