ABSTRACT: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.

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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.

variability in the powder properties, [9] bed thickness nonuniformity, [10] and laser parameters and scan paths that result in improper power melting. [11] Thus, even after optimizing LPBF operating para meters and identifying suitable processing windows, [12] rapid build qualification, improved quality, and higher production yields require methods of monitoring the melt pool and/or powder bed in situ, i.e., during a build, that enable realtime process feedback and automated quality detection. [13,14] The majority of LPBF process moni toring approaches rely on noncontact sensing [15] from optical, thermal, [16] and/ or acoustic [17,18] sensors. These sen sors provide assessments of spatial and spectral features of the melt pool, [19,20] process plume, [21] degree of spatter, [22][23][24][25] overhang layers, [26] or print bed. High speed image sequences of the melting process, [27] scans of the powder bed, [10,28] beam quality, [29] and/or thermal monitoring [30] are all routinely collected forms of in situ monitoring data. Making use of this data requires methods that can extract rel evant diagnostic information. For instance, before initiating laser melting, automated computer vision algorithms can characterize metal powder feedstocks, [31] and image analysis of newly spread powder can reveal nonuniformities in the powder bed thickness. [32] Aminzadeh et al. demonstrated layerbylayer detection of fusion defects from images using a Bayesian clas sifier. [33] Realtime events such as material ejecta are detectable by applying manually set thresholds to highspeed nearinfrared images of the melt pool. Also, increasing the µm pixel −1 image resolution relative to the standard deviation of measured track width, σ measured , may result in improved predictions of the final track width, δ predicted , and topography. [34] Reducing laser power proportionally to an integrated signal from a photodiode cali brated against a camera results in smoother overhang struc tures. [35] Using images of the print bed taken after laser melting, a level sets method can detect intentionally created defects, [36] machine vision algorithms can identify pore defects, [37] and multifractal image analysis can characterize layers with balling, cracks and pores, and no defects. [38] Visual imaging equipment is appealing to LPBF monitoring systems because it is relatively inexpensive and provides noncontact sensing. [13] As with most additive manufacturing systems, analysis of LPBF sensor data currently occurs postbuild, rendering A two-step machine learning approach to monitoring laser powder bed fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured postbuild through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised...