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At least 325 records · Page 18

Practical Considerations for Continuum Models Applied to Surface Electrochemistry

We report that modelling the electrolyte at the electrochemical interface remains a major challenge in ab initio simulations of charge transfer processes at surfaces. Recently, the development of hybrid polarizable continuum models/ab initio models have allowed for the treatment of solvation and electrolyte charge in a computationally efficient way. However, challenges remain in its application. Recent literature has reported that large cell heights are required to reach convergence, which presents a serious computational cost. Furthermore, calculations of reaction energetics require costly iterations to tune the surface charge to the desired potential. In this work, we present a simple capacitor model of the interface that illuminates how to circumvent both of these challenges. We derive a correction to the energy for finite cell heights to obtain the large cell energies at no additional computational expense. We furthermore demonstrate that the reaction energetics determined at constant charge are easily mapped to those at constant potential, which eliminates the need to apply iterative schemes to tune the system to a constant potential. Lastly, these developments together represent more than an order of magnitude reduction of the computational overhead required for the application of polarizable continuum models to surface electrochemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High-efficiency perovskite photovoltaic modules achieved via cesium doping

Perovskite solar modules have been attracting increasing attention due to their market potential, yet publications concerned with theintrinsic scale-up potential of different perovskite compositions remain relatively scarce. On the other hand, while great success is being made towards improving the power conversion efficiency (PCE) of perovskite solar cells (PSCs) by cesium cation (Cs + ) doping of the perovskite, more attention is being paid to the perovskite phase stabilization effect of Cs + doping, and less to other properties that are critical to understand and futher improve the PSC's. In this work, moderately-Cs-doped MAPbI 3 was employed as a model perovskite material in order to exclude the phase stabilization effect. Our systematic study revealed the influence of Cs + in organic-inorganic hybrid perovskites on the crystal structure, crystallization process, trap state density, band structure and charge (i.e., ions or photo-carriers) transport. Markedly, it has been observed that Cs + doping can greatly increase the carrier diffusion length in the perovskite films, thus improving the potential to scale-up PSC's.The PCE of small area devices (0.09 cm 2 ) was increased to 21.72% from 19.73%, with decreased hysteresis behavior and increased operational stability (T85 = 1000 h) after Cs + doped, where T85 refers to the retention of 85% of the initial PCE. Moreover, a PCE of 21.08% was obtained for a Cs + -containing perovskite module with an active area > 30 cm 2 , which demonstrates a better "reproducibility" than the reference sample (MAPbI3-based perovskite modules, PCE = 18.26%).

14 SOLAR ENERGY↗

Coupled Electronic and Nuclear Motions during Azobenzene Photoisomerization Monitored by Ultrafast Electron Diffraction

Ultrafast electron diffraction is a powerful technique that can resolve molecular structures with femtosecond and angstrom resolutions. We demonstrate theoretically how it can be used to monitor conical intersection dynamics in molecules. Specific contributions to the signal are identified which vanish in the absence of vibronic coherence and offer a direct window into conical intersection paths. A special focus is on hybrid scattering from nuclei and electrons, a process that is unique to electron (rather than X-ray) diffraction and monitors the strongly coupled nuclear and electronic motions in the vicinity of conical intersections. Furthermore, an application is made to the cis to trans isomerization of azobenzene, computed with exact quantum dynamics wavepacket propagation in a reactive two-dimensional nuclear space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Recovery of p -Hydroxybenzoic Acid from Cu-Catalyzed Alkaline Hydrogen Peroxide Pretreatment of Hybrid Poplar

p-Hydroxybenzoic acid (pHBA) is present in some hardwood biomass feedstocks as an ester attached to the polymeric lignin subunits. Here, we report the isolation and recovery of pHBA from alkaline liquor waste streams obtained from fractionation of hybrid poplar using the “Cu-AHP process”. This process features two stages: alkaline pre-extraction (AP) of the biomass and copper-catalyzed alkaline hydrogen peroxide pretreatment. On a biomass scale of 100 g, 0.5 g of pHBA with a purity of 96% was obtained after decolorization and crystallization. The scalability of the purification process was demonstrated by using liquor obtained from the first-stage AP with 900 g of poplar biomass to afford 3.5 g of crystalline pHBA with 97% purity. Altogether, these results show that pHBA may be isolated and purified from waste streams generated in the two-stage Cu-AHP process without any alteration of the process conditions or impact on carbohydrate and lignin fractionation.

