Function Of Nanoparticle Size Research Articles

Atomistic study of CoCrCuFeNi high entropy alloy nanoparticles: Role of chemical complexity

High entropy alloy nanoparticles are envisaged as one of the most interesting materials compared to monoatomic materials due to their modulated properties in terms of their convenient surface-to-volume ratio. However, studies are still missing to unveil how composition or nanoparticle size can influence nanoparticle morphology. Based on molecular dynamics simulations, we perform a structural characterization as a function of nanoparticle size and the chemical composition of high entropy alloy nanoparticles subject to multiple annealing cycles. After the multiple thermal loads, we observe a substantial migration of copper atoms towards the np surface, consistent with the experimental results of Cu-based high entropy alloys. The resulting high entropy alloy nanoparticle behaves as a core–shell nanostructure with a rich fcc phase on the surface (50% of Cu) and 5% fcc phase in the nanoparticle core. Inspecting the nanoparticle surface, it is observed that high entropy alloy nanoparticles have a lack of surface facets, leading to a more spherical shape, quite different from mono-metallic nanoparticles with a high number of facets. Performing an average atoms simulation, it showed that nanoparticles are prone to form 111 surface facets independent of the nanoparticle size, suggesting that for high entropy alloy nanoparticles, the chemical complexity avoids the formation of surface facets. The latter can be explained in terms of the lattice distortion inducing tensile/compressive stress that drives the surface reconstruction. All in all our results match extremely well with experimental evidence of FeNiCrCoCu nanocrystalline materials, explaining the Cu segregation in terms of surface energy and mixing enthalpy criteria. We believe that our results provide a detailed characterization of high entropy nanoparticles focusing on how chemical complexity induces morphological changes compared to mono-crystalline nanoparticles. Besides, our findings are valuable for experimental works aimed at designing the shape and composition of multicomponent nanoparticles.

Confined assembly of size-tunable silver nanoprisms on flexible Teflon tapes as highly active SERS hotspots for ultralow-level sensing of DNA and glucose

The design of smart and advanced sensors facilitates highly sensitive optical detection platforms for biomolecules. Herein, we attempted the confined assembly of silver nanoprisms (SNPs) on hydrophobic Teflon tapes to design a highly flexible and ultrasensitive biosensing platform for DNA and glucose. We investigated the sensor characteristics including morphological, optical, and SERS activities as a function of nanoparticle size. Pyramidal SNPs were synthesized, whose size was tuned by varying the bromide ion concentrations. The size of SNPs was decreased with an increase in bromide ion concentrations, where quasi-spherical nanoparticles were formed at higher bromide content. The hydrophobic Teflon surface caused considerable constrain during the evaporation of nanoparticle suspension, leading to highly confined SNPs. SERS activities of Teflon/SNP sensors were tuned by varying the size of SNPs, where a size of ∼34 nm exhibited the highest signal enhancement. The optimum Teflon/SNP sensor showed an enhancement factor of 1.3 × 109 with high stability and excellent reproducibility. Teflon/SNP sensor was further exploited to detect DNA and glucose in a label-free method, exhibiting sensitivities of approximately 13.6 fg/µL and 5 µM, respectively. Real-time monitoring of DNA and glucose from saliva was also investigated. Teflon/SNP substrates were found to be excellent biosensing platforms for ultralow-level sensing of biomolecules in a rapid and cost-effective manner.

The New Attempt at Modeling of the Three-Dimensional Geometry of the Epidermal Skin Layer and the Diffusion Processes of Nanomolecular Drug Carriers in Such Structures.

