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At least 1,081 records · Page 60

Tuning Organic Semiconductor Packing and Morphology through Non-equilibrium Solution Processing (Final Report)

Organic semiconductors (OSCs) are a promising candidate to produce low-cost, large area, and flexible electronics. There has previously been successful development of organic field effect transistors (OFETs), photovoltaics (OPV), and bioelectronics using OSCs. Solution processing of these materials allows for the fabrication of large-area devices in the kinetic crystallization regime. The charge transport capabilities have been shown to depend on the morphology and molecular packing of the OSC within thin films. Our hypothesis is that solution processing conditions may significantly impact OSC morphology and as a result the charge transport. Therefore, this work has focused on obtaining a fundamental understanding of the morphology and molecular packing of OSCs for optimal device performance. Through our work, we have gained a better understanding of how these structural conditions were influenced by the solution processing conditions. Our studies have led to a more systematic understanding of the various parameters that impact OSC morphology. Solution processing leads to non-equilibrium films, thus allowing for the formation of diverse morphologies that are inaccessible by other fabrication methods. Previously, our group has focused on tuning the morphology of small-molecular OSCs [53, 55-57]. However, little work had been devoted to polymer OSCs. There was a lack of detailed studies characterizing the solution-state of polymer OSCs in terms of their conformation, degree of entanglement, polymer aggregation, and chain relaxation dynamics. The solution state properties are also likely to be influenced by the rigidity and molecular weight of the polymer OSCs investigated. Thus, we have focused our attention on gaining a better insight of polymer OSC films and solution processing methods. Through this proposal, our approach is to investigate the correlation between solution-state properties and the morphology of the resulting polymer OSC films. We worked three specific aims: investigate the effects of 1) polymer OSC solution-state properties on final film morphology; 2) molecular additives on the solution-state and final film properties; 3) controlled pre-aggregation in the solution-state in the final film morphology. More rigid and planar polymer backbones should promote interchain charge transport and more efficient interchain hopping between polymer chains. Through tailoring the polymer backbone, polymer sidechains, and molecular weight, we expected the altered solution-state properties to affect the final film morphology. In addition, reducing the entanglements and promoting chain alignment will likely prevent charge carrier trapping through conformation disorders. We thus studied different mechanisms, such as molecular additives, to reduce entanglements in solution. Devices fabricated from these solutions were expected to have improved charge transport abilities. In addition to tailoring the solution-state characteristics of the polymer OSCs, we investigated the effect of solution processing methods on the molecular packing and morphology of polymer OSC films. Two main solution processing techniques, e.g., spin-coating and solution shearing, were employed to fabricate OFETs. Spin coating was employed to prepare OSC films, which produce isotropic films. This technique creates several parameters to tune such as spin coating speed, acceleration, and time. Additionally, our research group developed the solution shearing method, which consists of the solution initially sandwiched between two plates. By sliding the top plate, the solution front is exposed and drying begins. This technique allows for the creation of aligned large crystalline domains. Solution shearing consists of different processing parameters which can affect the final morphology, such as shearing speed, substrate temperature and temperature gradient, distance between both plates, and the tilt angle of the top plate. Due to the complexity of the polymer systems investigated, numerous techniques were employed to characterize the polymer OSC solution state and films. All the materials were characterized using the DOE supported synchrotron X-ray scattering facilities at the Stanford Synchrotron Radiation Lightsource (SSRL). Using grazing incidence X-ray scattering (GIXS) and Near Edge X-ray Absorption Fine Structure (NEXAFS) techniques, the crystalline structure and molecular orientations of the thin films were measured. Optical absorption (UV-Vis) spectroscopy was employed to determine the aggregation state in solution and films of the polymer systems. Polarized UV-Vis also allowed for the determination of the relative degree of polymer chain alignment for solution sheared films. Additionally, various other techniques were used to investigate other properties within the film, such as atomic force microscopy (AFM) and solution rheology. Finally, the device performance is quantified through the fabrication and characterization of OFETs, which will highlight the effects of morphology on charge transport.

