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At least 631 records · Page 35

Embracing fine-root system complexity in terrestrial ecosystem modeling

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models. In this work, we propose a three-pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial–temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof-of-concept demonstration of TAM in a big-leaf model both conservatively and radically shows robust impacts of differentiation within fine-root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal.

59 BASIC BIOLOGICAL SCIENCES↗

Coordinated resource allocation to plant growth–defense tradeoffs

Summary Plant resource allocation patterns often reveal tradeoffs that favor growth (G) over defense (D), or vice versa. Ecologists most often explain G–D tradeoffs through principles of economic optimality, in which negative trait correlations are attributed to the reconciliation of fitness costs. Recently, researchers in molecular biology have developed ‘big data’ resources including multi‐omic (e.g. transcriptomic, proteomic and metabolomic) studies that describe the cellular processes controlling gene expression in model species. In this synthesis, we bridge ecological theory with discoveries in multi‐omics biology to better understand how selection has shaped the mechanisms of G–D tradeoffs. Multi‐omic studies reveal strategically coordinated patterns in resource allocation that are enabled by phytohormone crosstalk and transcriptional signal cascades. Coordinated resource allocation justifies the framework of optimality theory, while providing mechanistic insight into the feedbacks and control hubs that calibrate G–D tradeoff commitments. We use the existing literature to describe the coordinated resource allocation hypothesis (CoRAH) that accounts for balanced cellular controls during the expression of G–D tradeoffs, while sustaining stored resource pools to buffer the impacts of future stresses. The integrative mechanisms of the CoRAH unify the supply‐ and demand‐side perspectives of previous G–D tradeoff theories.

Monson, Russell K.↗

Through the lens of bioenergy crops: advances, bottlenecks, and promises of plant engineering

Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.

60 APPLIED LIFE SCIENCES↗

Understanding Carbon Dioxide Transfer in Direct Methanol Fuel Cells Using a Pore-Scale Model

Abstract The gas flow of carbon dioxide from the catalyst layer (CL) through the microporous layer (MPL) and gas diffusion layer (GDL) has great impacts on the water and fuel management in direct methanol fuel cells (DMFCs). This work has developed a liquid–vapor two-phase model considering the counter flow of carbon dioxide gas, methanol, and water liquid solution in porous electrodes of DMFC. The model simulation includes the capillary pressure as well as the pressure drop due to flow resistance through the fuel cell components. The pressure drop of carbon dioxide flow is found to be about two to three orders of magnitude higher than the pressure drop of the liquid flow. The big difference between liquid and gas pressure drops can be explained by two reasons: volume flowrate of gas is three orders of magnitude higher than that of liquid; only a small fraction of pores (<5%) in hydrophilic fuel cell components are available for gas flow. Model results indicate that the gas pressure and the mass transfer resistance of liquid and gas are more sensitive to the pore size distribution than the thickness of porous components. To buildup high gas pressure and high mass transfer resistance of liquid, the MPL and CL should avoid micro-cracks during manufacture. Distributions of pore size and wettability of the GDL and MPL have been designed to reduce the methanol crossover and improve fuel efficiency. The model results provide design guidance to obtain superior DMFC performance using highly concentrated methanol solutions or even pure methanol.

Electrochemistry↗

Experimental Analysis of Advanced Turbine for Supercritical CO2 Power Cycle

An advanced first stage high pressure turbine blade for a supercritical CO2 power cycle is tested in the Big Rig for Aerothermal Stationary Turbine Analysis (BRASTA) annular cascade at the Purdue Experimental Turbine Aerothermal Laboratory (PETAL) high pressure blow down facility alongside a baseline blade for comparison of aerodynamic performance. Both geometries are tested simultaneously using a novel off-axis rig design to allow for the blade geometries to be scaled up to achieve higher Reynolds numbers. The off-axis design necessitates the use of discrete sectors of airfoils, which are designed and additively manufactured in house using an mSLA printer. Printing the blades allows for unique routing of passage for blade surface static pressure taps that contour to the blade shape instead of requiring a straight line view from surface tap to egress, allowing all instrumentation for 15%, 50%, and 85% span to exist in the same passage. A flow conditioning gauze [1] is placed upstream of the blade passages to impart pressure, Mach, and swirl profiles to mimic the rotor relative frame inlet conditions to the blade. Downstream, a sonic valve is used to alter the backpressure to achieve different pressure ratios for different blowdown setpoints. The design of the rig, sonic wheel, and instrumentation are discussed along with the manufacturing and GD&T of the blades with respect to the build-up of this novel experimental apparatus. Initial commissioning data is obtained from inlet total pressure and temperature rakes, blade static pressure taps, and exit total pressure rakes. Oil visualization is performed using a silicone oil mixture containing titanium dioxide and pigmented for contrast of the blade suction side surface with the hub and shroud endwalls. The viscosity of the oil is tuned so that the oil does not fully thin and blow away during the time of the test, and endoscopic cameras are placed in the rig for live monitoring and recording. The preliminary oil visualization and rake profiles are presented, showcasing the operability of this additively manufactured off-axis flow path design.

Tuite, Logan [Purdue University]↗

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D is Driving Speed, Scale, and Efficiency

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Gibson, Brian↗

Development and characterization of the flight model spectrometer onboard LuSEE-Night

The Lunar Surface Electromagnetics Experiment - Night, LuSEE-Night, is a low-frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio-quiet far side of the Moon. Here, the LuSEE-Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA’s Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5-50MHz band. The flight model (FM) of the four-channel spectrometer has been developed. The FM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the FM meets all the requirements for LuSEE-Night.