Nanoparticles are presently considered the efficient carriers of medicals, cosmetics, and pharmaceuticals in the human organism. There is a lot of research carried out on the delivery of these materials in a non-invasive way. Such a method is very safe in times of global illnesses and pandemics. The most frequently investigated route is the approach to delivering nano-media through the skin as the result of diffusion processes. The stratum corneum, the outermost layer of skin, is the most resistive barrier to such a form of penetration. In this work, a new model is proposed to predict nanoparticles' transport through this layer. It introduces the concept of the three-dimensional model of the stratum corneum, which allows to define the skin surface area from which diffusion occurs. This structure was replaced by the single capillary, resulting from theoretical considerations. Modeling of the diffusion process of nanoparticles as the result of Brownian motion in such a capillary was performed numerically using COMSOL Multiphysics package programs. Further, using the dimensions of such a capillary, a new model of diffusion was developed in which the parameters allow to determine the effective diffusion coefficient as a function of nanoparticle size and the viscosity of a liquid. As a result, the proposed models provide a new and efficient approach to the determination of the nano-molecules' transport phenomena through the skin layer.

The PEGylated and non-PEGylated interaction of the anticancer drug 5-fluorouracil with paramagnetic Fe3O4 nanoparticles as drug carrier

• The chemotherapy drug, 5-fluorouracil, is adsorbed to Fe 3 O 4 nanoparticles that act as a nanocarrier during targeted drug delivery. • Binding energy as a function of nanoparticle size is investigated. • Chemisorption, physisorption and hydrogen bonding is reported on. • Drug release, delivery and potential modifications at elevated temperatures are reported on. • The drug’s HOMO-LUMO and electrostatic potential are reported on. A simulation study is done on the anticancer drug, 5-fluorouracil, on PEGylated and non-PEGylated Fe 3 O 4 nanoparticles (NPs) to be employed as drug delivery vehicles. This is done for NP sizes varying from 1 to 6.9 nm and within a molecular dynamics (MD) study with a Monte Carlo simulated annealing scheme. Bonding modes are investigated with reference to chemisorption, physisorption and hydrogen bonding in the context of bond strength (binding energy) as a function of the 5-fluorouracil molecule’s loading on different sized NPs. Density functional theory (DFT) simulations are also performed to investigate the frontier molecular orbitals and molecular electrostatic potentials. This is done in the context of the interaction of the anticancer drug with DNA to explore if the use of this drug carrier may have the potential to alter the drug’s chemistry before delivery to the target site and therefore reduce its efficacy. Interactions with water molecules are also studied.

Analysis of physical dose enhancement in nano-scale for nanoparticle-based radiation therapy: a Cluster and endothelial cell model

Objective(s): One major difficulty of conventional radiotherapy is the lack of selectivity between the tumor and the organs at risk. In nanoparticle aided radiotherapy, heavy elements are present at higher concentrations in the tumor than normal tissues. This study aimed to model the characteristics of secondary electrons generated from the interaction of clusters comprised of five different nanoparticles including Gold, Gadolinium, Iridium, Bismuth, and Hafnium atoms with low energy x-rays (similar to brachytherapy sources in terms of energy) as a function of nanoparticle size and beam energy. Materials and Methods: To better evaluate the contributions of secondary electrons in energy deposition, and also to develop a framework in analyzing further measurements in the future, we attempted to enhance and promote existing mathematical models for energy deposition in endothelial cells by nanoparticle-enhanced radiotherapy. Also, the MCNPX Monte Carlo code was used to model the identical geometry and the dose enhancement factor was calculated for all types of simulated nano-clusters.Results: Our results showed that for our model consist of a nano-cluster and an endothelial cell the DEF significantly depends on the energy of photons and L- and K-edge binding energy of the atoms inside the nano-cluster. However, for Gd at the energy 60 keV, a higher dose enhancement factor was seen.Conclusion: It can be concluded that the mathematical model considers the DEF variation with photon energy and the effect of NP type is considered in DEF calculations. However, the MC method has indicated very high sensitivity to photon energy, and NP type compared to the mathematical method.