36 MATERIALS SCIENCE↗

Ionic Liquids for Direct Air Capture of CO 2 using Electric‐Field‐Mediated Moisture Gradient Process (Final Technical Report)

The final report provides executive summary, a list of publications, and information on training graduate students and postdoctoral researchers. We carried out computational and experimental research on understanding molecular-level mechanism of how CO 2 is absorbed in a solution containing ethylene glycol as the solvent and KOH as the salt in the presence of ionic liquids and under the influence of electric field. In doing so, we developed an automated high-throughput method which allowed us to measure the solubility of CO 2 in a large number of ionic liquids, considerably speeding up the CO 2 solubility measurement. We also demonstrated how varying the concentration of ionic liquids in ethylene glycol can result in a maximum in ionic conductivity. Reaction of CO 2 and subsequent release results in a 50% reduction when the process is operated at an ionic liquid-ethylene glycol concentration yielding maximum ionic conductivity amongst all the ionic liquid-ethylene glycol combinations studied as a part of this research. We utilized machine learning models to identify unique ionic liquid-solvent combinations with ionic conductivity much higher than that measured for ionic liquid-ethylene glycol combinations. We demonstrated that the rate of CO 2 reaction with KOH in ethylene glycol can be optimized with the type of ionic liquid and its concentration. Overall, the research led to publication of 10 peer-reviewed research articles and several presentations at national conferences. We are also in the process of developing additional manuscripts based on the research carried out as a part of this project. Two graduate students and two postdoctoral researchers were supported on the funding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗

Vickers hardness prediction from machine learning methods

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Ion-temperature-gradient stability near the magnetic axis of quasisymmetric stellarators

Abstract The stability of the ion-temperature gradient mode in quasisymmetric stellarators is assessed. This is performed using a set of analytical estimates together with linear gyrokinetic simulations. The peak growth rates, their corresponding real frequencies and wave-vectors are identified. A comparison is made between a first-order near-axis expansion model and eleven realistic designs obtained using numerical optimization methods. It is found that while the near-axis expansion is able to replicate the growth rates, real frequencies and perpendicular wave-vector at the inner core (both using simplified dispersion relations and first-principle gyrokinetic simulations), it leads to an overestimation of the growth rate at larger radii. An approximate analytic solution of the ITG dispersion relation for the non-resonant limit suggests growth rates could be systematically higher in quasi-axisymmetric (QA) configurations compared to quasi-helically (QH) symmetric ones. However except for very close to the axis, linear gyrokinetic simulations do not show systematic differences between QA and QH configurations.

Physics↗

Structural Analysis of Multilayer Tapered Canted-Cosine-Theta Superconducting Magnet for the Interaction Region of Electron Ion Collider

The electron ion collider (EIC) will collide high energy and highly polarized hadron and electron beams with luminosities up to 10 34 cm -2 s -1 . The magnet designs at the interaction region (IR) are challenging due to the close proximity of the hadron and electron beams. A canted cosine theta (CCT) quadrupole magnet was designed due to the space restrictions at the IR. The CCT magnet has a tapered aperture, which allows higher gradients at the interaction point (IP) side of the magnet. The tapered geometry makes the magnet not symmetrical along the axial direction. In conclusion, to validate the design in terms of the mechanical strength and the allowed deformations, a detailed structural mechanical analysis for the tapered CCT quadrupole magnet has been carried out using the finite element method (FEM) considering both the shrinking due to cool down and the electromagnetic force on the conductors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Long-Term Stability of PVD/Sputtered Au Electrical Contacts to Thermoelectric Bi 2 Te 2 : Re-evaluation of SAND2015-7203

The fabrication of long-lived electrical contacts to thermoelectric Bi 2 Te 3 -based modules is a challenging problem due to chemical incompatibilities and rapid diffusion rates. Previously, technical guidance from SAND report 2015-7203 selected electroplated Au as the preferred method for fabrication of long-lived contacts because of concerns that the grain structure of sputtered/physical vapor deposited (PVD) Au contacts can evolve during aging. We have re- evaluated PVD Au contacts and show that they are appropriate for long-life service. We measure grain size and morphology at different aging times under accelerated temperature gradient conditions, and we show that the PVD Au contacts are stable and remain relatively unchanged. The PVD Au fabricated here is not subject to the deterioration observed in the previous report.

