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At least 739 records · Page 41

Fast baryonic field painting for Sunyaev-Zel’dovich analyses: Transfer function vs hybrid effective field theory

Here, we present two approaches for “painting” baryonic properties relevant to the Sunyaev-Zel’dovich (SZ) effect—optical depth and Compton-y—onto three-dimensional N-body simulations, using the MillenniumTNG suite as a benchmark. The goal of these methods is to produce fast and accurate reconstruction methods to aid future analyses of baryonic feedback using the SZ effect. The first approach employs a Gaussian process emulator to model the SZ quantities via a transfer function, while the second utilizes hybrid effective field theory (HEFT) to reproduce these quantities within the simulation. Our analysis involves comparing both methods to the true MillenniumTNG optical depth and Compton-y fields using several metrics, including the cross-correlation coefficient, power spectrum, and power spectrum error. Additionally, we assess how well the reconstructed fields correlate with dark matter haloes across various mass thresholds. The results indicate that the transfer function method yields more accurate reconstructions for fields with initially high correlations (r ≈ 1), such as between the optical depth and dark matter fields. Conversely, the HEFT-based approach proves more effective in enhancing correlations for fields with weaker initial correlations (r ∼ 0.5), such as between the Compton-y and dark matter fields. Lastly, we discuss extensions of our methods to improve the reconstruction performance at the field level.

Liu, R. Henry [University of California, Berkeley,↗

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Recent advances of two-dimensional material additives in hybrid perovskite solar cells

Perovskite solar cells (PSCs) have become one of the state-of-the-art photovoltaic technologies due to their facile solution-based fabrication processes combined with extremely high photovoltaic performance originating from excellent optoelectronic properties such as strong light absorption, high charge mobility, long free charge carrier diffusion length, and tunable direct bandgap. However, the poor intrinsic stability of hybrid perovskites under environmental stresses including light, heat, and moisture, which is often associated with high defect density in the perovskite, has limited the large-scale commercialization and deployment of PSCs. The use of process additives, which can be included in various subcomponent layers in the PSC, has been identified as one of the effective approaches that can address these issues and improve the photovoltaic performance. Among various additives that have been explored, two-dimensional (2D) materials have emerged recently due to their unique structures and properties that can enhance the photovoltaic performance and device stability by improving perovskite crystallization, defect passivation, and charge transport. In this work, we provide a review of the recent progresses in 2D material additives for improving the PSC performance based on key representative 2D material systems, including graphene and its derivatives, transitional metal dichalcogenides, and black phosphorous, providing a useful guideline for further exploiting unique nanomaterial additives for more efficient and stable PSCs in the near future.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Hybrid printing of wearable piezoelectric sensors

Piezoelectricity provides an ideal electromechanical mechanism with emerging applications in wearable devices due to its simplicity and self-powered nature. However, the 3D printing of piezoelectric devices still faces many challenges, including material printability, high energy poling process, and low dimensional accuracy. This study demonstrates, for the first time, a tellurium nanowire-based piezoelectric device fabricated by a hybrid printing method integrating highly complementary aerosol jet printing and extrusion printing in a single printing platform. The aerosol-jet-printed tellurium nanowire demonstrates piezoelectric properties without the need for any poling processing due to the unique properties of the tellurium nanowires. The silver nanowire electrodes printed by aerosol jet printing demonstrate excellent conductivity and stretchability without the need for sintering. An extrusion method is employed to print the silicone films, which serve as the stretchable substrate and the electrical insulation layers between the printed tellurium and silver. The printed wearable piezoelectric devices were attached to a human wrist to detect different hand gestures and to a human neck to detect heartbeat without using an external power source. In conclusion, the fully printed, sintering-free and poling-free, and stretchable piezoelectric device opens enormous opportunities for facile integration with a broad range of printed electronics and wearable devices.