An efficient homogenization scheme for analyzing the elastic properties of hybrid nanocomposites filled with multiscale particles

Designing material requires the establishment of structure–property relationships for multiscaled nanoparticle/microparticle-reinforced polymer hybrid nanocomposites. This fundamental task is the first step in developing a reliable new method. In the present study, two micromechanical analytical models are proposed to develop an efficient homogenization scheme, in the case of calculating elastic properties for a multiscaled hybrid nanocomposite consisting of silica nanoparticles and glass microparticles embedded in the epoxy matrix. In the small scale, we consider homogenous interphase surrounding the nanoparticle which the first model takes into consideration. In the small scale, considering the thickness of this interphase as variable and characteristic length scale, the influence of nanoparticle size on the overall elastic properties is calculated. In the large scale, an interface model of homogenization is proposed; this model too calculates the elastic properties of the overall nanocomposite as a function of inclusion size in microsize representative volume elements. In the large scale, the existence of surface stress and strains is a result of “sticking” behavior of the matrix to the inclusion surfaces. By combining these two models, we can determine the effective elastic properties of a hybrid nanocomposite as a function of nanoparticle size, microscale inclusion size, interphase thickness, and volume fractions. The model predictions are in good agreement with the experimental data provided in the literature.

Size- and dose-dependent cytotoxicity of ZIF-8 based on single cell analysis

ZIF-8 nanoparticles (NPs) has been demonstrated with good potential in drug delivery, which causes an increasing attention on relevant toxicity study. In this work, MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide), glutathione (GSH), reactive oxygen species (ROS), stain analysis and gene detection assays were performed on ZIF-8 (50, 90 and 200 nm) incubated HepG2 cells. Moreover, time-resolved inductively coupled plasma mass spectrometry (TRA-ICP-MS) was applied for single cell analysis; the variation in cellular zinc amount and the proportion of zinc up-taken cells was investigated as a function of NPs size, incubation concentration/time and elimination. Smaller size of ZIF-8 NPs would lead to higher zinc accumulation and toxicity. The function of ZIF-8 on cells is assumed to be mainly related to zinc intracellular accumulation. The possible action path is presented as high accumulation of zinc in ZIF-8 incubated cells lead to high ROS level and cellular inflammation, ultimately inducing necrocytosis. For better understanding of the bio-effect of ZIF-8, ZnO NPs and Zn2+ incubated HepG2 cells were evaluated in the same way. Higher accumulation of zinc in larger part of the cell population was found in ZIF-8 incubated cells than that in ZnO NPs incubated cells. It demonstrated higher bioavailability for ZIF-8 over ZnO NPs. While, in drug delivery application, the possible risk of the remained intracellular ZIF-8 cannot be ignored.

Electromagnetic Thermal Energy Transfer in Nanoparticle Assemblies Below Diffraction Limit

Abstract Fabrication of micro- and nanoscale electronic components has become increasingly demanding due to device and interconnect scaling combined with advanced packaging and assembly for electronic, aerospace, and medical applications. Recent advances in additive manufacturing have made it possible to fabricate microscale, 3D interconnect structures but heat transfer during the fabrication process is one of the most important phenomena influencing the reliable manufacturing of these interconnect structures. In this study, optical absorption and scattering by three-dimensional (3D) nanoparticle packings are investigated to gain insight into micro/nano heat transport within the nanoparticles. Because drying of colloidal solutions creates different configurations of nanoparticles, the plasmonic coupling in three different copper nanoparticle packing configurations was investigated: simple cubic (SC), face-centered cubic (FCC), and hexagonal close packing (HCP). Single-scatter albedo (ω) was analyzed as a function of nanoparticle size, packing density, and configuration to assess effect for thermo-optical properties and plasmonic coupling of the Cu nanoparticles within the nanoparticle packings. This analysis provides insight into plasmonically enhanced absorption in copper nanoparticle particles and its consequences for laser heating of nanoparticle assemblies.

Anionic nanoparticles enable the oral delivery of proteins by enhancing intestinal permeability.

