Complex nanoshaped structures (nanoshape structures here are defined as shapes enabled by sharp corners with radius of curvature <5 nm) have been shown to enable emerging nanoscale applications in energy, electronics, optics, and medicine. This nanoshaped fabrication at high throughput is well beyond the capabilities of advanced optical lithography. While the highest-resolution e-beam processes (Gaussian beam tools with non-chemically amplified resists) can achieve <5 nm resolution, this is only available at very low throughputs. Large-area e-beam processes, needed for photomasks and imprint templates, are limited to similar to 18 nm half-pitch lines and spaces and similar to 20 nm half-pitch hole patterns. Using nanoimprint lithography, we have previously demonstrated the ability to fabricate precise diamond-like nanoshapes with similar to 3 nm radius corners over large areas. An exemplary shaped silicon nanowire ultracapacitor device was fabricated with these nanoshaped structures, wherein the half-pitch was 100 nm. The device significantly exceeded standard nanowire capacitor performance (by 90%) due to relative increase in surface area per unit projected area, enabled by the nanoshape. Going beyond the previous work, in this paper we explore the scaling of these nanoshaped structures to 10 nm half-pitch and below. At these scales a new "shape retention" resolution limit is observed due to polymer relaxation in imprint resists, which cannot be predicted with a linear elastic continuum model. An all-atom molecular dynamics model of the nanoshape structure was developed here to study this shape retention phenomenon and accurately predict the polymer relaxation. The atomistic framework is an essential modeling and design tool to extend the capability of imprint lithography to sub-10 nm nanoshapes. This framework has been used here to propose process refinements that maximize shape retention, and design template assist features (design for nanoshape retention) to achieve targeted nanoshapes.
A first principles understanding of the sound field produced by multirotor drones in hover is presented. Propeller diameters ranging from 8 to 12 in. are examined and with configurations comprising an isolated rotor, quadcopter, and hexacopter configuration. The drone pitch, defined as the ratio of drone diameter to rotor diameter, is the same for all multirotor configurations and is valued at 2.25. A six-degree-of-freedom load cell is used to assess the aerodynamic performance of each configuration, whereas an azimuthal array of 1∕2 in. microphones, placed between two and three hub-center diameters from the drone center, is used to assess the acoustic near field. The analysis is performed using standard statistical metrics such as sound pressure level and overall sound pressure level and is presented to demonstrate the relationship between the number of rotors, the drone rotor size, and its aerodynamic performance (thrust) relative to the near-field acoustics.
Characterizing the pore structure of shale is essential to understanding fluid transport through the matrix and optimizing any stimulation plan. Organic shales are heterogeneous at multiple scales, and the characteristic length scales or correlation lengths are often longer than the scale of samples used for laboratory analysis. Using laboratory data to make predictions at the wellbore scale therefore requires careful upscaling. Using samples of Barnett and Eagle Ford shales, and a siliceous, oil-bearing shale from the northern Rocky Mountains, we performed high- pressure mercury intrusion (HPMI) and low-pressure nitrogen sorption. We determined the properties of the pore network (size distribution, connectivity, spatial correlation) by constructing representative pore networks that allowed reconstruction of the HPMI and nitrogen sorption data. We then upscaled the results determining the correlation length with a percolation-based scaling function. Based on the HPMI and nitrogen sorption measurements, pore networks tend to be very poorly connected at the micron scale, with average coordination numbers between 2 and 3. Clusters of connected pores are typically a few hundred microns in size. Our work has significant implications for using laboratory measurements to predict reservoir properties. While samples are relatively homogeneous at the scale of SEM images or HPMI/nitrogen sorption measurements, organic-rich samples in particular have longer-range correlations that are not captured at this scale and yet exert significant control on transport properties. This will affect production from a fracture-stimulated well since induced microcracks and their interactions with the in situ pore structure are extremely important for moving hydrocarbons toward the main induced fracture system, as demonstrated by previous researchers. Multi-scale characterization is therefore necessary to gain a full understanding of the shale matrix.
