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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effective Field Theory Analysis of CDMSlite Run 2 Data

CDMSlite Run 2 was a search for weakly interacting massive particles (WIMPs) with a cryogenic 600 g Ge detector operated in a high-voltage mode to optimize sensitivity to WIMPs of relatively low mass from 2 - 20 GeV/$c^2$. In this article, we present an effective field theory (EFT) analysis of the CDMSlite Run 2 data using an extended energy range and a comprehensive treatment of the expected background. A binned likelihood Bayesian analysis was performed on the recoil energy data, taking into account the parameters of the EFT interactions and optimizing the data selection with respect to the dominant background components. Energy regions within 5$\sigma$ of known activation peaks were removed from the analysis. The Bayesian evidences resulting from the different operator hypotheses show that the CDMSlite Run 2 data are consistent with the background-only models and do not allow for a signal interpretation assuming any additional EFT interaction. Consequently, upper limits on the WIMP mass and coupling-coefficient amplitudes and phases are presented for each EFT operator. These limits improve previous CDMSlite Run 2 bounds for WIMP masses above 5 GeV/$c^2$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The Pseudoenzyme β‐Amylase9 From Arabidopsis Activates α‐Amylase3: A Possible Mechanism to Promote Stress‐Induced Starch Degradation

ABSTRACT Starch accumulation in plants provides carbon for nighttime use, for regrowth after periods of dormancy, and for times of stress. Both ɑ‐ and β‐amylases (AMYs and BAMs, respectively) catalyze starch hydrolysis, but their functional roles are unclear. Moreover, the presence of catalytically inactive amylases that show starch excess phenotypes when deleted presents questions on how starch degradation is regulated. Plants lacking one of these catalytically inactive β‐amylases, BAM9, have enhanced starch accumulation when combined with mutations in BAM1 and BAM3, the primary starch degrading BAMs in response to stress and at night, respectively. BAM9 has been reported to be transcriptionally induced by stress although the mechanism for BAM9 function is unclear. From yeast two‐hybrid experiments, we identified the plastid‐localized AMY3 as a potential interaction partner for BAM9. We found that BAM9 interacted with AMY3 in vitro and that BAM9 enhances AMY3 activity about three‐fold. Modeling of the AMY3‐BAM9 complex predicted a previously undescribed alpha–alpha hairpin in AMY3 that could serve as a potential interaction site. Additionally, AMY3 lacking the alpha–alpha hairpin is unaffected by BAM9. Structural analysis of AMY3 showed that it can form a homodimer in solution and that BAM9 appears to replace one of the AMY3 monomers to form a heterodimer. The presence of both BAM9 and AMY3 in many vascular plant lineages, along with model‐based evidence that they heterodimerize, suggests that the interaction is conserved. Collectively these data suggest that BAM9 is a pseudoamylase that activates AMY3 in response to cellular stress, possibly facilitating stress recovery.

Biochemistry & Molecular Biology↗
DOE OSTI2020

A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. Here, the results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗
DOE OSTI2025

The Short-Baseline Near Detector at Fermilab: Input to the European Strategy for Particle Physics 2026 Update

SBND is a 112 ton liquid argon time projection chamber (LArTPC) neutrino detector located 110 meters from the Booster Neutrino Beam (BNB) target at Fermilab. Its main goals include searches for eV-scale sterile neutrinos as part of the Short-Baseline Neutrino (SBN) program, other searches for physics beyond the Standard Model, and precision studies of neutrino-argon interactions. In addition, SBND is providing a platform for LArTPC neutrino detector technology development and is an excellent training ground for the international group of scientists and engineers working towards the upcoming flagship Deep Underground Neutrino Experiment (DUNE). SBND began operation in July 2024, and started collecting stable neutrino beam data in December 2024 with an unprecedented rate of ~7,000 neutrino events per day. During its currently approved operation plans (2024-2027), SBND is expected to accumulate nearly 10 million neutrino interactions. The near detector dataset will be instrumental in testing the sterile neutrino hypothesis with unprecedented sensitivity in SBN and in probing signals of beyond the Standard Model physics. It will also be used to significantly advance our understanding of the physics of neutrino-argon interactions ahead of DUNE. After the planned accelerator restart at Fermilab (2029+), opportunities are being explored to operate SBND in antineutrino mode in order to address the scarcity of antineutrino-argon scattering data, or in a dedicated beam-dump mode to significantly enhance sensitivity to searches for new physics. SBND is an international effort, with approximately 40% of institutions from Europe, contributing to detector construction, commissioning, software development, and data analysis. Continued European involvement and leadership are essential during SBND's operations and analysis phase for both the success of SBND, SBN and its role leading up to DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