The oral delivery of bioactive peptides and proteins is prevented by the intestinal epithelial barrier, in which intercellular tight junction complexes block the uptake of macromolecules. Here we show that anionic nanoparticles induce tight junction relaxation, increasing intestinal permeability and enabling the oral delivery of proteins. This permeation-enhancing effect is a function of nanoparticle size and charge, with smaller (≤ 200 nm) and more negative particles (such as silica) conferring enhanced permeability. In healthy mice, silica nanoparticles enabled the oral delivery of insulin and exenatide, with 10 U kg-1 orally delivered insulin sustaining hypoglycaemia for a few hours longer than a 1 U kg-1 dose of subcutaneously injected insulin. In healthy, hyperglycaemic and diabetic mice, the oral delivery of 10 U kg-1 insulin led to a dose-adjusted bioactivity of, respectively, 35%, 29% and 23% that of the subcutaneous injection of 1 U kg-1 insulin. The permeation-enhancing effect of the nanoparticles was reversible, non-toxic, and attributable to the binding to integrins on the surface of epithelial cells.

Reciprocal Control of Hierarchical DNA Origami-Nanoparticle Assemblies.

DNA origami mechanisms offer promising tools for precision nanomanipulation of molecules or nanomaterials. Recent advances have extended the function of individual DNA origami devices to material scales via hierarchical assemblies. However, achieving rapid and precise control of large conformational changes in hierarchical assemblies remains a critical challenge. Here, we demonstrate a method for controlling DNA origami-nanoparticle assemblies through a multiscale approach, in which nanoparticles impart control on the conformation of individual DNA origami mechanisms, whereas DNA origami assemblies control the conformation of nanoparticle arrays. Specifically, we show that the angular distributions of DNA origami hinge mechanisms are tunable as a function of nanoparticle size and distance from the hinge vertex. We selectively adjust the affinity of nanoparticle binding sites, resulting in hinge actuation via DNA melting without releasing the nanoparticle, thereby enabling rapid and reversible temperature-based actuation. Finally, we demonstrate this rapid actuation in DNA origami-nanoparticle arrays of length scales extending over a micron. These results provide guiding principles toward the design of dynamic, DNA-origami hierarchical materials capable of storing and releasing mechanical energy.

Numerical Modeling of Nanofluid Thermal Conductivity: The Effect of Nanonetwork on Thermal Transport Behavior

Abstract Nanofluids have drawn increasing attention in heat transfer applications due to their anomalous enhancement of the thermophysical properties in contemporary literature. Various studies have shown that the addition of minute concentration of the nanoparticles to a base solvent can yield dramatic enhancement of the effective thermal conductivity. A number of parameters have been reported to affect the level of such enhancement such as size, shape, morphology, concentration, and material properties of the nanoparticles. Many different theoretical models have also been proposed in the past literature for predicting the thermal conductivity of nanofluids under different conditions. In general, these models are based on either simplified static composite model or nanoconvection effect considering the Brownian motion of the nanoparticles. However, a few studies have explored the impact of nanoparticle aggregation on the nanofluid thermal conductivity. In particular, the formation of porous percolation structure by the nanoparticles can alter the effective thermal conductivity of nanofluid substantially. In this study, a two-stage numerical simulation is performed to analyze the thermal transport behavior inside nanofluid considering different levels of percolation network formed by the nanoparticles. Based on the simulation results, an empirical model is developed to predict the effective thermal conductivity of nanofluid as a function of nanoparticle size, concentration, and the permeability of nano-aggregation. The results demonstrated a strong dependence of nanofluid thermal conductivity on the nanocluster density, where a looser nanonetwork can yield a significantly higher level of thermal conductivity enhancement under the same particle size and concentration conditions.

Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis.

Metal nanoparticles have received substantial attention in the past decades for their applications in numerous areas, including medicine, catalysis, energy, and the environment. Despite these applications, the fundamentals of adsorption on nanoparticle surfaces as a function of nanoparticle size, shape, metal composition, and type of adsorbate are yet to be found. Herein, we introduce the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition, and different adsorbates by combining first principles calculations with machine learning. Our model fits a large number of data and accurately predicts adsorption trends on nanoparticles (both monometallic and alloy) that have not been previously seen. In addition to its application power, the model is simple and uses tabulated and rapidly calculated data for metals and adsorbates. We connect adsorption with stability behavior of nanoparticles, thus advancing the design of optimal nanoparticles for applications of interest, such as catalysis.