09 BIOMASS FUELS↗

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗

Bright magnetic dipole radiation from two-dimensional lead-halide perovskites

Light-matter interactions in semiconductors are uniformly treated within the electric dipole approximation; multipolar interactions are considered “forbidden.” We experimentally demonstrate that this approximation inadequately describes light emission in two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs), solution processable semiconductors with promising optoelectronic properties. By exploiting the highly oriented crystal structure, we use energy-momentum spectroscopies to demonstrate that an exciton-like sideband in 2D HOIPs exhibits a multipolar radiation pattern with highly directed emission. Electromagnetic and quantum-mechanical analyses indicate that this emission originates from an out-of-plane magnetic dipole transition arising from the 2D character of electronic states. Symmetry arguments and temperature-dependent measurements suggest a dynamic symmetry-breaking mechanism that is active over a broad temperature range. These results challenge the paradigm of electric dipole–dominated light-matter interactions in optoelectronic materials, provide new perspectives on the origins of unexpected sideband emission in HOIPs, and tease the possibility of metamaterial-like scattering phenomena at the quantum-mechanical level.

42 ENGINEERING↗

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗

The Tiny Heater: Creating Heat with Hybrid Nano-Antennas [LDRD HQ Highlights Article]

SRNL scientists demonstrate that an electromagnetic field, either as a light or magnetic field, is selectively coupled to shape-selective hybrid nano-antennas for efficient thermal processes. Localized heating occurs extremely fast, reducing the ‘wasted' thermal load on the environment. Being non-contact, efficient, and highly selective, the required input energy is greatly diminished. By strategically placing nano-antennas at desired locations, heat can be controlled at the nano-level. The location for nano-antennas, and the subsequent energy deposition, may be fine-tuned through specific chemical, steric, or magnetic interactions. The nano-antennas, composed of combinations of plasmonic, magnetic, and hydride components, are used for controlled release of hydrogen isotopes, chemotherapy drugs, environmental contaminants, enhanced catalytic processes, (bio)imaging and therapeutics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Cobalt-Free Cathodes for Next Generation Li-Ion Batteries

In this U.S. Department of Energy sponsored project Nexceris, in collaboration with project partners; The Ohio State University and Navitas Advanced Systems have advanced the technical maturity of a non-cobalt containing cathode for next-generation Li-ion batteries. The cathode is based on the lithium manganese nickel-titanium oxide, LiNi 0.5 Mn 1.5 TiO 4 (LNMTO) high voltage spinel. To address limitations with poor cycle and calendar life an microstucturally hierarchical LNMO/LNMTO core-shell cathode powder has been developed that enables the formation of a solid-electrolyte interface that effectively passivates the cathode surface. The microstructural enhancements of the cathode material focus on preferentially enriching the surface with titanium. In parallel, new, optimized binder and electrolyte chemistries have been incorporated to address degradation mechanisms associated with high-voltage systems. Single-layer pouch cell and large-format 2-Ah cell testing have shown that an optimized LNMO/LNMTO core-shell powder significantly improves initial cell capacity and cycle life compared to homogeneous LNMO powder. To support development and the fabrication of 2-Ah cells a novel Hybrid Alternative Wet-Chemical Synthesis (HAWCS) process have been developed. This low-cost, synthesis approach enables the excellent compositional and particle morphology control achieved with co-precipitation without the strict process controls and associated expensive process equipment.

25 ENERGY STORAGE↗

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗

Fabricating Silver Nanowire–IZO Composite Transparent Conducting Electrodes at Roll-to-Roll Speed for Perovskite Solar Cells

This study addresses the challenges of efficient, large-scale production of flexible transparent conducting electrodes (TCEs). We fabricate TCEs on polyethylene terephthalate (PET) substrates using a high-speed roll-to-roll (R2R) compatible method that combines gravure printing and photonic curing. The hybrid TCEs consist of Ag metal bus lines (Ag MBLs) coated with silver nanowires (AgNWs) and indium zinc oxide (IZO) layers. All materials are solutions deposited at speeds exceeding 10 m/min using gravure printing. We conduct a systematic study to optimize coating parameters and tune solvent composition to achieve a uniform AgNW network. The entire stack undergoes photonic curing, a low-energy annealing method that can be completed at high speeds and will not damage the plastic substrates. The resulting hybrid TCEs exhibit a transmittance of 92% averaged from 400 nm to 1100 nm and a sheet resistance of 11 Ω/sq. Mechanical durability is tested by bending the hybrid TCEs to a strain of 1% for 2000 cycles. The results show a minimal increase (<5%) in resistance. The high-throughput potential is established by showing that each hybrid TCE fabrication step can be completed at 30 m/min. We further fabricate methylammonium lead iodide solar cells to demonstrate the practical use of these TCEs, achieving an average power conversion efficiency (PCE) of 13%. The high-performance hybrid TCEs produced using R2R-compatible processes show potential as a viable choice for replacing vacuum-deposited indium tin oxide films on PET.