First-principles DFT modeling of nitrobenzene adsorption on the Ag(111) surface at varying monolayer coverages

Here, the adsorption behavior of nitrobenzene on the Ag(111) surface as a function of coverage is investigated using density functional theory. Adsorption energies and optimized geometries are analyzed together with isolated intermolecular interaction calculations and electronic structure analysis, including Bader charge partitioning and projected density of states, to disentangle the roles of adsorbate–surface bonding and through-space adsorbate–adsorbate interactions. At low and intermediate coverages (θ = 1/9 and θ = 2/9), bidentate adsorption at top sites is favored due to strong adsorbate–surface interactions, with additional stabilization at θ = 2/9 arising from favorable intermolecular separations. At higher coverage (θ = 1/3), bidentate adsorption is destabilized by strong intermolecular repulsion, and monodentate adsorption becomes energetically preferred as rotational freedom allows more favorable intermolecular spacing. Charge density differences, Bader charge, and density of states analyses show that charge transfer from the Ag surface is localized primarily on the nitro group and increases with coverage and adsorption denticity, although this increase does not directly correlate with adsorption strength at high coverage due to competing intermolecular interactions. These results demonstrate that surface coverage can induce a transition in preferred adsorption denticity driven by intermolecular interactions, highlighting the importance of adsorbate packing in organic molecule adsorption at metal interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Gut microbiota and its therapeutic implications in tumor microenvironment interactions

The development of cancer is not just the growth and proliferation of a single transformed cell, but its tumor microenvironment (TME) also coevolves with it, which is primarily involved in tumor initiation, development, metastasis, and therapeutic responses. Recent years, TME has been emerged as a potential target for cancer diagnosis and treatment. However, the clinical efficacy of treatments targeting the TME, especially its specific components, remains insufficient. In parallel, the gut microbiome is an essential TME component that is crucial in cancer immunotherapy. Thus, assessing and constructing frameworks between the gut microbiota and the TME can significantly enhance the exploration of effective treatment strategies for various tumors. In this review the role of the gut microbiota in human cancers, including its function and relationship with various tumors was summarized. In addition, the interaction between the gut microbiota and the TME as well as its potential applications in cancer therapeutics was described. Furthermore, it was summarized that fecal microbiota transplantation, dietary adjustments, and synthetic biology to introduce gut microbiota-based medical technologies for cancer treatment. This review provides a comprehensive summary for uncovering the mechanism underlying the effects of the gut microbiota on the TME and lays a foundation for the development of personalized medicine in further studies.

Feng, Pengya↗
DOE OSTI2024

Simulating dirty bosons on a quantum computer

Abstract Quantum computers hold the potential to unlock new discoveries in complex quantum systems by enabling the simulation of physical systems that have heretofore been impossible to implement on classical computers due to intractability. A system of particular interest is that of dirty bosons, whose physics highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one and two dimensions. Specifically, we explore the disorder-induced delocalized-to-localized transition using adiabatic state preparation. In one dimension, the quantum circuits can be compressed to small enough depths for execution on currently available quantum computers. In two dimensions, the compression scheme is no longer applicable, thereby requiring the use of large-scale classical state vector simulations to emulate quantum computer performance. In addition, simulating interacting bosons via emulation of a noisy quantum computer allowed us to study the effect of quantum hardware noise on the physical properties of the simulated system. Our results suggest that scaling laws control how noise modifies observables versus its strength, the circuit depth, and the number of qubits. Moreover, we observe that noise impacts the delocalized and localized phases differently. A better understanding of how noise alters the observed properties of the simulated system is essential for leveraging near-term quantum devices for simulation of dirty bosons, and indeed for condensed matter systems in general.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Upregulation of a novel LncRNA AC104958.2 stabilized by PCBP2 promotes proliferation and microvascular invasion in hepatocellular carcinoma

