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At least 793 records · Page 44

Laser-induced selective local patterning of vanadium oxide phases

The same elements can form different compounds with widely different physical properties. Synthesis of a single-phase material is commonly achieved by controlling experimental conditions. Synthesizing materials that incorporate multiple specific spatially distributed chemical phases is often challenging, especially if different phases must be organized into well-defined spatial patterns. Here, we present an efficient solid reaction laser annealing (SRLA) approach to directly write regions of different local chemical compositions. We demonstrate the practical utility of our approach by locally writing microscale patterns of distinct chemical phases in vanadium oxide thin films. Specifically, we achieved the controlled local recrystallization of a uniform V 2 O 3 matrix into VO 2 , V 3 O 5 , and V 4 O 7 regions exhibiting sharp 1st- and 2nd-order metal–insulator phase transitions over a wide range of critical temperatures, i.e., a characteristic feature of select vanadium oxides that is extremely sensitive to even minute structural or compositional imperfections. We utilized the local chemical phase writing to pattern spiking oscillators with distinct electrical behavior directly in the thin film sample without employing elaborate lithography fabrication. Our laser tuning local chemical composition opens a pathway to synthesize a wide range of artificially micropatterned composite materials, with precision and control unattainable in conventional material synthesis methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Critical Role of the Steric Factor in the Viscoelasticity of Vitrimers

Dynamic covalent networks (DCNs) are a promising solution to mitigate plastic-waste-related issues through improved recyclability enabled by dynamic bonds. However, our understanding of the mechanisms controlling their viscoelasticity, especially in vitrimers where bond exchange relies on associative reactions, remains limited. Here, in this study, we investigate the dynamics in model DCNs with boric ester functionalities, and the analysis of the temperature dependence of their terminal relaxation times revealed a puzzling result: extremely large Arrhenius prefactors associated with their dynamic bond rearrangement times. We ascribe this observation to the often-overlooked chemical steric factor that slows down chemical reactions, therefore decreasing the vitrimers’ bond exchange rate by many orders. The estimated steric factor of the bond exchange in our model DCNs is comparable to those observed in boronic ester exchange reactions between small molecules. Additional analysis of literature data revealed an overall low steric factor also for imine bond exchange, thus highlighting the role of this parameter in tremendously slowing down bond exchange in DCNs despite low activation energy barriers. We propose a general approach for designing vitrimers with desired viscoelastic and creep properties considering the critical role of the steric factor in bond rearrangement mechanisms, in addition to the traditionally considered activation energy and matrix properties.

Carden, Gregory P. [Univ. of Tennessee, Knoxville,↗

Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6

Abstract Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climate projections and quantify the relative importance of downscaling and bias-correction. We analyze simple climate metrics such as annual temperature and precipitation averages, as well as several indices of climate extremes. We find that downscaling and bias-correction often contribute substantial uncertainty to local decision-relevant climate outcomes, though our results are strongly heterogeneous across space, time, and climate metrics. Our results can provide guidance to impact modelers and decision-makers regarding the uncertainties associated with downscaling and bias-correction when performing local-scale analyses, as neglecting to account for these uncertainties may risk overconfidence relative to the full range of possible climate futures.

54 ENVIRONMENTAL SCIENCES↗

Analysis of Physical and Structural Properties of Alkali Oxide–Modified Tellurite Glasses

Glasses in the system xM 2 O·(1-x)TeO 2 (0 ≤ x ≤ 0.25), where M = Li, Na, K, Rb, Cs, were made by melting in platinum crucibles. Various quenching methods were used to make each glass because some glasses require extremely rapid cooling. Physical properties such as the glass transition onset temperature (Tg) and density (ρ) were measured. In order to explore the short-range structure in these glasses, Raman spectroscopy, infrared spectroscopy, and high-energy x-ray diffraction were used. Furthermore, these experiments on tellurite glasses were performed to find correlations between the physical and structural properties.

36 MATERIALS SCIENCE↗

New York State Climate Impacts Assessment Chapter 01: Introduction

Abstract New York State's climate is changing. Temperatures are rising, precipitation is increasing, sea levels are rising, and extreme weather events are becoming more frequent and severe. Over time, these changes are projected to worsen. The New York State Climate Impacts Assessment provides a science‐based analysis of what to expect from climate change in New York. Working with partners across the state, the team assembled to produce this assessment reviewed the latest science and modeling to project and characterize what New York State's climate is likely to look like in the future and how this will affect natural systems, society, and various sectors of the economy. The assessment will enable decision‐makers at all levels—from individual residents, businesses, and landowners to municipal and state government agencies—to better understand and make informed choices about how to plan and prepare for climate change.

Science & Technology - Other Topics↗

Genome-Scale Metabolic Model of Caldicellulosiruptor bescii Reveals Optimal Metabolic Engineering Strategies for Bio-based Chemical Production

The extremely thermophilic cellulolytic bacterium, Caldicellulosiruptor bescii , degrades plant biomass at high temperatures without any pretreatments and can serve as a strategic platform for industrial applications. The metabolic engineering of C. bescii , however, faces potential bottlenecks in bio-based chemical productions.

59 BASIC BIOLOGICAL SCIENCES↗

Progress and Perspectives: Zirconium Electrodeposition from Different Electrolytes

Zirconium (Zr) possesses outstanding properties, including exceptional chemical resistance and a high melting point, and is therefore desirable for use in a wide variety of challenging environments, such as in nuclear reactors and the chemical processing industry. To minimize the amount of Zr required for any given application, developing methods for generating metallic Zr coatings is highly advantageous. Owing to Zr’s highly negative reduction potential, electrodeposition in traditional solvents at near ambient temperatures remains challenging and thus not well understood. Due to the extreme conditions required to deposit this metal, extensive work has been conducted in molten salt electrolytes, however the broad applicability of this methodology is limited due to its corrosivity. The primary focus of this article is to present an overview of Zr’s electrochemical behavior and to consolidate the efforts of researchers in exploring electrodeposition techniques for Zr involving aqueous, organic, ionic liquid, deep eutectic, and molten salt solvents. With this information, we highlight trends across solvent systems and opportunities for future research.

42 ENGINEERING↗

Influence of extreme fracture flow channels on the thermal performance of open-loop geothermal systems at commercial scale

Abstract Adequate stewardship of geothermal resources requires accurate forecasting of long-term thermal performance. In enhanced geothermal systems and other fracture-dominated reservoirs, predictive models commonly assume constant-aperture fractures, although spatial variations in aperture can greatly affect reservoir permeability, fluid flow distribution, and heat transport. Whereas previous authors have investigated the effects of theoretical random aperture distributions on thermal performance, here we further explore the influence of permeability heterogeneity considering field-constrained aperture distributions from a meso-scale field site in northern New York, USA. Using numerical models of coupled fluid flow and heat transport, we conduct thermal–hydraulic simulations for a hypothetical reservoir consisting of a relatively impervious porous matrix and a single, horizontal fracture. Our results indicate that in highly channelized fields, most well design configurations and operating conditions result in extreme rates of thermal drawdown (e.g., 50% drop in production well temperatures in under 2 years). However, some other scenarios that account for the risks of short-circuiting can potentially enhance heat extraction when mass flow rate is not excessively high, and the direction of geothermal extraction is not aligned with the most permeable features in the reservoir. Through a parametric approach, we illustrate that well separation distance and relative positioning play a major role in the long-term performance of highly channelized fields, and both can be used to help mitigate premature thermal breakthrough.

Rangel-Jurado, Nicolás↗

Machine learning assisted prediction of tungsten heavy alloy plasma facing component performance for fusion energy applications

Tungsten and tungsten heavy alloys (WHAs), known for their remarkably high hardness, durability, and corrosion resistance, play a critical role in the thriving development of nuclear fusion reactors in recent years. However, the exploration in tungsten alloys for the nuclear-related applications has been limited by the difficulty of manufacturing and the complexity of experiments to reproduce the environment of nuclear reaction. Therefore, this project aims to utilize nanoscale simulation methods such as density functional theory (DFT) and molecular dynamics (MD) with the help of machine learning techniques to not only understand the mechanisms of tungsten alloys but also allow us to computationally predict their mechanical behaviors under extreme environments. One critical problem of the application of WHAs in nuclear reactors is the surface melting. In the current design of the SPARC reactor, the WHA, W97Ni2.1Fe0.9 or W97NiFe, is chosen to be the first wall components to confine the plasma where the particles are fiercely moving and colliding into each other to create nuclear fusion reaction. This process will generate extremely high heat flux onto these WHA tiles, leaving high surface temperature that could possibly melt the surface of the WHA tiles, As illustrated in Fig. 1(a). a laser experiment previously done illustrates that a rough surface damage would be made after the surface melting where the matrix area mainly composed of nickel and iron as shown in Fig. 1(b), will first melt and then leave vacancies between these tungsten grains. Unfortunately, these kinds of roughness on the first-wall components could be deadly to the plasma inside a Tokmak reactor because the heat that is supposed to dissipate at a designed ratio through the tiles may in turn be excessively absorbed and accumulated on any uneven area of the surface, which will eventually make the whole nuclear reaction fail. In this project, we will introduce a machine learning potential, Allegro, based on DFT calculation and then build a MD model for W-Ni-Fe alloys.

36 MATERIALS SCIENCE↗

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗

Assessment of the Amplification Factor Transport Transition Model for High-Mach Number Flows

In its present state, the Amplification Factor Transport (AFT) model provides an efficient alternative for high-fidelity stability methods for complex geometries subject to low-speed conditions considered in the industrial-production CFD environment. Its performance has not yet been documented for the typical transition scenarios in hypersonic conditions. The goal of the present study is to apply the AFT model to a yawed- and flared-cone configuration, for which the laminar flows respectively support the crossflow and second-mode instability mechanisms, and to compare the amplification results against high-fidelity results computed with Linear Parabolized Stability Equations (LPSE). High-resolution viscous-flow solutions are provided with OVERFLOW and COFFE. For the yawed-cone case, the AFT model yields the largest amplification in a region of the flow where the expected crossflow instability is not amplified (in the leeward symmetry plane) according to LPSE. For the flared-cone case with a wall-temperature slightly lower than the adiabatic value, the AFT model yields extremely large amplification factors (in excess of 100), exceeding the computed LPSE amplification of second-mode instability results by a factor 14. Upon considering a cold-wall condition, the model shows much smaller amplification factors, which is the opposite with respect to the expected behavior of the second mode. The AFT model does not adequately capture the crossflow and second-mode instability mechanisms in its present state.

Groot, Koen J.↗

Global variance decomposition of downscaled and bias-corrected CMIP6 climate projections

This dataset provides the results of a global variance decomposition of downscaled and bias-corrected CMIP6 climate projections. The total projection variance for a set of climate metrics is partitioned into contributions from: scenario uncertainty, model/GCM uncertainty, downscaling and bias-correction uncertainty, and interannual variability. The contribution from each source is expressed as a percentage of the total variance. Seven climate metrics are analyzed: Annual average temperature (avg_tas.nc) Annual total precipitation (tot_pr.nc) Annual maximum of daily maximum temperature (max_tasmax.nc) Annual maximum 1-day precipitation (max_pr.nc) Annual number of extremely hot days (hot_days.nc) Annual number of extremely wet days (wet_days.nc) Annual number of dry days (dry_days.nc) Extremely hot/wet days are defined to occur when temperature/precipitation exceeds the local 99th percentile defined over 1980-2014. Dry days are defined to occur when daily precipitation is less than 1mm. all_metrics_timesliced.nc gives the results for all metrics averaged over three 20-year periods: 2020-2039, 2050-2069, 2080-2099. For more details on the methods, see: Lafferty & Sriver, Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6, npj Climate & Atmospheric Science (2023) An interactive visualization of this data can be found at: https://lafferty-sriver-2023-downscaling-uncertainty.msdlive.org