A novel mass transfer coefficient correlation for improved rare earth metals extraction via supported nanoliquids membrane

Rare earth metals Gadolinium (Gd) and Neodymium (Nd) extraction from aqueous media were investigated by supported nanoliquid membrane. The supported nanoliquid membrane was prepared by dispersion of nanoparticles in organic membrane phase. Different sizes and concentrations of the nanoparticles were tested in order to find their influence on the extraction. Using hydrophobic nanoparticle SiO2, permeability coefficient improvement of 28.3% and 49.5% for Gd and Nd extraction were observed, respectively. It was found that the SiO2 nanoparticle gained the highest intensification in metal ion extraction via supportel nanoliquid membrane. Moreover, a novel correlation for mass transfer coefficient prediction was developed with AARE of 17.03%, based on theoretical equations related to the supported liquid membrane transport mechanism which was function of nanoparticle size and concentration.

Ordered Hybrid Assembly of Palladium Nanoparticles and Perylene Molecules on an Alumina Template

Combining in a synergic way inorganic and organic matter at nanometre level has become a key research pathway for nanoelectronics, light harvesting, energy storage or sensing. Herein, we demonstrate the nanoscale ordering at room temperature of a two-dimensional hybrid assembly combining a long-range ordered array of Pd nanoparticles (NPs) with perylene molecules. The ordering, driven by the periodic superstructure of the Al2O3 ultrathin layer support, has been achieved for 0.9 nm diameter NPs and flat-lying molecules with a 2:1 perylene:NP relative ratio. At larger NP size and perylene coverage, molecules tilt up on alumina and adsorb on NPs. Combined near-field microscopy and optical spectroscopies provide a detailed understanding of the structural properties as a function of NP size and molecular coverage. This hybrid assembly opens the way to study at the molecular level the optical and electronic properties resulting from the coupling of organic molecules and nanoparticles using multi-scale surface sensitive techniques.

Gluing Interfaces with Soft Nanoparticles.

Nanoparticles have been recently shown to be able to act as effective adhesives capable of binding two soft materials together. We performed coarse-grained molecular dynamics simulations to study contact mechanics of soft nanoparticles at the interfaces between two elastic surfaces. Depending on the nanoparticle size as well as the substrates' elastic and interfacial properties, a nanoparticle at the interface between two elastic substrates could be in a bridging or Pickering state. The degree of penetration of a nanoparticle into a substrate is shown to be determined by nanoparticle size, strength of nanoparticle-substrate interactions, and nanoparticle and substrate elastic properties. Using the weighted histogram analysis method, we calculated the potential of mean force for separation of two substrates whose interface was reinforced by deformable nanoparticles. These simulations show that interface reinforcement is a function of nanoparticle size and elastic modulus. The most effective reinforcement of the interface was observed for the softest nanoparticles which could result in close to 8 times increase in the work of adhesion.

A General Hybrid GMDH–PNN Model to Predict Thermal Conductivity for Different Groups of Nanofluids

In this study, a general model for estimating the nanofluids (NFs) thermal conductivity by using a hybrid group method of data handling polynomial neural network (GMDH–PNN) has been investigated. NFs thermal conductivity was modeled as a function of nanoparticle size and volume fraction, nanoparticle and base fluid thermal conductivity, and base fluid temperature. For this purpose, a network that contains 6 hidden layers with 2 inputs in each layer and with training algorithm of least squares regression has been applied. The results showed a good accuracy for estimating the thermal conductivity of NFs with a root mean squared error (RMSE) of 0.03027 for 118 systems containing 1929 training data sets. Furthermore, the RMSE for 27 systems containing 244 data as the validation sets was 0.02843 and also mean absolute percentage errors (MAPE) for training and validation data sets were 4.47 and 4.59%, respectively. Moreover, the proposed hybrid GMDH–PNN model was compared with different models from literature for different groups of NFs. The results indicated an improvement in prediction of thermal conductivity with lower errors compared to the previous models.

