A 3D-MICROPRINTED COAXIAL NOZZLE FOR FABRICATING LONG, FLEXIBLE MICROFLUIDIC TUBING.

A variety of emerging applications, particularly those in medical and soft robotics fields, are predicated on the ability to fabricate long, flexible meso/microfluidic tubing with high customization. To address this need, here we present a hybrid additive manufacturing (or "three-dimensional (3D) printing") strategy that involves three key steps: (i) using the "Vat Photopolymerization (VPP) technique, "Liquid-Crystal Display (LCD)" 3D printing to print a bulk microfluidic device with three inlets and three concentric outlets; (ii) using "Two-Photon Direct Laser Writing (DLW)" to 3D microprint a coaxial nozzle directly atop the concentric outlets of the bulk microdevice, and then (iii) extruding paraffin oil and a liquid-phase photocurable resin through the coaxial nozzle and into a polydimethylsiloxane (PDMS) channel for UV exposure, ultimately producing the desired tubing. In addition to fabricating the resulting tubing-composed of polymerized photomaterial-at arbitrary lengths (e.g., > 10 cm), the distinct input pressures can be adjusted to tune the inner diameter (ID) and outer diameter (OD) of the fabricated tubing. For example, experimental results revealed that increasing the driving pressure of the liquid-phase photomaterial from 50 kPa to 100 kPa led to fluidic tubing with IDs and ODs of 291±99 µm and 546±76 µm up to 741±31 µm and 888±39 µm, respectively. Furthermore, preliminary results for DLW-printing a microfluidic "M" structure directly atop the tubing suggest that the tubing could be used for "ex situ DLW (esDLW)" fabrication, which would further enhance the utility of the tubing.

FABRICATION OF MULTILUMEN MICROFLUIDIC TUBING FOR EX SITU DIRECT LASER WRITING.

Among the numerous additive manufacturing or "three-dimensional (3D) printing" techniques, two-photon Direct Laser Writing (DLW) is distinctively suited for applications that demand high geometric versatility with micron-to-submicron-scale feature resolutions. Recently, "ex situ DLW (esDLW)" has emerged as a powerful approach for printing 3D microfluidic structures directly atop meso/macroscale fluidic tubing that can be manipulated by hand; however, difficulties in creating custom esDLW-compatible multilumen tubing at such scales has hindered progress. To address this impediment, here we introduce a novel methodology for fabricating submillimeter multilumen tubing for esDLW 3D printing. Preliminary fabrication results demonstrate the utility of the presented strategy for resolving 743 µm-in-diameter tubing with three lumens-each with an inner diameter (ID) of 80 µm. Experimental results not only revealed independent flow of discrete fluorescently labelled fluids through each of the three lumens, but also effective esDLW-printing of a demonstrative 3D "MEMS" microstructure atop the tubing. These results suggest that the presented approach could offer a promising pathway to enable geometrically sophisticated microfluidic systems to be 3D printed with input and/or output ports fully sealed to multiple, distinct lumens of fluidic tubing for emerging applications in fields ranging from drug delivery and medical diagnostics to soft surgical robotics.

Surgical Planning and Optimization of Patient-Specific Fontan Grafts With Uncertain Post-Operative Boundary Conditions and Anastomosis Displacement.

OBJECTIVE: Fontan surgical planning involves designing grafts to perform optimized hemodynamic performance for the patient's long-term health benefit. The uncertainty of post-operative boundary conditions (BC) and graft anastomosis displacements can significantly affect optimized graft designs and lead to undesirable outcomes, especially for hepatic flow distribution (HFD). We aim to develop a computation framework to automatically optimize patient-specific Fontan grafts with the maximized possibility of keeping post-operative results within clinical acceptable thresholds. METHODS: The uncertainties of BC and anastomosis displacements were modeled using Gaussian distributions according to prior research studies. By parameterizing the Fontan grafts, we built surrogate models of hemodynamic parameters taking the design parameters and BC as input. A two-phase reliability-based robust optimization (RBRO) strategy was developed by combining deterministic optimization (DO) and optimization under uncertainty (OUU) to reduce computational cost. RESULTS: We evaluated the performance of the RBRO framework by comparing it with the DO method in four cases of Fontan patients. The results showed that the surgical plans computed from the proposed method yield up to 79.2% improvement in the reliability of the HFD than those of the DO method ( ). The mean values of indexed power loss (iPL) and the percentage of non-physiologic wall shear stress (%WSS) for the optimized surgical plans met the clinically acceptable thresholds. CONCLUSION: This study demonstrated the effectiveness of our RBRO framework to address the uncertainties of BC and anastomosis displacements for Fontan surgical planning. SIGNIFICANCE: The technique developed in this paper demonstrates a significant improvement in the reliability of the predicted post-operative outcomes for Fontan surgical planning. This planning technique is immediately applicable as a building block to enable technology for optimal long-term outcomes for pediatric Fontan patients and can also be used in other pediatric and adult cardiac surgeries.

Locally Optimizable Joint Embedding Framework to Design Nitrogen-rich Molecules that are Similar but Improved.

Deep learning has shown great potential for generating molecules with desired properties. But the cost and time required to obtain relevant property data have limited study to only a few classes of materials for which extensive data have already been collected. We develop a deep learning method that combines a generative model with a property prediction model to fuse small data of one class of molecules with larger data in another class. Common low-level physicochemical properties are jointly embedded into a latent space that can be used to design molecules in the smaller class. The chemical space around the molecules in the training set is explored through local gradient ascent optimization. Based on nine molecules from the original training set, nine new molecules are found to have improved properties while remaining structurally similar to the training molecules thereby easing requirements for entirely new synthesis routes. Validation is performed using an equilibrium thermochemistry code to verify the molecules and target properties. A specific example targeting the Chapman-Jouguet velocity and small data for nitrogen-rich molecules is shown. Despite the relative lack of nitrogen-rich molecule data, the results demonstrate that fusing and joint embedding with plentiful low nitrogen molecular data can produce higher generative performance than using the scarce data alone.

Applying machine learning techniques to predict the properties of energetic materials.

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.