Highlights: • AC104958.2 is a novel lncRNA identified to be upregulated in HCC tissues and associated with MVI. • Knockout of AC104958.2 inhibited cell proliferation, invasion, migration and EMT. • AC104958.2 interacted with PCBP2. • PCBP2 was upregulated in HCC and enhanced the RNA stability of AC104958.2. Long non-coding RNAs (lncRNAs) were reported to be involved in tumorigenesis and progression of hepatocellular carcinoma (HCC). Microvascular invasion (MVI) is an independent predictor for early recurrence and overall survival in postoperative patients with HCC. However, the mechanisms how lncRNAs affect HCC and MVI remain elusive. By RNA sequencing (RNA-seq) in a series of 65 HCC samples and 30 paired adjacent non-tumor liver tissue, we identified a novel lncRNA AC104958.2 that was significantly upregulated in HCC tissues and associated with MVI. Overexpression of AC104958.2 obviously elevated cell viability, metastasis, invasion and epithelial-mesenchymal transition (EMT), while knockout of AC104958.2 mediated by CRISPR/Cas9 technique showed the opposite effects. In addition, the interaction between AC104958.2 and Poly (rC) binding protein 2 (PCBP2) was identified by RNA pull down and mass spectrometry (MS), which was further validated by RNA immunoprecipitation (RIP). PCBP2 was also upregulated in HCC and associated with MVI. High expression of both AC104958.2 and PCBP2 was correlated with tumor size, TNM stage and MVI in HCC. Overexpression of PCBP2 greatly increased the cell viability, metastasis, invasion and EMT. Moreover, actinomycin D assay showed that overexpression of PCBP2 enhanced the RNA stability of AC104958.2. In conclusion, our study showed that a novel lncRNA AC104958.2 exerted oncogenic roles in HCC and might be a promising biomarker and therapeutic target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Visualizing the Effect of Oxidation on Magnetic Domain Behavior of Nanoscale Fe 3 GeTe 2 for Applications in Spintronics

Magnetic van der Waals (vdW) materials offer an opportunity to design heterostructures that will lead to exotic functionalities that arise from interfacial interaction. In addition to coupling to different vdW materials, the naturally oxidized surface layer of a vdW material also forms a heterostructure with its bulk film, giving rise to intriguing phenomena. Here, we directly observe the impact of oxidation on the magnetic domains, namely, magnetic stripe domain and skyrmions, in a nanoscale Fe 3 GeTe 2 flake using cryo Lorentz transmission electron microscopy. After the Fe 3 GeTe 2 is exposed to ambient conditions, partial oxidation leads to an increase in the density of skyrmions even under zero magnetic field. Complete oxidation leads to a loss of the magnetic domain structure. We observe a gradual change in Fe 3 GeTe 2 from single crystal to amorphous as the oxidation increases. The oxidized Fe 3 GeTe 2 primarily consists of iron oxide, which could be antiferromagnetic in nature. We hypothesize that the interfacial interaction between these surface antiferromagnetic oxides and the bulk ferromagnetic Fe 3 GeTe 2 , as well as the effect of interfacial roughness, leads to the increase in Néel skyrmion creation. Furthermore, this work opens a path to harness controlled oxidation as a build block to create dense skyrmion lattices without the need for an external magnetic field, leading to potential future applications in spintronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The quest for efficient oxygenated fuels: Examining interactions between lubricant components and oxygenates

This work empirically evaluated oxygenated blendstock-lubricant compatibility using a novel but simple method. The reactivity or compatibility was evaluated via gas chromatograph equipped with a flame ionization detector (GC-FID) before and after heating for 2h at 150 °C, which would roughly mimic conditions encountered in the combustion chamber of an Spark Ignition (SI) or Compression-Ignition (CI) engine. Five oxygenate blendstocks spanning several functional groups and chain lengths (prenol, isoprenol, butyl acetate, isohexanol, polyoxymethylene dimethyl ethers) were evaluated in a base fuel with nine variations of lubricant components of controlled concentrations, including fully formulated commercial lubricants. Baseline comparative examples were also generated, to decouple the effect of the oxygenate on the mixture, by heating only the base fuel with the lubricant component. Results demonstrate the relative compatibility between noted lubricants and oxygenates in base fuel upon heating. This was illustrated by minimal to no changes in the GC-FID traces. However, reactions of several of the chosen lubricants, especially Molybdenum di (2-ethylhexyl) phosphorodithioate (Molyvan L) and zinc dialkyldithiophosphate (ZDDP), with prenol and polyoxymethylene dimethyl ethers blendstocks showed substantial side reactions compared to corresponding baseline examples. Control experiments without oxygenates but comprising the same lubricant components showed no change in the mixture with heating. We concluded the incompatibility is due to the oxygenate and not the components of the base fuel. This preliminary work is valuable in establishing trends as well as provide useful information when a new component is introduced in the fuel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Mechanisms of Metal Additive-Induced Ordering During SNIPS Membrane Formation

