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Electrophoretic deposition (EPD) of platinum nanoparticles (PtNPs) on platinum-iridium (Pt-Ir) neural electrode surfaces is a promising strategy to tune the impedance of electrodes implanted for deep brain stimulation in various neurological disorders such as advanced Parkinson's disease and dystonia. However, previous results are contradicting as impedance reduction was observed on flat samples while in three-dimensional (3D) structures, an increase in impedance was observed. Hence, defined correlations between coating properties and impedance are to date not fully understood. In this work, the influence of direct current (DC) and pulsed-DC electric fields on NP deposition is systematically compared and clear correlations between surface coating homogeneity and in vitro impedance are established. The ligand-free NPs were synthesized via pulsed laser processing in liquid, yielding monomodal particle size distributions, verified by analytical disk centrifugation (ADC). Deposits formed were quantified by UV-vis supernatant analysis and further characterized by scanning electron microscopy (SEM) with semiautomated interparticle distance analyses. Our findings reveal that pulsed-DC electric fields yield more ordered surface coatings with a lower abundance of particle assemblates, while DC fields produce coatings with more pronounced aggregation. Impedance measurements further highlight that impedance of the corresponding electrodes is significantly reduced in the case of more ordered coatings realized by pulsed-DC depositions. We attribute this phenomenon to the higher active surface area of the adsorbed NPs in homogeneous coatings and the reduced particle-electrode electrical contact in NP assemblates. These results provide insight for the efficient EPD of bare metal NPs on micron-sized surfaces for biomedical applications in neuroscience and correlate coating homogeneity with in vitro functionality.

We conduct numerical simulations of acoustic focusing in dense suspensions to map the design space of acoustically patterned materials and understand the relationships between input parameters, structural features, and functional properties. We develop closed-form expressions for acoustic forces on particles, enabling rapid simulation of thousands of particles, and find excellent agreement with experimentally focused patterns over a range of conditions. We map the geometrical and microstructural features of focused particle patterns and their dependence on processing parameters. We find that mesostructural geometrical features (focused line height, width, and profile shape) can be controlled reliably over a broad range by modulating input parameters, and that while microstructural features are less readily modulated via input parameters, they are well-suited for various transport properties in functional materials. Notably, packing density nears the random close packing limit at 0.64, and particle contact density shows anisotropy favoring particle contacts along the focused lines. These results guide process design for controlling the properties of patterned materials, and outline the property ranges accessible via acoustic focusing. Additionally, we discuss the dependence of material functionalities, particularly electrical, thermal, and ionic transport properties, on the meso- and micro-structural features of patterned composite materials in the context of acoustic focusing.

This document describes the collection and organization of a dataset that consists of raw videos and extracted sub-images from video frames of a promising additive manufacturing technique called Two-Photon Lithography (TPL). Four unprocessed videos were collected, with each video capturing the printing process of different combinations of 3D parts on different photoresists at varying light dosages. These videos were further trimmed to obtain short clips that are organized by experimental parameters. Additionally, this dataset also contains a python script to reproduce an organized directory of cropped video frames extracted from the trimmed videos. These cropped frames focus on a region of interest around the parts being printed. We envision that the raw videos and cropped frames provided in this dataset will be used to train various computer vision and machine learning algorithms for applications such as object segmentation and localization applications. The cropped video frames were manually labelled by an expert to determine the quality of the printed part and for printing parameter optimization as presented in “Automated Detection of Part Quality during Two-Photon Lithography via Deep Learning” [1] .

Electronic displays are nearly ubiquitous in modern society. With the drive to lower power consumption, reflective displays based on reversible electrophoretic deposition of nanoparticles have emerged as a candidate next generation display technology. Understanding the origins of color in these displays is challenging because the color is determined by the transient arrangement of particles in the electric field. In this article, a new technique, based on small angle x-ray scattering, is developed to simultaneously interrogate nanoparticle arrangement and measure color while the display is in operation. Furthermore, a particle-based numerical model of electrophoretic deposition is demonstrated to quantitatively predict transient interparticle distances during operation.

Microfluidic-based microencapsulation requires significant oversight to prevent material and quality loss due to sporadic disruptions in fluid flow that routinely arise. State-of-the-art microcapsule production is laborious and relies on experts to monitor the process, e.g. through a microscope. Unnoticed defects diminish the quality of collected material and/or may cause irreversible clogging. To address these issues, we developed an automated monitoring and sorting system that operates on consumer-grade hardware in real-time. Using human-labeled microscope images acquired during typical operation, we train a convolutional neural network that assesses microencapsulation. Based on output from the machine learning algorithm, an integrated valving system collects desirable microcapsules or diverts waste material accordingly. Although the system notifies operators to make necessary adjustments to restore microencapsulation, we can extend the system to automate corrections. Since microfluidic-based production platforms customarily collect image and sensor data, machine learning can help to scale up and improve microfluidic techniques beyond microencapsulation.