Nano-Particle VO2 Insulator-Metal Transition Field-Effect Switch with 42 mV/decade Sub-Threshold Slope

The possibility of controlling the insulator-to-metal transition (IMT) in nano-particle VO2 (NP-VO2) using the electric field effect in a metal-oxide-VO2 field-effect transistor (MOVFET) at room temperature was investigated for the first time. The IMT induced by current in NP-VO2 is a function of nano-particle size and was studied first using the conducting atomic force microscope (cAFM) current-voltage (I-V) measurements. NP-VO2 switching threshold voltage (VT), leakage current (Ileakage), and the sub-threshold slope of their conductivity (Sc) were all determined. The cAFM data had a large scatter. However, VT increased as a function of particle height (h) approximately as VT(V) = 0.034 h, while Ileakage decreased as a function of h approximately as Ileakage (A) = 3.4 × 10−8e−h/9.1. Thus, an asymptotic leakage current of 34 nA at zero particle size and a tunneling (carrier) decay constant of ~9.1 nm were determined. Sc increased as a function of h approximately as Sc (mV/decade) = 2.1 × 10−3eh/6 and was around 0.6 mV/decade at h~34 nm. MOVFETs composed of Pt drain, source and gate electrodes, HfO2 gate oxide, and NP-VO2 channels were then fabricated and showed gate voltage dependent drain-source switching voltage and current (IDS). The subthreshold slope (St) of drain-source current (IDS) varied from 42 mV/decade at VG = −5 V to 54 mV/decade at VG = +5 V.

Unraveling Polymeric Nanoparticles Cell Uptake Pathways: Two Decades Working to Understand Nanoparticles Journey to Improve Gene Therapy.

Polymeric nanoparticles have aroused an increasing interest in the last decades as novel advanced delivery systems to improve the treatment of many diseases. Hard work has been performed worldwide designing and developing polymeric nanoparticles using different building blocks, which target specific cell types, trying to avoid bioaccumulation and degradation pathways. The main handicap of the design is to understand the final fate and the journey that the nanoparticle will follow, which is intimately ligated with the chemical and physical properties of the nanoparticles themselves and specific factors of the targeted cells. Although the huge number of published scientific articles regarding polymeric nanoparticles for biomedical applications, their use in clinics is still limited. This fact could be explained by the limited data reporting the interaction of the huge diversity of polymeric nanoparticles with cells. This knowledge is essential to understand nanoparticle uptake and trafficking inside cells to the subcellular target structure.In this chapter, we aim to contribute to this field of knowledge by: (1) summarizing the polymeric nanoparticles properties and cellular factors that influence nanoparticle endocytosis and (2) reviewing the endocytic pathways classified as a function of nanoparticle size and as a function of the receptor playing a role. The revision of previously reported endocytic pathways for particular polymeric nanoparticles could facilitate scientist involved in this field to easily delineate efficient delivery systems based on polymeric nanoparticles.

On the mechanism of pulsed laser ablation of phthalocyanine nanoparticles in an aqueous medium

Laser ablation of phthalocyanine nanoparticles has potential for cancer treatment. The ablation is accompanied by the formation of microbubbles and the sublimation of nanoparticles. This was investigated in a liquid medium simulating tissue using optical-acoustic and spectral-luminescent methods. The thresholds for the appearance of microbubbles have been determined as a function of nanoparticle size. For the minimal size particles (80 nm) this threshold is equal to about 20–25 mJ cm−2 and for the maximal size particles (230 nm) this threshold is equal to about 7 mJ cm−2. It was estimated that the particle temperature at which bubbles arise is near 145 °С.

