Correlating Interlayer Spacing and Separation Capability of Graphene Oxide Membranes in Organic Solvents.

Membranes synthesized by stacking two-dimensional graphene oxide (GO) hold great promise for applications in organic solvent nanofiltration. However, the performance of a layer-stacked GO membrane in organic solvent nanofiltration can be significantly affected by its swelling and interlayer spacing, which have not been systematically characterized. In this study, the interlayer spacing of the layer-stacked GO membrane in different organic solvents was experimentally characterized by liquid-phase ellipsometry. To understand the swelling mechanism, the solubility parameters of GO were experimentally determined and used to mathematically predict the Hansen solubility distance between GO and solvents, which is found to be a good predictor for GO swelling and interlayer spacing. Solvents with a small solubility distance (e.g., dimethylformamide, N-methyl-2-pyrrolidone) tend to cause significant GO swelling, resulting in an interlayer spacing of up to 2.7 nm. Solvents with a solubility distance larger than 9.5 (e.g., ethanol, acetone, hexane, and toluene) only cause minor swelling and are thus able to maintain an interlayer spacing of around 1 nm. Correspondingly, GO membranes in solvents with a large solubility distance exhibit good separation performance, for example, rejection of more than 90% of the small organic dye molecules (e.g., rhodamine B and methylene blue) in ethanol and acetone. Additionally, solvents with a large solubility distance result in a high slip velocity in GO channels and thus high solvent flux through the GO membrane. In summary, the GO membrane performs better in solvents that are unlike GO, i.e., solvents with large solubility distance

Modeling of Electro-deposition and Mechanical Stability at Li Metal/Solid Electrolyte Interface during Plating in Solid-State Batteries

Interfacial deposition stability between Li metal and a solid electrolyte (SE) is important in preventing interfacial contact loss, mechanical fracture, and dendrite growth in Li-metal solid-state batteries (SSB). In this work, we investigate the deposition and mechanical stability at the Li metal/SE interface and its consequences (such as SE fracture and contact loss). A wide range of contributing factors are investigated, such as charge and mass transfer kinetics, the plasticity of Li metal and fracture of the SE, and the applied stack pressure. We quantify the effect of the ionic conductivity of the SE, the exchange current density of the interfacial charge-transfer reaction and SE surface roughness on the Li deposition stability at the Li metal/SE interface. We also propose a mechanical stability window for the applied stack pressure that can prevent both contact loss and SE fracture, which can be extended to other metal-electrode (such as Sodium) SSB systems.Comment: 38 pages, 7 figure

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials

A Molecular Dynamics Study on Rotational Nanofluid and Its Application to Desalination

In this work, we systematically study a rotational nanofluidic device for reverse osmosis (RO) desalination by using large scale molecular dynamics modeling and simulation. Moreover, we have compared Molecular Dynamics simulation with fluid mechanics modeling. We have found that the pressure generated by the centrifugal motion of nanofluids can counterbalance the osmosis pressure developed from the concentration gradient, and hence provide a driving force to filtrate fresh water from salt water. Molecular Dynamics modeling of two different types of designs are performed and compared. Results indicate that this novel nanofluidic device is not only able to alleviate the fouling problem significantly, but it is also capable of maintaining high membrane permeability and energy efficiency. The angular velocity of the nanofluids within the device is investigated, and the critical angular velocity needed for the fluids to overcome the osmotic pressure is derived. Meanwhile, a maximal angular velocity value is also identified to avoid Taylor-Couette instability. The MD simulation results agree well with continuum modeling results obtained from fluid hydrodynamics theory, which provides a theoretical foundation for scaling up the proposed rotational osmosis device. Successful fabrication of such rotational RO membrane centrifuge may potentially revolutionize the membrane desalination technology by providing a fundamental solution to the water resource problem

A Computational Study of Nanoporous Material used in Ions/Water Separation

In this thesis, the potential applications of typical nanoporous material (including Graphene Oxide (GO), Carbon Nanotube (CNT) and molybdenum disulfide (MoS_2)) in water and ion separation are systematically investigated. Different computational methods (including Molecular Dynamics (MD), Density functional theorem (DFT), Transport theorem in membrane) are introduced to study the mechanical and chemical properties of these materials; Depending on the corresponding experiments to be simulated, hundreds of models are built to explore properties in the process and compared with experimental results. Several independent conclusions from different models are made and described in different chapters, whose main application is for desalination. Desalination is one of the most promising approaches to provide fresh water in the face of growing water demand. Currently, commercial reverse osmosis (RO) techniques still suffer from important drawbacks, including high energy consuming and serious fouling. In order for this method can be really used to solve the water challenges of this century, a step-change is needed in RO membrane technology. Because of significant advances in the field of nanotechnology and computational material and chemical science in the past decade, it is becoming possible to develop a new generation of RO membranes.We show that all these nanoporous materials possess exceptional physical and mechanical properties, which allow for water passage while rejecting salt ions if it possessed nanometer-sized pores. By using computer simulations (Both DFT and MD) from the atomic scale to the engineering scale, we first investigated the relationship between the atomic structure of nanoporous material and its membrane properties in RO applications. Then we studied the thermodynamics, chemistry and mechanics of each material andthe water and salt surrounding it. Finally, we establish the system-level implications of promising membrane properties for desalination plants, which can act as an RO membrane with several orders of magnitude higher water permeability than traditional polymer membranes as long as the size of nanopores is well controlled.Besides of desalination, in chapter 6, we also investigate diffusion and selectivity of carbon nanotube embedded in the cell membrane to $Na^+$ and $K^+$ ions, in order to find the minimum diameter for different ions to pass through, and compare with the so called biological selectivity filters. The present work is a systematic study of ion and water transport through CNT embedded in cell membrane.Overall, our efforts on this issue are of importance for future simulation studies investigating water and ion conduction through nanoscopic channels. This dissertation might also prove useful in designing more efficient nanoscopic conduits for future experimental studies, and even highlights a path for the development of next-generation membranes for clean water production in the 21st century

A Molecular Dynamics Study of Crosslinked Phthalonitrile Polymers: The Effect of Crosslink Density on Thermomechanical and Dielectric Properties

In this work, molecular dynamics (MD) and molecular mechanics (MM) simulations are used to study well-equilibrated models of 4,4′-bis(3,4-dicyanophenoxy)biphenyl (BPh)–1,3-bis(3-aminophenoxy)benzene (m-APB) phthalonitrile (PN) system with a range of crosslink densities. A cross-linking technique is introduced to build a series of systems with different crosslink densities; several key properties of this material, including thermal expansion, mechanical properties and dielectric properties are studied and compared with experimental results. It is found that the coefficient of linear thermal expansion predicted by the model is in good agreement with experimental results and indicative of the good thermal stability of the PN polymeric system. The simulation also shows that this polymer has excellent mechanical property, whose strength increases with increasing crosslink density. Lastly and most importantly, the calculated dielectric constant—which shows that this polymer is an excellent insulating material—indicates that there is an inverse relation between cross-linking density and dielectric constant. The trend gave rise to an empirical quadratic function which can be used to predict the limits of attainable dielectric constant for highly crosslinked polymer systems. The current computational work provides strong evidence that this polymer is a promising material for aerospace applications and offers guidance for experimental studies of the effect of cross-linking density on the thermal, mechanical and dielectric properties of the material