Isoporous membranes can be fabricated by combining self-assembly with nonsolvent induced phase separation (SNIPS) using an amphiphilic block copolymer like polystyrene-b-poly(4-vinylpyridine) (SV). Poly(4-vinylpyridine) (V) is known to complex with metal salts, which are hypothesized to stabilize solution ordering and preserve structure during casting. We explored how the molar ratio of metal additive to the poly(4-vinylpyridine) block affected the final membrane morphology via scanning electron microscopy (SEM). Dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), and in situ grazing-incidence SAXS were used to track changes in solution ordering and chain conformation as a function of the molar ratio of the additive to the V block. Additives induced aggregation, promoted the formation of more compact conformations in solution, and facilitated micelle ordering onto lattices at optimal ratios. Furthermore, these experimental results were supported by random phase approximation calculations, which helped explain how the thermodynamic order–disorder transition shifts with additive binding strength. Stronger additive–polymer interactions reduced the block copolymer volume fraction required for ordering in solution, allowing ordered domains to form at lower polymer concentrations.

Additives↗
DOE OSTI2023

The Riddle of Dark LLZO: Cobalt Diffusion in Garnet Separators of Solid‐State Lithium Batteries

Abstract Solid‐state batteries (SSBs) with a Li 7 La 3 Zr 2 O 12 (LLZO) garnet electrolyte are attracting much attention as robust and safe alternative to conventional lithium‐ion batteries. Technical challenges in the practical implementation of garnet SSBs are related to the need for high‐temperature sintering, which often leads to undesirable chemical reactions with the cathode material. While these reactions are well understood for composite cathodes, very little is known about similar processes between cathode and separator during battery fabrication. This work focuses on understanding the processes between the composite LiCoO 2 ‐LLZO cathode and the LLZO separator and how they affect the battery performance. The extensive diffusion of Co‐ions within LLZO, which leads to the often‐observed LLZO darkening, is shown to have a significant impact on ionic conductivity, electronic conductivity, and dendrite stability of the separator. Experimental data coupled with large‐scale molecular dynamics simulations uncover the diffusion mechanism for Co‐ions and identify secondary phases that form during these interactions. In addition to extensive Co‐ion diffusion within the grains, a non‐uniform segregation of Co‐ions at grain boundaries is found leading to the formation of three distinct Co‐containing phases. This work offers a general approach to studying the fundamental ion diffusion processes that occur during the fabrication of oxide SSBs.

electrochemical properties↗
DOE OSTI2024

Room‐Temperature Formate Ester Transfer Hydrogenation Enables an Electrochemical/Thermal Organometallic Cascade for Methanol Synthesis from CO 2

Abstract The reduction of CO 2 to synthetic fuels is a valuable strategy for energy storage. However, the formation of energy‐dense liquid fuels such as methanol remains rare, particularly under low‐temperature and low‐pressure conditions that can be coupled to renewable electricity sources via electrochemistry. Here, a multicatalyst system pairing an electrocatalyst with a thermal organometallic catalyst is introduced, which enables the reduction of CO 2 to methanol at ambient temperature and pressure. The cascade methanol synthesis proceeds via CO 2 reduction to formate by electrocatalyst [Cp*Ir(bpy)Cl] + (Cp*=pentamethylcyclopentadienyl, bpy=2,2′‐bipyridine), Fischer esterification of formate to isopropyl formate catalyzed by trifluoromethanesulfonic acid (HOTf), and thermal transfer hydrogenation of isopropyl formate to methanol facilitated by the organometallic catalyst (H‐PNP)Ir(H) 3 (H‐PNP=HN(C 2 H 4 P i Pr 2 ) 2 ). The isopropanol solvent plays several crucial roles: activating formate ion as isopropyl formate, donating hydrogen for the reduction of formate ester to methanol via transfer hydrogenation, and lowering the barrier for transfer hydrogenation through hydrogen bonding interactions. In addition to reporting a method for room‐temperature reduction of challenging ester substrates, this work provides a prototype for pairing electrochemical and thermal organometallic reactions that will guide the design and development of multicatalyst cascades.