(Invited) Studies of Li Insertion in Magnetite at Multiple Length Scales

Magnetite, Fe3O4 is a low-cost inverse-spinel material with high theoretical capacity, 926 mAh/g. Magnetite exhibits solid-state transport resistances, that have been alleviated through nanostructuring. However, the nanoparticles have been observed to form secondary structural motifs upon electrode fabrication. Physics-based modeling has revealed that resistances on both the nano (crystal) scale and on the scale of the secondary micron-sized structural motifs both contribute to the overpotential observed upon discharge.1,2 These resistances are found to be a function of cycle rate and also electrode fabrication methods, as synthetic methods have been utilized to eliminate the secondary structural motifs.3 Modeling the interaction of lithium and the Fe3O4 crystal lattice at the atomic scale has also been employed utilizing DFT, and it has been found that atomic scale effects have measurable performance implications at the device-scale. DFT calculations have revealed that the high potentials (~3.0 V) initially observed at low discharge concentrations of lithium into magnetite (x~1.0) cannot be due to Li insertion into the pristine Fe3O4 lattice, due to phase instability and poor voltage prediction agreement of the resultant intercalated structures, LixFe3O4. Instead, DFT calculations and pre-edge XANES experiments have indicated that there exist Fe cationic defects, the concentration of which occur as a function of nanoparticle size, where smaller particles are observed to have a higher volume fraction of defects.4 DFT has predicted that lithium insertion into these Fe cationic defects are responsible for the initial high intercalation voltages. The nanoparticle size dependence of cationic defects, leads to the prediction of a nanoparticle-size-dependent reversible potential, corroborated experimentally. Additionally, the experimental observation of increased capacity of smaller nanoparticles over larger nanoparticles can also be partly explained by the increased concentration of cationic defects in the smaller particles. Here, we show that modeling at the atomic scale gives insight into the high potentials occurring at low lithium concentrations and the nanoparticle size-dependence of the reversible potential. Coarse grained modeling at the nano and micron scales have identified the unexpected impact of the secondary micron sized motifs on the overpotentials. The use of modeling at atomic, nano, and micron scales has revealed that all three length scales have nontrivial and measurable implications at the device scale. Understanding of these complex nonequilibrium devices over multiple length and time scales and is a requirement if we are to realize the next generation of lithium-ion batteries. In addition to atomic scale modeling, continuum level modeling of batteries is an established method for interrogating performance. Previous efforts have focused on obtaining good model agreement with experimentally observed electrochemical measurements; where good agreement is usually used to validate the model and the concentration profiles predicted by the models are analyzed. Efforts have been made to directly interrogate the concentration profiles derived from battery operation, however, because these systems are highly dynamic even short time gaps between operation and characterization can result in significantly different profiles. Operando EDXRD characterization of battery electrodes has enabled us to directly compare the battery environment during operation to the results predicted from simulations. The final part of my talk will be about how to leverage techniques from the rapidly advancing field of computer science, specifically machine learning, to assist in development of physics-based modeling. The utility of physics-based models is well known, however, good models require significant time and expertise to develop, and while the results of the models are accessible to experimentalists, model development is practically restricted to those with the expertise. Using relatively simple and well-established computer science techniques such as sampling and cross-validating we believe we can significantly reduce the time necessary for model development and outsource parameter estimation and model selection to a computer program. (1) Knehr, K. W.; Brady, N. W.; Cama, C. A.; Bock, D. C.; Lin, Z.; Lininger, C. N.; Marschilok, A. C.; Takeuchi, K. J.; Takeuchi, E. S.; West, A. C., J. Electrochem. Soc. 2015, 162 (14), A2817–A2826. (2) Knehr, K. W.; Brady, N. W.; Lininger, C. N.; Cama, C. A.; Bock, D. C.; Lin, Z.; Marschilok, A. C.; Takeuchi, K. J.; Takeuchi, E. S.; West, A. C., ECS Trans. 2015, 69 (1), 7–19. (3) Bock, D. C.; Pelliccione, C. J.; Zhang, W.; Wang, J.; Knehr, K. W.; Wang, J.; Wang, F.; West, A. C.; Marschilok, A. C.; Takeuchi, K. J.; Takeuchi, E. S. D ACS Appl. Mater. Interfaces 2016, 8 (18), 11418–11430. (4) Menard, M. C.; Marschilok, A. C.; Takeuchi, K. J.; Takeuchi, E. S., Electrochim. Acta 2013, 94 (42), 320–326.