Lafferty, David↗

Development of coated heat flux gauges for fast responding measurements

Thermal systems often exhibit transient behaviors that have important implications for the operation of the system and can be difficult to predict. For these reasons, experimental testing is often required to ensure system durability requirements are achieved. One important parameter governing the survivability of components in hot, high-stress environments is the heat flux into the part that dictates the temperature distribution for the component. However, sensors required to experimentally characterize heat fluxes in extreme environments must also be resilient. Here, this study presents the development of coated heat transfer gauges capable of robust, high-frequency measurements in turbine research facilities. The addition of a protective coating increases the durability of the gauge, but inherent of that coating is the attenuation of high-frequency temperature penetrations. As a result, this study first outlines the use of analytical solutions to define a gauge design for a specific frequency range and heat transfer, ensuring that subsurface signals can be rectified to surface conditions through inverse methods. Then, the fabrication of polyimide substrate sensors with a parylene-F coating is described. Micro surface heaters added to the custom sensors were used to determine important geometric and thermal properties necessary to calculate accurate surface heat flux. Ultimately, this work shows increased sensor robustness in a turbine test bed and experimentally validates that the frequency response of the fabricated sensors meet the design intent.

47 OTHER INSTRUMENTATION↗

Containerless Techniques for in-situ X-Ray Measurements on Materials in Extreme Conditions

This article reviews containerless methods in the context of measuring materials in-situ using x-ray techniques. Non-contact processing enables high chemical purity to be achieved even at temperatures above 2000 °C. The absence of extrinsic heterogenous nucleation allows supercooling and supersaturation of liquids and solutions. In many cases new types of glass can be made, particularly from fragile liquids. Current methods for containerless processing are briefly reviewed. The application of scattering, spectroscopic and emerging x-ray techniques is described. Examples including measurements on molten metal oxides, molten metallic alloys and organic molecules are presented. The work is discussed in the broader context of combining structure and property data to predict how to make new applied materials.

36 MATERIALS SCIENCE↗

The Quest for High-Temperature Superconductivity in Nickelates under Ambient Pressure

Recently, superconductivity with Tc ≈ 80 K was discovered in La3Ni2O7 under extreme hydrostatic pressure (>14 GPa). For practical applications, we needed to stabilize this state at ambient pressure. It was proposed that this could be accomplished by substituting La with Ba. To put this hypothesis to the test, we used the state-of-the-art atomic-layer-by-layer molecular beam epitaxy (ALL-MBE) technique to synthesize (La1−xBax)3Ni2O7 films, varying x and the distribution of La (lanthanum) and Ba (barium). Regrettably, none of the compositions we explored could be stabilized epitaxially; the targeted compounds decomposed immediately into a mixture of other phases. So, this path to high-temperature superconductivity in nickelates at ambient pressure does not seem promising.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Three-dimensional superconductivity induced by an extremely small amount of Li in Li x ⁢SnSe 2

Unconventional superconductivity occurs often in materials with low dimensionality. Furthermore, we report superconductivity observed in layered Li x SnSe 2 with the superconducting transition temperature T c ~ 6 K. Through L⁢i + intercalation in semiconducting SnSe 2 via electrochemical process, Li x ⁢SnSe 2 is formed with an extremely small x value as estimated from the c-axis lattice parameter, carrier concentration, and first-principles calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Construction of MoS 2 /MoO 3 Heterostructure with Ultrafast-Charged and Superior Low-Temperature Sodium Storage Properties

The electrochemical reaction kinetics of sodium-ion batteries (SIBs) become sluggish at low temperatures, resulting in significant reductions in energy density and power density. Rational design of anode materials with excellent low-temperature performance is of great significance for promoting the application of SIBs under extreme conditions. Here, in this study, the spontaneous hydrolysis and oxidation reactions of MoS 2 in aqueous solution are used to successfully construct the MoS 2 /MoO 3 heterostructure. The formation of built-in electric fields at the MoS 2 /MoO 3 heterointerfaces improves the electrochemical reaction kinetics, thereby enhancing the rate performance. In addition, the dual-phase material can effectively buffer the volume strain during the cycle process, thereby improving the cycle stability. Thus, the MoS 2 /MoO 3 displays ultrafast charging properties at room temperature (up to 244.6 mAh g –1 at 40 A g –1 , discharge/charge in 22 s). Even at −40 °C, it also exhibits a high capacity of 303.7 mAh g –1 and superior cycling performance (capacity retention rate up to 92.9% after 900 cycles at 2 A g –1 ).

MoS2↗