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3D printing enables the fabrication of complex architectures by automating long sequences of additive steps. The increasing sophistication of printers, materials, and generative design promises to make geometric complexity a non-issue in manufacturing; however, this complexity can only be realized if a design can be translated into a physically executable sequence of printing operations. We investigate this planning problem for freeform direct-write assembly, in which filaments of material are deposited through a nozzle translating along a 3D path to create sparse, frame-like structures. We enumerate the process constraints for different variants of the freeform assembly process and show that, in the case where material stiffens via a glass transition, determining whether a feasible sequence exists is NP-complete. Nonetheless, for topologies typically encountered in real-world applications, finding a feasible or even optimal sequence is a tractable problem. We develop a sequencing algorithm that maximizes the fidelity of the printed part and minimizes the probability of print failure by modeling the assembly as a linear, elastic frame. We implement the algorithm and validate our approach experimentally, printing objects composed of thousands of sugar alcohol filaments with diameters of 100-200 microns. The assembly planner allows the freeform process to be applied to arbitrarily complex parts, from tissue engineering and microfluidics at the micrometer scale, to vascularized functional materials and soft robots at the millimeter scale, to structural components at the meter scale, thus opening a variety of assembly possibilities., Submitted to IEEE Transactions on Automation Science and Engineering

Water-soluble glass patterned by 3D printing is a versatile tool for tissue engineering and microfluidics. Glasses can be patterned layer-by-layer as in conventional fused deposition modeling but also along 3D, “freeform” paths. In the latter approach, extruding heated material through a nozzle translating in 3D space allows for fabrication of sparse, freestanding networks of cylindrical filaments. These freeform structures are suitable for sacrificial molding with a variety of media, leaving complex microchannel networks. However, 3D printing carbohydrate glass in this way presents several unique challenges: 1) the material must resist degradation and crystallization during printing, 2) the glass must be hot enough to flow freely during extrusion and fuse to the printed construct, while cooling rapidly to retain its shape upon exiting the nozzle, 3) the extruder needs to apply high pressure, with rapid stop and start times and 4) the net force that acts on the filament during extrusion must be minimized so that the filament shape is predictable, i.e., coincides with the path taken by the nozzle. First, we review the properties of commercially available carbohydrate glasses and provide a guide for processing isomalt, our material of choice, to achieve the best printing performance. A pressure-controlled, piston-driven extruder is then described which allows for rapid responses and precise control over the material flow rate. We then analyze the heat transfer within the filament and the forces that contribute to the filament’s final shape. We find that the dominant force is due to the radial flow of the molten glass as it exits the nozzle. This analysis is validated on a purpose-built isomalt 3D printer, which we utilize to characterize relationships between extrusion pressure, translation speed, filament diameter, and viscous force. The insights of the physics of the printing process enable fabrication of intricate freeform prints as well as layer-by-layer designs. The practical and theoretical considerations should facilitate adoption of additive manufacturing of carbohydrate glasses with applications to a wide variety of fields, including tissue engineering and microfluidics.

Confocal and multiphoton optical imaging techniques have been powerful tools for evaluating the performance of and monitoring experiments within microfluidic devices, but this application suffers from two pitfalls. The first is that obtaining the necessary imaging contrast often requires the introduction of an optical label which can potentially change the behavior of the system. The emerging analytical technique stimulated Raman scattering (SRS) microscopy promises a solution, as it can rapidly measure 3D concentration maps based on vibrational spectra, label-free; however, when using any optical imaging technique, including SRS, there is an additional problem of optical aberration due to refractive index mismatch between the fluid and the device walls. New approaches such as 3D printing are extending the range of materials from which microfluidic devices can be fabricated; thus, the problem of aberration can be obviated simply by selecting a chip material that matches the refractive index of the desired fluid. To demonstrate complete chemical imaging of a geometrically complex device, we first use sacrificial molding of a freeform 3D printed template to create a round-channel, 3D helical micromixer in a low-refractive-index polymer. We then use SRS to image the mixing of aqueous glucose and salt solutions throughout the entire helix volume. This fabrication approach enables truly nonperturbative 3D chemical imaging with low aberration, and the concentration profiles measured within the device agree closely with numerical simulations.

Here we demonstrate a method for creating multilayer or 3D microfluidics by casting a curable resin around a water-soluble, freestanding sacrificial mold. We use a purpose-built 3D printer to pattern self-supporting filaments of the sugar alcohol isomalt, which we then back-fill with a transparent epoxy resin. Dissolving the sacrificial mold leaves a network of cylindrical channels as well as input and output ports. We use this technique to fabricate a combinatorial mixer capable of producing 8 combinations of two fluids in ratios ranging from 1 : 100 to 100 : 1. This approach allows rapid iteration on microfluidic chip design and enables the use of geometry and materials not accessible using conventional soft lithography. The ability to precisely pattern round channels in all three dimensions in hard and soft media may prove enabling for many organ-on-chip systems.

Soybeans are a commodity crop of significant economic and nutritional interest. As an important source of protein, buyers of soybeans are interested in not only the total protein content but also in the specific amino acids that comprise the total protein content. Raman spectroscopy has the chemical specificity to measure the twenty common amino acids as pure substances. An unsolved challenge, however, is to quantify varying levels of amino acids mixed together and bound in soybeans at relatively low concentrations. Here we report the use of transmission Raman spectroscopy as a secondary analytical approach to nondestructively measure specific amino acids in intact soybeans. With the employment of a transmission-based Raman instrument, built specifically for nondestructive measurements from bulk soybeans, spectra were collected from twenty-four samples to develop a calibration model using a partial least-squares approach with a random-subset cross validation. The calibration model was validated on an independent set of twenty-five samples for oil, protein, and amino acid predictions. After Raman measurements, the samples were reduced to a fine powder and conventional wet chemistry methods were used for quantifying reference values of protein, oil, and 18 amino acids. We found that the greater the concentrations (% by weight component of interest), the better the calibration model and prediction capabilities. Of the 18 amino acids analyzed, 13 had R(2) values greater than 0.75 with a standard error of prediction c.a. 3-4% by weight. Serine, histidine, cystine, tryptophan, and methionine showed poor predictions (R(2)0.75), which were likely a result of the small sampling range and the low concentration of these components. It is clear from the correlation plots and root-mean-square error of prediction that Raman spectroscopy has sufficient chemical contrast to nondestructively quantify protein, oil, and specific amino acids in intact soybeans.

The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods.

Fourier-transform infrared imaging (FT-IR) is a well-established modality but requires the acquisition of a spectrum over a large bandwidth, even in cases where only a few spectral features may be of interest. Discrete frequency infrared (DF-IR) methods are now emerging in which a small number of measurements may provide all the analytical information needed. The DF-IR approach is enabled by the development of new sources integrating frequency selection, in particular of tunable, narrow-bandwidth sources with enough power at each wavelength to successfully make absorption measurements. Here, we describe a DF-IR imaging microscope that uses an external cavity quantum cascade laser (QCL) as a source. We present two configurations, one with an uncooled bolometer as a detector and another with a liquid nitrogen cooled Mercury Cadmium Telluride (MCT) detector and compare their performance to a commercial FT-IR imaging instrument. We examine the consequences of the coherent properties of the beam with respect to imaging and compare these observations to simulations. Additionally, we demonstrate that the use of a tunable laser source represents a distinct advantage over broadband sources when using a small aperture (narrower than the wavelength of light) to perform high-quality point mapping. The two advances highlight the potential application areas for these emerging sources in IR microscopy and imaging.