36 MATERIALS SCIENCE↗

Multi-material direct ink writing and co-sintering of gadolinium oxide – zirconium oxide components

Methods for fabrication of multi-material or functionally graded ceramic composite architectures are of interest for numerous applications. However, conventional co-sintering of multi-material ceramic parts is a challenge because differences in the sintering behavior of the two materials leads to interfacial strain and, ultimately, component failure. Direct ink writing (DIW) is an extrusion-based additive manufacturing process that excels at multi-material printing because multiple extrusion nozzles can be installed on the same gantry system. Furthermore, the use of DIW as a method to fabricate multi-material ceramic green bodies offers an additional variable for controlling and potentially matching sintering kinetics in the slurry formulation used for two dissimilar feedstocks. In the work documented in this manuscript, we explored two strategies to successfully co-sinter multi-material ceramic oxides: slurry optimization to match sintering kinetics and material gradients to step from one material to another. This manuscript also quantifies the allowable mismatch that avoids part cracking in solid solution forming multi-material systems and discusses best strategies to reduce mismatch during co-sintering. Inks composed of gadolinium oxide (Gd 2 O 3 ) and zirconium oxide (ZrO 2 ), a surrogate for uranium oxide (UO 2 ), were thermally matched, which resulted in a sintering mismatch reduction of over 10%. It was found that ~1% mismatch is tolerable during debind cycles and that ~5% mismatch is manageable during sintering cycles after slurry formulations are optimized to match the sintering behavior. Use of continuous gradients is shown to reduce sintering mismatch, although geometric resolution may be lost due to solid solution formation.

Snarr, Patrick L. [Oak Ridge National Laboratory (↗

Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity

Abstract Plants demonstrate a broad range of responses to environmental shifts. One of the most remarkable responses is plasticity, which is the ability of a single plant genotype to produce different phenotypes in response to environmental stimuli. As with all traits, the ability of plasticity to evolve depends on the presence of underlying genetic diversity within a population. A common approach for evaluating the role of genetic variation in driving differences in plasticity has been to study genotype-by-environment interactions (G × E). G × E occurs when genotypes produce different phenotypic trait values in response to different environments. In this review, we highlight progress and promising methods for identifying the key environmental and genetic drivers of G × E. Specifically, methodological advances in using algorithmic and multivariate approaches to understand key environmental drivers combined with new genomic innovations can greatly increase our understanding about molecular responses to environmental stimuli. These developing approaches can be applied to proliferating common garden networks that capture broad natural environmental gradients to unravel the underlying mechanisms of G × E. An increased understanding of G × E can be used to enhance the resilience and productivity of agronomic systems.

60 APPLIED LIFE SCIENCES↗

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Gradient-Based Multi-Area Distribution System State Estimation

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Long-Term Stability of PVD/Sputtered Au Electrical Contacts to Thermoelectric Bi 2 Te 2 (Re-evaluation of SAND2015-7203)

The fabrication of long-lived electrical contacts to thermoelectric Bi 2 Te 3 -based modules is a challenging problem due to chemical incompatibilities and rapid diffusion rates. Previously, technical guidance from SAND report 2015-7203 selected electroplated Au as the preferred method for fabrication of long-lived contacts because of concerns that the grain structure of sputtered/physical vapor deposited (PVD) Au contacts can evolve during aging. We have re-evaluated PVD Au contacts and show that they are appropriate for long-life service. We measure grain size and morphology at different aging times under accelerated temperature gradient conditions, and we show that the PVD Au contacts are stable and remain relatively unchanged. The PVD Au fabricated here is not subject to the deterioration observed in the previous report.

36 MATERIALS SCIENCE↗

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗

A machine learning approach to photometric metallicities of giant stars

ABSTRACT Despite the advances provided by large-scale photometric surveys, stellar features – such as metallicity – generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a neural network approach for estimating the metallicities and distances of red giant stars with 8-band photometry and parallaxes from Gaia EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties in the predictions arising from the range of possible outputs at each input and from the range of models compatible with the training set (through drop-out). A two-stage procedure is adopted where an initial network to estimate photoastrometric parallaxes is trained using a large sample of noisy parallax data from Gaia EDR3 and then a secondary network is trained using spectroscopic metallicities from the APOGEE and LAMOST surveys and an augmented feature space utilizing the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $\pm 0.19\, \mathrm{dex}$. The methodology is applied to stars within the Galactic bar/bulge with particular focus on a sample of 1.69 million objects with Gaia radial velocities. We demonstrate the use and validity of our approach by inspecting both spatial and kinematic gradients with metallicity in the Galactic bar/bulge recovering previous results on the vertical metallicity gradient (−0.528 ± 0.002 dex kpc−1) and the vertex deviation of the bar ($-21.29\pm 2.74\, \mathrm{deg}$).

