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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Ladder-type extension of a push–pull donor polymer affords enhanced peak external quantum efficiencies in microcavity organic photodetectors, with detectivities ranging from 1.07 × 10 12 to 1.82 × 10 10 Jones in the 900–1400 nm regime.
A simple and effective skeletal randomization strategy is proposed to finely tune solution-state aggregation towards simultaneously improving the solubility and crystallinity of conjugated polymers, leading to a markedly boosted hole mobility.
Pyruvate-derived dialkoxyalkanoates (DAOAs) were synthesized in good yield using a mild protocol. Combustion performance and physical properties of DAOAs compare favorably to other low-carbon diesel fuels.
The novel synthesis of carbon spherogels in organic solvents enables energy and solvent savings and omits supercritical drying. In addition, the resulting carbon spherogels feature excellent capacitance for energy storage in a supercapacitor setup.
This work demonstrates a remarkable room-temperature figure of merit zT of 1.3 for BiSbTe-based composites with excellent reproducibility using a scalable, low-cost ink processing technique.
With porous channels for ion transport, the electronic conductivity of MOFs can be tuned to realize outstanding memory operation and seamlessly integrated into devices emulating complex multi-terminal synaptic responses.
Biopolymers have exhibited potential as sustainable and circular replacements to existing commodity thermoplastic polymers. However, current biopolymers are limited by poor thermomechanical performance compared with their petroleum-derived counterparts. Herein, we report a simple strategy to achieve good mechanical properties in bio-inspired long-chain polyesters via melt copolymerization. By combining mono- and poly-hydroxyl functionalized long chain fatty acids, we show that tough, semi-crystalline materials can be produced that outperform related biopolymers in terms of their thermomechanical behavior. Finally, we envision that long-chain polyesters derived from hydroxylated fatty acids represent an ideal platform to create the next generation of commodity thermoplastics that possess advantaged properties, inherent biodegradability, and feedstock stability.
The first exploration of the impact of Yb 11 Sb 10 and Yb 10 MnSb 9 secondary phases in Yb 14 MnSb 11 , explaining years of confusing thermoelectric data.
The evolution of modern heterogeneous accelerators, such as GPUs, has significantly advanced the landscape of artificial intelligence (AI). There is a notable surge to adopt AI within the nuclear physics domain (AI4NP). While most AI4NP studies focus on feasibility analysis, our attention is directed towards evaluating their performance on contemporary GPUs that integrate tensor cores. We first benchmark the throughput of hyperparameterized multi-layer perceptron (MLP) models. We then examine the performance of an AI4NP application: Hydra. We assess the performance gain and accuracy loss caused by the tensor cores for low-precision floating-point operations. Our experiments encompass the PyTorch and TensorFlow Keras frameworks on NVIDIA’s T4 and A100 GPUs. We explore the behavior of different GPU hardware platforms and AI software tools. This study can be a valuable resource for guiding the performance optimization of larger-scale deployments of AI4NP applications.
Improving the photosynthetic enzyme Rubisco is a key target for enhancing C3 crop productivity, but progress has been hampered by the difficulty of evaluating engineered variants in planta without interference from the native enzyme. Here, we report the creation of a Rubisco-null Nicotiana tabacum platform by using CRISPR-Cas9 to knock out all 11 nuclear-encoded small subunit (rbcS) genes. Knockout was achieved in a line expressing cyanobacterial Rubisco from the plastid genome, allowing the recovery of viable plants. We then developed a chloroplast expression system for coexpressing both large and small subunits from the plastid genome. We expressed two resurrected ancestral Rubiscos from the Solanaceae family. The resulting transgenic plants were phenotypically normal and accumulated Rubisco to wild-type levels. Importantly, kinetic analyses of the purified ancestral enzymes revealed they possessed a 16 to 20% higher catalytic efficiency (k cat,air /K c,air ) under ambient conditions, driven by a significantly faster turnover rate (k cat,air ). We have demonstrated that our system allows robust in vivo assessment of novel Rubiscos and that ancestral reconstruction is a powerful strategy for identifying superior enzymes to improve photosynthesis in C3 crops.
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A significant improvement of plasma parameters in the optimized stellarator W7-X is found after injections of frozen hydrogen pellets. The ion temperature in the post-pellet phase exceeds 3 keV with 5 MW of electron heating and the global energy confinement time surpasses the empirical ISS04-scaling. The plasma parameters realized in such experiments are significantly above those in comparable gas-fuelled discharges. Here, we present details of these pellet experiments and discuss the main plasma properties during the enhanced confinement phases. Local power balance is applied to show that the heat transport in post-pellet phases is close to the neoclassical level for the ion channel and is about a factor of two above that level for the combined losses. In comparable gas-fuelled discharges, the heat transport is by about ten times larger than the neoclassical level, and thus is largely anomalous. It is further observed that the improvement in the transport is related to the peaked density profiles that lead to a stabilization of the ion-scale turbulence.
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We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (≥70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.
Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors, and energy conversion devices. Computational modeling can accurately predict material properties but, in order to be reliable, requires accurate descriptions of electron and phonon states and their interactions. While first-principles methods are capable of describing the energy spectrum of each carrier, using them to compute transport properties is still a formidable task, both computationally demanding and memory intensive, requiring integration of fine microscopic scattering details for estimation of macroscopic transport properties. To address this challenge, we present Phoebe—a newly developed software package that includes the effects of electron–phonon, phonon–phonon, boundary, and isotope scattering in computations of electrical and thermal transport properties of materials with a variety of available methods and approximations. This open source C++ code combines MPI-OpenMP hybrid parallelization with GPU acceleration and distributed memory structures to manage computational cost, allowing Phoebe to effectively take advantage of contemporary computing infrastructures. We demonstrate that Phoebe accurately and efficiently predicts a wide range of transport properties, opening avenues for accelerated computational analysis of complex crystals.
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