Platinum electrodes are critical components in many biomedical devices, an important example being implantable neural stimulation or recording electrodes. However, upon implantation, scar tissue forms around the electrode surface, causing unwanted deterioration of the electrical contact. We demonstrate that sub-monolayer coatings of platinum nanoparticles (PtNPs) applied to 3D neural electrodes by electrophoretic deposition (EPD) can enhance the electrode's active surface area and significantly lower its impedance. In this work we use ethanol-water mixtures as the EPD solvent, in contrast to our previous studies carried out in water. We show that EPD coating in 30 vol.% ethanol improves the device's electrochemical performance. Computational mesoscale multiparticle simulations were for the first time applied to PtNP-on-Pt EPD, revealing correlations between ethanol concentration, electrochemical properties, and coating homogeneity. Thereto, this optimum ethanol concentration (30 vol.%) balances two opposing trends: (i) the addition of ethanol reduces water splitting and gas bubble formation, which benefits surface coverage, and (ii) increased viscosity and reduced permittivity occur at high ethanol concentrations, which impair the coating quality and favoring clustering. A seven-fold increase in active surface area and significantly reduced in vitro impedance of the nano-modified neural stimulation electrode surfaces highlight the influence of ethanol-water mixtures in PtNP EPD.

For our direct ink write (DIW) setup, hours of video are collected of fibrous ink that is printed from a translucent nozzle while parts are made. Due to sporadic misalignment of the fibers, clogs may arise that disrupt ink flow, resulting in a failed part. Without on-line monitoring, defective parts can only be identified by operators who witness clogs as or after they occur, requiring operators to continuously monitor the process to eliminate defects. In order to alleviate this, we aim to minimize the effect of clogging via automated process monitoring and rapid detection, thereby reducing labor costs, material loss, and proper identification of defective parts. In this paper, we propose applying a convolutional neural network (CNN) for single frame classification on images gathered from our DIW setup. We report a class average recall of 99.85% across clogged and unclogged classes, and average error of 1.64% when evaluated on new test video sequences, with a processing rate of 27.58 fps. Using class activation mapping, we can visualize image regions the CNN model identifies as salient for each class in performing its discriminative classification task.

Current microfluidic-based microencapsulation systems rely on human experts to monitor and oversee the entire process spanning hours in order to detect and rectify when defects are found. This results in high labor costs, degradation and loss of quality in the desired collected material, and damage to the physical device. We propose an automated monitoring and classification system based on deep learning techniques to train a model for image classification into four discrete states. Then we develop an actuation control system to regulate the flow of material based on the predicted states. Experimental results of the image classification model show class average recognition rate of 95.5%. In addition, simulated test runs of our valve control system verify its robustness and accuracy.

Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.

Laser Powder Bed Fusion (LPBF) is the predominant metal additive manufacturing technique that benefits from a significant body of academic study and industrial investment given its ability to create complex geometry parts. Despite LPBF’s widespread use, there still exists a need for process monitoring to ensure reliable part production and reduce post-build quality assessments. Towards this end, we develop and evaluate machine learning-based predictive models using height map-derived quality metrics for single tracks and the accompanying pyrometer and high-speed video camera data collected under a wide range of laser power and laser velocity settings. We extract physically intuitive low-level features representative of the meltpool dynamics from these sensing modalities and explore how these vary with the linear energy density. We find our Sequential Decision Analysis Neural Network (SeDANN) model – a scientific machine learning model that incorporates physical process insights – outperforms other purely data-driven black box models in both accuracy and speed. The general approach to data curation and adaptable nature of SeDANN’s scientifically informed architecture should benefit LPBF systems with an evolving suite of sensing modalities and post-build quality measurements.

Selective Laser Melting (SLM) is a metal additive manufacturing technique. The lack of SLM process repeatability is a barrier for industrial progression. SLM product quality is hard to control, even when using fixed system settings. Thus SLM could benefit from a monitoring system that provides quality assessments in real-time. Since there is no publicly available SLM dataset, we ran experiments to collect over one thousand videos, measured the physical output via height map images, and applied a proposed image processing algorithm to them to produce a dataset for semi-supervised learning. Then we trained convolutional neural networks (CNNs) to recognize desired quality metrics from videos. Experimental results demonstrate our the effectiveness of our proposed monitoring approach and also show that the semi-supervised model can mitigate the time and expense of labeling an entire SLM dataset.