47 OTHER INSTRUMENTATION↗

Molecular Layer Deposited Aluminum‐Based Hybrid Resist for High‐Resolution Nanolithography and Direct Ultra‐High Aspect Ratio Pattern Transfer

Inorganic-containing hybrid photoresists are critical for next-generation extreme ultraviolet (EUV) lithography and angstrom-era semiconductor miniaturization. However, associated conventional solution processing struggles to achieve ultrathin, uniform films with high conformality and compositional control, limiting overall patterning performance. Here, this study reports the systematic lithographic patterning characterization of an Al-based hybrid resist synthesized via vapor-phase molecular layer deposition (MLD), using the trimethylaluminum (TMA) metal precursor and the hydroquinone (HQ) aromatic organic linker. The resist supports both sub-20 nm high-resolution nanolithography and virtually infinite silicon plasma etch selectivity. Lithographic performance studied using electron beam lithography (EBL) as a proxy for EUV shows that, under an optimized process adopting post-exposure bake, the resist achieves the best resolution of 15.4 nm linewidth under stringent 1:1 line-space high-density patterning—limited only by the minimum beam size of the EBL system. The resist also shows wide dose latitude and balanced performance in resolution, roughness, and sensitivity. The infinite silicon etch selectivity, stemming from spontaneous aluminum oxyfluoride passivation layer formation, enables fabrication of micrometer-tall, 40 nm-wide silicon nanofin structures using only a ≈30 nm-thick resist layer, without no hard mask. These results highlight the potential of MLD-based hybrid resists for developing next-generation resist materials for micro/nanoelectronics manufacturing.

36 MATERIALS SCIENCE↗

Aircraft System Noise Prediction Uncertainty Quantification for a Hybrid Wing Body Subsonic Transport Concept

Aircraft system level noise prediction for advanced, unconventional concepts has undergone significant improvement over the past two decades. The prediction modeling uncertainty must be quantified so that potential benefits of unconventional configurations, which are outside of the range of empirical models, can be reliably assessed. This paper builds on previous work in an effort to improve estimates of element prediction uncertainties where the prediction methodology has been improved, or new experimental validation data are available, to provide an estimate of the system level uncertainty in the prediction process. In general, the uncertainty of the prediction will be strongly dependent on the aircraft configuration as well as which technologies are integrated. While the quantitative uncertainty values contained here are specific to the hybrid wing body design presented, the underlying process is the same regardless of configuration. A refined process for determining the uncertainty for each element of the noise prediction is detailed in this paper. The system level uncertainty in the prediction of the aircraft noise is determined at the three certification points, using a Monte Carlo method. Comparisons with previous work show a reduction of 1 EPNdB in the 95%coverage interval of the cumulative noise level. The largest impediment for continued reduction in uncertainty for the hybrid wing body concept is the need for improved modeling and validation experiments for fan noise, propulsion airframe aeroacoustic effects, and the Krueger flap, which comprise the bulk of the uncertainty in the cumulative certification noise level.

June, Jason C.↗

STAR - A computer language for hybrid AI applications

Constructing Artificial Intelligence application systems which rely on both symbolic and non-symbolic processing places heavy demands on the communication of data between dissimilar languages. This paper describes STAR (Simple Tool for Automated Reasoning), a computer language for the development of AI application systems which supports the transfer of data structures between a symbolic level and a non-symbolic level defined in languages such as FORTRAN, C and PASCAL. The organization of STAR is presented, followed by the description of an application involving STAR in the interpretation of airborne imaging spectrometer data.

Borchardt, G. C.↗

Rolling Optimization of Transmission Network Recovery and Load Restoration Considering Hybrid Wind-Storage System and Cold Load Pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

chance-constrained optimization↗

Improved fabrication of scalable hybrid silicon nitride–thin-film lithium niobate electro-optic modulators

Strategies for improved fabrication of integrated thin-film lithium niobate electro-optic (EO) Mach–Zehnder modulators (MZMs) are reported using scalable processes and designs. The MZM devices utilize direct bonding of unetched and unpatterned thin-film lithium niobate to patterned and planarized silicon photonic microchips. The latter contains silicon nitride waveguide structures of various widths that are used to form hybrid modes that are suitable for high-bandwidth low-voltage EO modulators based on Pockels effect. We report that the incorporation of appropriately designed outgassing channels and certain modifications to key processing steps helped achieve a greater than 99% reduction in void density during bonding. Void reduction is critically important for these traveling-wave hybrid MZM devices in which the optical mode is controllably distributed between multiple thin layers and propagates over millimeter-scale lengths.