14 SOLAR ENERGY↗

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗

Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Upcycling Polyethylene Waste Into Hybrid Graphitic Porous Carbon Materials Used in High-Performance Zinc-Ion Hybrid Capacitors

Polyethylene (PE) waste is a challenge to upcycle into useful materials because this plastic tends to decompose into volatile compounds when heated at relatively low temperatures. In this work, we report a chemical process that addresses this challenge by converting mixtures of linear low-density polyethylene (LLDPE), low-density polyethylene (LDPE), and high-density polyethylene (HDPE) waste into a hybrid graphitic porous carbon (HGPC) that can be used as a zinc-ion hybrid capacitor cathode. The process uses a low temperature thermal oxidation pre-treatment step, with assistance of an inert solid additive (KCl) to increase the effective surface area of the PE melt, to functionalize, cross-link, and stabilize the PE waste followed by carbonization and catalytic graphitization steps at higher temperatures with a potassium carbonate (K2CO3) catalyst. The PE waste derived HPGC (PW-HPGC) has a hybrid structure composed of graphene-like carbon nanosheets grown on the surface of carbon particles, high porosity with the Brunauer–Emmett–Teller (BET) specific surface area up to 1,763 m2g-1, and good graphitic degree with average Raman I2D/IG ratios of 0.53. When used as a cathode material for zinc-ion hybrid capacitors, this PW-HGPC exhibits an excellent specific capacity up to 126.7 mAhg-1 at high mass loading of 10 mgcm-2. Moreover, PW-HGPC exhibits remarkable cycling stability with capacity retention of >94% after 10,000 cycles at a current density of 2.0 A g-1.

hybrid graphitic porous carbon↗

Multimodal fission from self-consistent calculations

When multiple fission modes coexist in a given nucleus, distinct fragment yield distributions appear. Multimodal fission has been observed in a number of fissioning nuclei spanning the nuclear chart, and this phenomenon is expected to affect the nuclear abundances synthesized during the rapid neutron-capture process (𝑟-process). In this study, we generalize the previously proposed hybrid model for fission-fragment yield distributions to predict competing fission modes and estimate the resulting yield distributions. Here, our framework allows for a comprehensive large-scale calculation of fission-fragment yields suited for 𝑟-process nuclear network studies. Nuclear density functional theory is employed to obtain the potential energy and collective inertia tensor on a multidimensional collective space defined by mass multipole moments. Fission pathways and their relative probabilities are determined using the nudged elastic band method. Based on this information, mass and charge fission yields are predicted using the recently developed hybrid model. Fission properties of fermium isotopes are calculated in the axial quadrupole-octupole collective space for three energy density functionals (EDFs). Disagreement between the EDFs appears when multiple fission modes are present. Within our framework, the UNEDF⁢1 HFB EDF agrees best with experimental data. Calculations in the axial quadrupole-octupole-hexadecapole collective space improve the agreement with the experiment for SkM*. We also discuss the sensitivity of fission predictions on the choice of EDF for several superheavy nuclei. Fission-fragment yield predictions for nuclei with multiple fission modes are sensitive to the underlying EDF. For large-scale calculations in which a minimal number of collective coordinates is considered, UNEDF⁢1 HFB provides the best description of experimental data, though the sensitivity motivates robust quantification of the uncertainties of the theoretical model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Ultrafast Carrier Self-Trapping Driven by Strong Exciton–Phonon Coupling in 2D MA 3 Bi 2 I 6 Cl 3 Perovskite