In this paper, we quantify market access in rural Tanzania, and the extent to which it constrains agricultural productivity. We collect granular data on farmer input and sales decisions, input and output prices, and travel costs in all 1,183 villages in two regions of Tanzania. We find that a village in the 90th percentile of the travel-cost adjusted price distribution faces input and output prices 40-55% less favorable than a village at the 10th percentile. In reduced form, an additional standard deviation of travel time is associated with 20-25% lower input adoption and output sales. We develop and quantify a spatial model of input adoption and conservatively estimate that farmers behave as if they face travel costs of 6% ad-valorem per kilometer of travel, which is equivalent to 40% when traveling to the closest retailer. Holding exogenous local factors fixed, we estimate that reducing travel costs by 50% (approximately the effect of paving rural roads) doubles adoption and reduces the adoption-remoteness gradient by 18%.

We evaluate the accuracy of local-density approximations (LDAs) using explicit molecular dynamics simulations of binary electrolytes comprised of equisized ions in an implicit solvent. The Bikerman LDA, which considers ions to occupy a lattice, poorly captures excluded volume interactions between primitive model ions. Instead, LDAs based on the Carnahan-Starling (CS) hard-sphere equation of state capture simulated values of ideal and excess chemical potential profiles extremely well, as well as the relationship between surface charge density and electrostatic potential. Excellent agreement between the EDL capacitances predicted by CS-LDAs and computed in molecular simulations is found even in systems where ion correlations drive strong density and free charge oscillations within the EDL, despite the inability of LDAs to capture the oscillations in the detailed EDL profiles.

We report a new framework for a quantitative understanding of optical trapping (OT) particle handling dynamics. We present a novel three-dimensional particle-based model that includes optical, hydrodynamic, and inter-particle forces. This semi-empirical colloid model is based on an open-source simulation code known as LAMMPS (large-scale atomic/molecular massively parallel simulator) and properly recapitulates the full OT force profile beyond the typical linear approximations valid near the trap center. Simulations are carried out with typical system parameters relevant for our experimental holographic optical trapping (HOT) system, including varied particle sizes, trap movement speeds, and beam powers. Furthermore, we present a new experimental method for measuring both the stable and metastable boundaries of the optical force profile to inform or validate the model's underlying force profile. We show that our framework is a powerful tool for accurately predicting particle behavior in a practical experimental OT setup and can be used to characterize and predict particle handling dynamics within any arbitrary OT force profile.

We derive a self-similarity criterion that must hold if a planar electric double layer (EDL) can be captured by a local-density approximation (LDA), without specifying any specific LDA. Our procedure generates a similarity coordinate from EDL profiles (measured or computed), and all LDA EDL profiles for a given electrolyte must collapse onto a master curve when plotted against this similarity coordinate. Noncollapsing profiles imply the inability of any LDA theory to capture EDLs in that electrolyte. We demonstrate our approach with molecular simulations, which reveal dilute electrolytes to collapse onto a single curve, and semidilute ions to collapse onto curves specific to each electrolyte, except where size-induced correlations arise. © 2013 American Physical Society.

We evaluate a particle-based model of colloidal suspensions that undergo electrophoretic motion[1] and deposition using an extensive set of mesoscale simulations that characterize experimentally relevant colloidal suspensions. We parameterize the model using only measurable quantities from suspensions that have properties of non-polar and aqueous solvents or ionic liquids with monodisperse polystyrene colloids between 1 nm - 1 μm in radius. The model accurately describes diffusive and electrophoretic motion of experimentally relevant colloidal suspensions. Furthermore, we extend the model to investigate electrophoretic deposition. The model is uniquely poised to elucidate how the deposit morphology depends on electric field strength, suspension properties, and colloid packing fraction because the model explicitly computes inter-particle interactions of colloidal suspensions. [1] Giera, B.; Zepeda-Ruiz, L. A.; Pascall, A. J.; Kuntz, J. D.; Spadaccini, C. M.; Weisgraber, T. H. Mesoscale Particle-Based Model of Electrophoresis. Journal of the Electrochemical Society. The Electrochemical Society 2015, pp D3030–D3035.

We present the reversible assembly of colloidal nanoparticles in organic solvents using low voltage and low frequency pulsed DC electric fields. Commercially available and custom nanoparticles (diameter ~ 50-200 nm) were suspended in organic solvents at concentrations of 0.25-50 wt% and sandwiched between indium tin oxide (ITO) electrodes of spacing 25 μm-2mm. Using square waves of frequency 0.25-5 Hz, voltages 0.5-5V, and DC offset 0.25-2.5V, colloidal suspensions were actuated for switchable contrast. Upon switching off the electric field, the assembled nanoparticles are able to resuspend within seconds. Using patterned electrodes, lateral electromigration of the particles was demonstrated. Depletion of the particles to the electrode regions resulted in transparency in the non-patterned regions. The distinct contrast generated can be exploited for tunable display applications. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-679931.