Engineered functional neural interfaces (fNIs) serve as essential abiotic–biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms. fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.
Background Despite significant advancements of optical imaging techniques for mapping hemodynamics in small animal models, it remains challenging to combine imaging with spatially resolved electrical recording of individual neurons especially for longitudinal studies. This is largely due to the strong invasiveness to the living brain from the penetrating electrodes and their limited compatibility with longitudinal imaging.
Understanding brain functions at the circuit level requires time-resolved simultaneous measurement of a large number of densely distributed neurons, which remains a great challenge for current neural technologies. In particular, penetrating neural electrodes allow for recording from individual neurons at high temporal resolution, but often have larger dimensions than the biological matrix, which induces significant damage to brain tissues and therefore precludes the high implant density that is necessary for mapping large neuronal populations with full coverage. Here, it is demonstrated that nanofabricated ultraflexible electrode arrays with cross-sectional areas as small as sub-10 µm2 can overcome this physical limitation. In a mouse model, it is shown that these electrodes record action potentials with high signal-to-noise ratio; their dense arrays allow spatial oversampling; and their multiprobe implantation allows for interprobe spacing at 60 µm without eliciting chronic neuronal degeneration. These results present the possibility of minimizing tissue displacement by implanted ultraflexible electrodes for scalable, high-density electrophysiological recording that is capable of complete neuronal circuitry mapping over chronic time scales.
The design, construction and preliminary measurements of a new test stand for accurately assessing the shear stress acting at the fluid surface interface of wall bounded flows is discussed. This stand is based on control volume analysis whereby a fully developed turbulent velocity profile produces shear forces which equate to the pressure drop measured between fixed points in a constant area pipe. The calibration stand is designed to facilitate both subsonic and supersonic flow. Subsonic flow conditions are achieved by placing different diameter nozzles at the exhaust of the test section thereby permitting different free stream velocities and mass flow rates for a given ratio of the total pressure to static pressure in the pipe. The advantages of this facility is in its ability to produce a broadrange of Reynolds numbers (based on centerline velocity and pipe diameter) and elevatedpressures that are required to gauge the sensitivity of modern shear stress sensors.
Application of nanoparticles in the subsurface typically requires the use of surface coatings to maintain stability in dispersion and to provide particular functionality. However, the presence of surface coatings may hinder or mask properties of the bare nanoparticle cores, which may be a concern in nuclear magnetic resonance (NMR) applications. In this study, we used different amounts of 3-aminopropyltriethoxysilane (APTES) coating on Fe3O4 magnetic nanoparticles (A-MNPs). We measured the longitudinal relaxation time (T1) values of those A-MNPs suspensions, and computed and compared the surface relaxivities of A-MNPs with different amounts of APTES coating. Our results showed that when the mass percentage of APTES coating increased from 1.60 to 4.22 wt%, the A-MNPs’ surface relaxivity decreased by 26.1%. To determine the surface relaxation mechanism(s), we also used various volume fractions of D2O to dilute A-MNP dispersions to two concentrations: 0.01 and 0.002 g/L Fe. In the final mixtures, the volume fractions of D2O were fixed as 0-, 30-, 50-, and 70-vol%. The NMR measurements indicated that, at relatively high Fe concentration (0.01 g/L), electron-proton interaction dominates surface relaxation, and the hydrogen atoms in the APTES did not significantly alter the surface relaxation mechanism of the nanoparticles. At a lower Fe concentration (0.002 g/L), proton-proton relaxation, due to the APTES, also played a role in the overall relaxation mechanism on nanoparticle surfaces. A-MNPs with more APTES coating showed lower apparent surface relaxivities with higher D2O volume fractions in the mixture, indicating a greater amount of proton-proton relaxation on the nanoparticle surfaces.