79 ASTRONOMY AND ASTROPHYSICS↗

PDE-constrained high-order mesh optimization

Here, we present a novel framework for PDE-constrained r-adaptivity of high-order meshes. The proposed method formulates mesh movement as an optimization problem, with an objective function defined as a convex combination of a mesh quality metric and a measure of the accuracy of the PDE solution obtained via finite element discretization. The proposed formulation achieves optimized, well-defined high-order meshes by integrating mesh quality control, PDE solution accuracy, and robust gradient regularization. We adopt the Target-Matrix Optimization Paradigm to control geometric properties across the mesh, independent of the PDE of interest. To incorporate the accuracy of the PDE solution, we introduce error measures that control the finite element discretization error. The implicit dependence of these error measures on the mesh nodal positions is accurately captured by adjoint sensitivity analysis. Additionally, a convolution-based gradient regularization strategy is used to ensure stable and effective adaptation of high-order meshes. We demonstrate that the proposed framework can improve mesh quality and reduce the error by up to 10 times for the solution of Poisson and linear elasto-static problems. The approach is general with respect to the dimensionality, the order of the mesh, the types of mesh elements, and can be applied to any PDE that admits well-defined adjoint operators.

Computer science↗

Chapter 3 - Harvesting Thermal Energy with Ferroelectric Materials

Thermal energy is considered the ubiquitous form of energy as all other forms of energy ultimately degrade to thermal energy. Thermal to electrical energy conversion is currently an important method for electricity generation. The traditional power cycles, however, become technically and economically unviable when heat source temperature reduces below 100 °C. Thermal energy harvesting has been proposed as a viable alternative for low-grade thermal to electrical energy conversion. Thermal energy harvesting is a method of generating electricity using temperature-induced variations in material properties. It is particularly useful when the heat sources are available free-of-cost, either in the form of natural heat or waste heat. While there are several thermal energy harvesting techniques, pyroelectricity is the most promising as it can be used in situations where spatial thermal gradients are difficult to establish or the temperature of the heat source is changing. Despite the fact that the pyroelectric effect in ferroelectric materials has been known for past several decades, thermal energy harvesting using ferroelectric materials has not been much explored in the literature. In this chapter, we would explore various aspects of thermal energy harvesting using ferroelectric materials. We have first established the physics behind ferroelectricity, then provided a detailed description on ferroelectric thermal energy harvesting, discussed various ferroelectric thermodynamic cycles and devices proposed in the literature, and finally described various other emerging applications of ferroelectricity.

DIRECT ENERGY CONVERSION,MATERIALS SCIENCE,OTHER I↗

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗

The JET hybrid scenario in Deuterium, Tritium and Deuterium-Tritium

The JET hybrid scenario has been developed from low plasma current carbon wall discharges to the record-breaking Deuterium-Tritium plasmas obtained in 2021 with the ITER-like Be/W wall. The development started in pure Deuterium with refinement of the plasma current, and toroidal magnetic field choices and succeeded in solving the heat load challenges arising from 37 MW of injected power in the ITER like wall environment, keeping the radiation in the edge and core controlled, avoiding MHD instabilities and reaching high neutron rates. The Deuterium hybrid plasmas have been re-run in Tritium and methods have been found to keep the radiation controlled but not at high fusion performance probably due to time constraints. For the first time this scenario has been run in Deuterium-Tritium (50:50). These plasmas were re-optimised to have a radiation-stable H-mode entry phase, good impurity control through edge T i gradient screening and optimised performance with fusion power exceeding 10 MW for longer than three alpha particle slow down times, 8.3 MW averaged over 5 s and fusion energy of 45.8 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