Mere, Viphretuo↗

Reflective liquid crystal light valve with hybrid field effect mode

There is disclosed a high performance reflective mode liquid crystal light valve suitable for general image processing and projection and particularly suited for application to real-time coherent optical data processing. A preferred example of the device uses a CdS photoconductor, a CdTe light absorbing layer, a dielectric mirror, and a liquid crystal layer sandwiched between indium-tin-oxide transparent electrodes deposited on optical quality glass flats. The non-coherent light image is directed onto the photoconductor; this reduces the impedance of the photoconductor, thereby switching the AC voltage that is impressed across the electrodes onto the liquid crystal to activate the device. The liquid crystal is operated in a hybrid field effect mode. It utilizes the twisted nematic effect to create a dark off-state (voltage off the liquid crystal) and the optical birefringence effect to create the bright on-state. The liquid crystal thus modulates the polarization of the coherent read-out or projection light responsively to the non-coherent image. An analyzer is used to create an intensity modulated output beam.

Boswell, Donald D.↗

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗

Enabling 6C Fast Charging of Li–Ion Batteries with Graphite/Hard Carbon Hybrid Anodes

Li-ion batteries that can simultaneously achieve high-energy density and fast charging are essential for electric vehicles. Graphite anodes enable a high-energy density, but suffer from an inhomogeneous reaction current and irreversible Li plating during fast charging. In contrast, hard carbon exhibits superior rate performance but lower energy density owing to its lower initial coulombic efficiency and higher average voltage. In this work, these tradeoffs are overcome by fabricating hybrid anodes with uniform mixtures of graphite and hard carbon, using industrially-relevant multi-layer pouch cells (>1 Ah) and electrode loadings (3 mAh cm –2 ). By controlling the graphite/hard carbon ratio, this study shows that battery performance can be systematically tuned to achieve both high-energy density and efficient fast charging. Pouch cells with optimized hybrid anodes retain 87% and 82% of their initial specific energy after 500 cycles of 4C and 6C fast-charge cycling, respectively. This is significantly higher than the 61% and 48% specific energy retention with graphite anodes under the same conditions. The enhanced performance is attributed to improved homogeneity of the reaction current throughout the hybrid anode, which is supported by continuum-scale modeling. Furthermore, this process is directly compatible with existing roll-to-roll battery manufacturing, representing a scalable pathway to fast charging.

25 ENERGY STORAGE↗

Repurposing Post-Consumer Polyethylene to Access Cross-Linked Polyethylene with Reprocessabilty, Recyclability, and Tunable Properties

Polyethylene (PE) is the most widely produced plastic but accumulation and resistance to degradation has significantly contributed to the plastic waste crisis. Upcycling has presented promising solutions to transform PE waste into value-added products. In this study, mixed post-consumer PE was successfully repurposed into reprocessable and chemically recyclable cross-linked polyethylene (XLPE). This process involved converting PE into telechelic oligomers, followed by repolymerization using a hybrid cross-linking system consisting of a dynamic cross-linker 2,4,6-triethoxy-1,3,5-triazine (TETA) and non-dynamic cross-linker tris(6-isocyanatohexyl)isocyanurate (Tri-HDI). In the resulting XLPE, TETA facilitated iterative reprocessing with minimal property degradation across cycles, whereas Tri-HDI helped preserve functional performance throughout service life. Compared to PE, XLPE exhibited enhanced mechanical properties, reduced creep deformation under application-relevant temperatures, and high temperature structural stability. Notably, copolymerizing PE oligomers with commercial macrodiols was employed to create composite XLPEs, enabling tuning material properties. After use, XLPE can be efficiently and selectively depolymerized under mild conditions, even when mixed with commercial insulator cables. This depolymerization allows for the recovery of the constituent building blocks, enabling purification and subsequent repolymerization for reuse. This approach demonstrates the potential of repurposing plastic waste into sustainable materials and fostering the development of a circular economy.

INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMI↗

Electrochemical Lignin Conversion

Lignin is the largest source of renewable aromatic compounds, making the recovery of aromatic compounds from this material a significant scientific goal. Recently, many studies have reported on lignin depolymerization and upgrading strategies. Electrochemical approaches are considered to be low cost, reagent free, and environmentally friendly, and can be carried out under mild reaction conditions. Here, different electrochemical lignin conversion strategies, including electrooxidation, electroreduction, hybrid electro-oxidation and reduction, and combinations of electrochemical and other processes (e.g., biological, solar) for lignin depolymerization and upgrading are discussed in detail. In addition to lignin conversion, electrochemical lignin fractionation from biomass and black liquor is also briefly discussed. Finally, the outlook and challenges for electrochemical lignin conversion are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Accelerated impurity solver for DMFT and its diagrammatic extensions

Here, we present ComCTQMC, a GPU accelerated quantum impurity solver. It uses the continuous-time quantum Monte Carlo (CTQMC) algorithm wherein the partition function is expanded in terms of the hybridisation function (CT-HYB). ComCTQMC supports both partition and worm-space measurements, and it uses improved estimators and the reduced density matrix to improve observable measurements whenever possible. ComCTQMC efficiently measures all one and two-particle Green's functions, all static observables which commute with the local Hamiltonian, and the occupation of each impurity orbital. ComCTQMC can solve complex-valued impurities with crystal fields that are hybridized to both fermionic and bosonic baths. Most importantly, ComCTQMC utilizes graphical processing units (GPUs), if available, to dramatically accelerate the CTQMC algorithm when the Hilbert space is sufficiently large. We demonstrate acceleration by a factor of over 600 (100) in a simulation of δ-Pu at 600 K with (without) crystal fields. In easier problems, the GPU offers less impressive acceleration or even decelerates the CTQMC. Here we describe the theory, algorithms, and structure used by ComCTQMC in order to achieve this set of features and level of acceleration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗

Digital coded exposure formation of frames from event-based imagery

Abstract Event-driven neuromorphic imagers have a number of attractive properties including low-power consumption, high dynamic range, the ability to detect fast events, low memory consumption and low band-width requirements. One of the biggest challenges with using event-driven imagery is that the field of event data processing is still embryonic. In contrast, decades worth of effort have been invested in the analysis of frame-based imagery. Hybrid approaches for applying established frame-based analysis techniques to event-driven imagery have been studied since event-driven imagers came into existence. However, the process for forming frames from event-driven imagery has not been studied in detail. This work presents a principled digital coded exposure approach for forming frames from event-driven imagery that is inspired by the physics exploited in a conventional camera featuring a shutter. The technique described in this work provides a fundamental tool for understanding the temporal information content that contributes to the formation of a frame from event-driven imagery data. Event-driven imagery allows for the application of arbitrary virtual digital shutter functions to form the final frame on a pixel-by-pixel basis. The proposed approach allows for the careful control of the spatio-temporal information that is captured in the frame. Furthermore, unlike a conventional physical camera, event-driven imagery can be formed into any variety of possible frames in post-processing after the data is captured. Furthermore, unlike a conventional physical camera, coded-exposure virtual shutter functions can assume arbitrary values including positive, negative, real, and complex values. The coded exposure approach also enables the ability to perform applications of industrial interest such as digital stroboscopy without any additional hardware. The ability to form frames from event-driven imagery in a principled manner opens up new possibilities in the ability to use conventional frame-based image processing techniques on event-driven imagery.

47 OTHER INSTRUMENTATION↗

Microwave-Free Dynamic Nuclear Polarization via Sudden Thermal Jumps

Dynamic nuclear polarization (DNP) presently stands as the preferred strategy to enhance the sensitivity of nuclear magnetic resonance measurements, but its application relies on the use of high-frequency microwave to manipulate electron spins, an increasingly demanding task as the applied magnetic field grows. Here we investigate the dynamics of a system hosting a polarizing agent formed by two distinct paramagnetic centers near a level anticrossing. We theoretically show that nuclear spins polarize efficiently under a cyclic protocol that combines alternating thermal jumps and radio-frequency pulses connecting hybrid states with opposite nuclear and electronic spin alignment. Central to this process is the difference between the spin-lattice relaxation times of either electron spin species, transiently driving the electronic spin bath out of equilibrium after each thermal jump. Without the need for microwave excitation, this route to enhanced nuclear polarization may prove convenient, particularly if the polarizing agent is designed to feature electronic level anticrossings at high magnetic fields.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