In well logging applications, nuclear magnetic resonance (NMR) is recognized as a powerful tool to differentiate various fluids inside porous media. However, it can be challenging to do so in complex situations, e.g. when fluid peaks overlap with each other. Water-soluble contrast agents, such as MnCl2 or Gd-EDTA, have been proposed for use to accelerate the water relaxation, thus separating the water signal from those of other fluids. Together with these contrast agents, the log-inject-log method is used and the difference between the two logs is attributed to the doped water phase. This application only works with water-based mud. To extend its use to oil-based mud (OBM), it is desirable to find alternatives to water-soluble contrast agents that are compatible with OBM.
In this work, we introduce a new group of doping agents: oil-soluble contrast agents. We selected several iron-based complex compounds that are oil-soluble, and tested and evaluated their effects on oil signal relaxation using a laboratory NMR apparatus. We also tested hydrophobic iron oxide nanoparticles as a contrast agent. The results showed that both the complex compounds and nanoparticles were able to reduce the transverse relaxation time of oil from longer than 2 s to less than 20 ms. To demonstrate their applicability in porous media, we injected doped oil into gas-saturated Berea sandstone and limestone core plugs. With conventional 1D NMR measurements or the use of water-soluble contrast agents, it is not straightforward to discern the gas signal from the OBM signal. Our experiments showed that the gas signal could be easily identified in the presence of doped oil via simple T2 scans. We also performed experiments to demonstrate that the peaks of water and doped oil could be readily differentiated.
The use of oil-soluble doping agents can significantly enhance the contrast of the NMR signals originated from different formation fluids, thus facilitating the fluid typing process. It provides a key alternative to the current water-doping technique. It is particularly advantageous when changing the oil relaxation is desired, such as for eliminating signal interferences from OBM invasion and differentiating heavy oil from clay-bound water. It also provides the possibility of speeding up the logging process by dramatically reducing the oil relaxation time. In addition, they can be employed in the laboratory for various purposes such as water saturation determination and fluid displacement monitoring. These contrast agents can also be of useful when oil-soluble contrast agents are desired in other fields, such as medical imaging applications.
We tested how different emulsion characteristics would affect transport through sandstone cores and recovery of residual oil. Our results show that the behavior of nanoparticle-stabilized emulsions flowing through porous media can be described in terms of filtration theory and electrostatic and van der Waals interactions. Residual oil recovery was enhanced by optimizing em—ulsion characteristics such as salinity, method of generation, and zeta potential. We emulsified widely available, low-cost natural gas liquids in brine using polyethylene glycol-coated silica nanoparticles. Emulsions were generated via sonication at varying salinities and zeta potentials for observations of emulsion characteristics. We conducted corefloods in Boise sandstone to assess the effects of different emulsion properties on residual oil recovery of heavy oils, effective permeability reduction capabilities (i.e. conformance control), and in-situ emulsion stability. Emulsions with high salinity content resulted in better in situ emulsion stability and up to 89% recovery of residual mineral oil at low injection rates. By increasing the salinity, the magnitude of the repulsive electrostatic force between emulsion droplets and grain surfaces is decreased, leading to increased droplet interception on grain surfaces. This results in more extensive droplet-pore throat blockage, redirecting the displacing fluid into less permeable zones. Increasing the magnitude of the droplet zeta potential of injected emulsions marginally increased in oil recovery, significantly reduced permeability, and increased in situ emulsion stability. The best residual oil recovery occurs when emulsion droplets can persist without coalescence under the pressures required to push them into small pore throats, while simultaneously moving through the larger pore throats rather than being mechanically or electrostatically retained. Proper emulsion flood design, therefore, must incorporate characterization of both the pore structure and the electrostatic properties of reservoir rocks and how these will interact with the emulsions.
Real-time drilling optimization is a topic of significant interest because of its economic value, and its importance increases particularly during periods of low oil prices. This paper evaluates different optimization strategies and algorithms for real-time optimization of an objective function (function to be optimized) specific to drilling. The objective function optimized here is derived from a data-driven (or machine-learning) model with an unknown functional form. A data-driven model has been used to calculate the objective function [rate of penetration (ROP)] because it has been shown to be more efficient in ROP prediction relative to deterministic models (Hegde and Gray 2017). The data-driven ROP model is built using machine-learning algorithms; measured drilling parameters [weight on bit (WOB), revolutions per minute (rev/min), strength of rock, and flow rate] are used as inputs to predict the ROP.