Two-dimensional (2D) hybrid bismuth halide perovskites have emerged as promising lead-free materials for optoelectronic applications due to their solution processability and tunable structures. Here, in this study, we investigate 2D layered hybrid perovskite MA 3 Bi 2 I 6 Cl 3 using temperature-dependent photoluminescence (PL) and femtosecond transient absorption spectroscopy. Our results reveal strong coupling between excitons and phonons, evidenced by giant Huang–Rhys factors, coherent phonon oscillations, and ultrafast carrier self-trapping into small-polaron and self-trapped exciton (STE) states. These processes appear as time-dependent ground-state bleach and photoinduced absorption features, highlighting the influence of the lattice in carrier dynamics. Wavelength- and time-resolved measurements reveal that PL emission is dominated by STEs, while free exciton emission is weak and short-lived. By comparing 2D MA 3 Bi 2 I 6 Cl 3 with 0D MA 3 Bi 2 I 9 , which contains molecularly isolated [BiI 6 ] 3− octahedra and 2D MA 3 Bi 2 Br 9 perovskites, we demonstrate how halide composition and structural dimensionality influence the balance between free exciton populations and carrier localization. These insights uncover the intrinsic kinetic landscape of photoexcited states in MA 3 Bi 2 I 6 Cl 3 . Overall, our study contributes to a mechanistic understanding of exciton–phonon interactions in lead-free 2D perovskites.

Pradeep, Kodimana Ramakrishnan [Northwestern Univ.↗

Predicting Deposition Rate and Closing the Loop on Aerosol Jet Printing with In‐Line Light Scattering Measurements

Aerosol jet printing is a compelling noncontact, digital manufacturing technology for flexible, hybrid, and conformal electronics, but batch‐to‐batch variability and process drift inhibit systematic study and translation to production environments. Emerging measurement techniques for the aerosol volume fraction, a key parameter governing deposition rate, have shown promise for real‐time response, including a recent demonstration of human‐in‐the‐loop control. By integrating improved light scattering measurements with custom software, a robust environment for real‐time monitoring is established and fully automated closed‐loop control over the deposition rate during printing is enabled. The physical basis for scattering measurements as a feedback source is examined and the quantitative insight this provides for ink development and fundamental process studies is highlighted. Three candidate process variables for closed‐loop control, namely the atomizer voltage, carrier gas flow rate, and print speed, are then evaluated. The atomizer voltage demonstrates the best extended duration validity because it addresses atomization variability directly, but carrier gas flow rate and print speed provide faster and more deterministic responses. This methodology is then demonstrated for electrical properties using a silver nanoparticle ink, maintaining 98% of printed patterns within 10% of the mean resistance value over 3 h of printing.

36 MATERIALS SCIENCE↗

Lepidocrocite Titanate–Graphene Composites for Sodium-Ion Batteries

To overcome electronic transport issues of layered titanates in sodium-ion batteries, we have designed and synthesized composites of lepidocrocite titanates with reduced graphene oxide through a solution-based self-assembly approach. The parent lepidocrocite titanate (K 0.8 [Ti 1.73 Li 0.27 ]O 4 ) was exfoliated by a soft-chemical approach and mechanical shaking. Exfoliated layered titania sheets (LTO) were then combined with reduced graphene oxide (rGO) layers to assemble into composites through flocculation. Countercations (i.e., Mg 2+ ) were used for the self-assembly of negatively charged titania and rGO nanosheets via flocculation. The carbon content in the composites was tuned from 1 to 17% by changing the ratio of titania and rGO sheets in the mixed colloidal suspensions. Electrodes were processed with as-prepared LTO-rGO composites without any carbon additives and tested in sodium half-cell configurations. Mg + -coagulated LTO-rGO composite electrodes deliver higher capacities than electrodes prepared with coagulated titania sheets and 10% acetylene black in sodium half-cells and display good capacity retention after 50 cycles. Electrochemical impedance spectroscopy results indicate lower charge transfer resistance for LTO-14.5%rGO composites than that of coagulated titania sheets with 10% acetylene black. A power law analysis of cells containing the composites indicate a hybrid mechanism consisting of both surface and diffusional processes. A comparison with a similar system, that of dopamine-derived LTO-C heterostructures, reveal significant differences. While capacities showed a strong dependence on carbon content for the dopamine-derived materials, this was not true for the LTO-rGO composites. Instead, the highest capacity was obtained for the 14.5% rGO sample, with a lower value obtained for the 17% rGO sample. A greater proportion of the redox processes were surface rather than diffusional in nature for the LTO-rGO composites as well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