Fernández, Sergio [Department of Chemistry Univers↗
DOE OSTI2024

Room-Temperature Formate Ester Transfer Hydrogenation Enables an Electrochemical/Thermal Organometallic Cascade for Methanol Synthesis from CO 2

The reduction of CO 2 to synthetic fuels is a valuable strategy for energy storage. However, the formation of energy-dense liquid fuels such as methanol remains rare, particularly under low-temperature and low-pressure conditions that can be coupled to renewable electricity sources via electrochemistry. Here, in this study, a multicatalyst system pairing an electrocatalyst with a thermal organometallic catalyst is introduced, which enables the reduction of CO 2 to methanol at ambient temperature and pressure. The cascade methanol synthesis proceeds via CO 2 reduction to formate by electrocatalyst [Cp*Ir(bpy)Cl] + (Cp*=pentamethylcyclopentadienyl, bpy=2,2'-bipyridine), Fischer esterification of formate to isopropyl formate catalyzed by trifluoromethanesulfonic acid (HOTf), and thermal transfer hydrogenation of isopropyl formate to methanol facilitated by the organometallic catalyst (H-PNP)Ir(H) 3 (H-PNP=HN(C 2 H 4 P i Pr 2 ) 2 ). The isopropanol solvent plays several crucial roles: activating formate ion as isopropyl formate, donating hydrogen for the reduction of formate ester to methanol via transfer hydrogenation, and lowering the barrier for transfer hydrogenation through hydrogen bonding interactions. In addition to reporting a method for room-temperature reduction of challenging ester substrates, this work provides a prototype for pairing electrochemical and thermal organometallic reactions that will guide the design and development of multicatalyst cascades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Small Molecule Activation with Intramolecular “Inverse” Frustrated Lewis Pairs

The intramolecular “inverse” frustrated Lewis pairs (FLPs) of general formula 1-BR 2 -2-[(Me 2 N) 2 C=N]-C 6 H 4 (3-6) [BR 2 = BMes 2 (3), BC 12 H 8 , (4), BBN (5), BBNO (6)] were synthesized and structurally characterized by multinuclear NMR spectroscopy and X-ray analysis. Furthermore, these novel types of pre-organized FLPs, featuring strongly basic guanidino units rigidly linked to weakly Lewis acidic boryl moieties via an ortho-phenylene linker, are capable of activating H-H, C-H, N-H, O-H, Si-H, B-H and C=O bonds. 4 and 5 deprotonated terminal alkynes to form the zwitterionic borates 1-(RC≡C-BR 2 )-2-[(Me 2 N) 2 C=NH]-C 6 H 4 (R = Ph, H) and reacted with ammonia, BnNH 2 and pyrrolidine, to generate the adducts 1-(R 2 HN→BR 2 )-2-[(Me 2 N) 2 C=NH]-C 6 H 4 , where the N-H functionality is activated by intramolecular H-bond interactions. In addition, 5 was found to rapidly add across the double bond of H 2 CO, PhCHO and PhNCO to form cyclic zwitterionic guanidinium borates in excellent yields. Likewise, 5 is capable of cleaving H 2 , HBPin and PhSiH 3 to form various amino boranes. Collectively, the results demonstrate that these new types of intramolecular FLPs featuring weakly Lewis acidic boryl and strongly basic guanidino moieties are as potent as conventional intramolecular FLPs with strongly Lewis acidic units in activating small molecules.

08 HYDROGEN↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2020

Moiré superconductivity

Recently, superconductivity was discovered at very low densities in slightly misaligned graphene multilayers. Surprisingly, despite extremely low electronic density (about 10 –4 electrons per unit cell), these systems realize strong-coupling superconductivity, with the transition temperature being a large fraction of the Fermi energy $(T_C ~ 0.1_{ϵ_{F}})$. In this work, we propose a qualitative explanation for this remarkable phenomenon, highlighting similarities and qualitative differences with the conventional uniform high-density superconductivity. Most importantly, we find that periodic superimposed potential generically enhances local interactions relative to nonlocal (for instance, Coulomb) interactions. In addition, the density of states is enhanced as well, exponentially in modulation strength for low lying bands in some cases. Combination of these two effects makes moiré systems natural intermediate or strong-coupled superconductors, with potential for very high transition temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