Real-time drilling optimization that is data-driven is challenging because of run-time constraints. This is perceived as a handicap for data-driven models because their functional form is unknown, making them more difficult to optimize. This paper evaluates algorithms depending on their ability to best maximize the objective (ROP) and their time effectiveness. Two simple yet robust algorithms, the eyeball method and the random-search method, are presented as plausible solutions to this problem. These methods are then compared with popular metaheuristic algorithms, evaluating the tradeoff between improvement in the objective (search for a global optimal) and the computational time of run.
Using results from the simulations conducted in this paper, we concluded that data-driven models can be used for real-time drilling despite their computational constraints by choosing the right optimization algorithm. The best tradeoff in terms of ROP increase as well as computational efficiency evaluated in this paper is the simplex algorithm. The ROP was improved by 30% on average with a variance of 2.5% in the test set over 14 formations that were tested.
The matrix permeability of shale is controlled by the microstructure of shale pore network. Therefore, a thorough understanding of shale pore structure is fundamental to the prediction of shale permeability. We constructed a physically representative pore network model for two Barnett Shale samples. We predicted a Darcy permeability of 7.19 nanodarcies (nd) and apparent permeability of 57.34 nd for these samples. We explored the pore structure of shale matrix with low-pressure nitrogen adsorption/desorption isotherms. The pore size distribution, the network connectivity, as well as pore spatial arrangement are determined for the organic matter and the whole matrix. We separated pores in the network model into two groups: the affiliated pores and the dominant pores. For these samples, the affiliated pores are pores with diameters smaller than 8 nm and the dominant pores are with diameters larger than 8 nm. The affiliated pores develop on the walls of the dominant pores. The cutoff value between affiliated pores and dominant pores may differ for different samples. The pore spatial arrangement is validated against previous literature resultsand a diagenetic explanation for development of shale pore system. The network model can be used to predict shale permeability and other petrophysical properties.
Microfractures are important mechanical discontinuities in shales and are important for fluid flow during production. Understanding their properties is crucial for accurate shale production prediction and implementing effective stimulation strategies. Scanning electron microscope (SEM) images are useful for characterizing shale microstructure, but manual image analysis is often challenging and time consuming. We present an alternative method for quickly characterizing microfractures and obtaining pore structure information from SEM images using machine learning algorithms and image processing. Using this approach, SEM images were obtained from deformed and intact samples of a carbonate rich shale and a siliceous shale with the goal of identifying microfractures. Support vector machine, convolutional neural networks, and four pretrained convolutional neural networks were implemented to differentiate SEM images containing fractures (frac-images) and SEM images containing no fractures (non-frac-images). Images containing fractures were identified with 92% training accuracy and 88% testing accuracy. A pretrained convolutional neural network with 16 layers (vgg16) was shown to perform best for this image classification task.
Reduced-order models of the airwake produced by the flow over a simple frigate shipare developed using POD based methods. The focus is to understand the trade spacebetween cost and accuracy, where different forms of the POD technique are concerned.Of particular importance is the upfront expense of employing ‘classical’ or snapshot formsof the POD technique in both scalar and vector forms using either time suppressed data(conventional-POD), or kernels constructed from cross-spectral densities of the fluctuatingvelocity. The latter approach is referred to as harmonic-POD so as not to exclude harmonictransforms in space. The flow over a simple frigate ship is an ideal test bed given that it isunsteady, three-dimensional, inhomogenous in all spatial directions, and stationary in time. The spatial modes from all three techniques are shown to correspond to unique time-scales, thereby demonstrating how the preservation of the temporal behavior associated with a particular spatial scale is not unique to the harmonic-POD approach.