47 OTHER INSTRUMENTATION↗

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Realization of thousand-second improved confinement plasma with Super I-mode in Tokamak EAST

Mastering nuclear fusion, which is an abundant, safe, and environmentally competitive energy, is a great challenge for humanity. Tokamak represents one of the most promising paths toward controlled fusion. Obtaining a high-performance, steady-state, and long-pulse plasma regime remains a critical issue. Recently, a big breakthrough in steady-state operation was made on the Experimental Advanced Superconducting Tokamak (EAST). A steady-state plasma with a world-record pulse length of 1056 s was obtained, where the density and the divertor peak heat flux were well controlled, with no core impurity accumulation, and a new high-confinement and self-organizing regime (Super I-mode = I-mode + e-ITB) was discovered and demonstrated. These achievements contribute to the integration of fusion plasma technology and physics, which is essential to operate next-step devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Lithium pollution of a white dwarf records the accretion of an extrasolar planetesimal

Tidal disruption and subsequent accretion of planetesimals by white dwarfs can reveal the elemental abundances of rocky bodies in exoplanetary systems. Those abundances provide information on the composition of the nebula from which the systems formed, which is analogous to how meteorite abundances inform our understanding of the early Solar System. We report the detection of lithium, sodium, potassium, and calcium in the atmosphere of the white dwarf Gaia DR2 4353607450860305024, which we ascribe to the accretion of a planetesimal. Using model atmospheres, we determine abundance ratios of these elements, and, with the exception of lithium, they are consistent with meteoritic values in the Solar System. We compare the measured lithium abundance with measurements in old stars and with expectations from Big Bang nucleosynthesis.

79 ASTRONOMY AND ASTROPHYSICS↗

Artificial intelligence “sees” split electrons

Chemical bonds between atoms are stabilized by the exchange-correlation (xc) energy, a quantum-mechanical effect in which “social distancing” by electrons lowers their electrostatic repulsion energy. Kohn-Sham density functional theory (DFT) states that the electron density determines this xc energy, but the density functional must be approximated. Furthermore, this is usually done by satisfying exact constraints of the exact functional (making the approximation predictive), by fitting to data (making it interpolative), or both. Two exact constraints—the ensemble-based piecewise linear variation of the total energy with respect to fractional electron number and fractional electron z-component of spin —require hard-to-control nonlocality. On page 1385 of this issue, Kirkpatrick et al. have taken a big step toward more accurate predictions for chemistry through the machine learning of molecular data plus the fractional charge and spin constraints, expressed as data that a machine can learn.

74 ATOMIC AND MOLECULAR PHYSICS↗

TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems

Deep learning (DL), in particular deep neural networks, by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties—such as stability, conservation, and positivity—and accuracy are required. DL methods in their original forms are often not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates for missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics, hence promoting better generalization. This paper develops a model-constrained DL approach and its variant TNet—a Tikhonov neural network—which are capable of learning not only information hidden in the training data but also in the underlying mathematical models to solve inverse problems governed by partial differential equations in low data regimes. We provide the constructions and some theoretical results for the proposed approaches for both linear and nonlinear inverse problems. Since TNet is designed to learn inverse solutions with Tikhonov regularization, it is interpretable: in fact it recovers Tikhonov solutions for linear cases while potentially approximating Tikhonov solutions for nonlinear inverse problems. We also prove that data randomization can enhance not only the smoothness of the networks but also their generalizations. Comprehensive numerical results confirm the theoretical findings and show that with even as little as 1 training data sample for one-dimensional (1D) deconvolution, 5 for an inverse 2D heat conductivity problem, 100 for inverse initial conditions for a time-dependent 2D Burgers’s equation, and 50 for inverse initial conditions for 2D Navier–Stokes equations, TNet solutions can be as accurate as Tikhonov solutions while being several orders of magnitude faster. Furthermore, this is possible owing to the model-constrained term, replications, and randomization.

97 MATHEMATICS AND COMPUTING↗

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Towards a muon collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work.

43 PARTICLE ACCELERATORS↗

Higgs-portal dark matter in Brane-world cosmology

Abstract The Higgs-portal scalar dark matter (DM) model is a simple extension of the Standard Model (SM) to incorporate a DM particle to the SM, where a $$Z_2$$ Z 2 -odd real scalar field is introduced as a DM candidate. We consider this DM model in the context of 5-dimensional brane-world cosmology, where our 3-dimensional space is realized as a hyper-surface embedded in 4-dimensional space. In the setup, all the SM and DM fields reside on the hyper-surface while graviton lives in the bulk. We consider two well-known brane-world cosmologies, namely, the Randall–Sundrum (RS) and the Gauss–Bonnet (GB) brane-world cosmologies, in which the standard Big Bang cosmology is reproduced at low temperatures below the so-called “transition temperature” while at high temperatures the expansion law of the universe is significantly modified. Such a non-standard expansion law directly impacts the prediction for the relic density of the Higgs-portal DM. We investigate the brane-world cosmological effects and identify the allowed model parameter region by combining the constraints from the observed DM relic density, and the direct and indirect DM detection experiments. It is well-known that only DM masses in the vicinity of half the Higgs boson mass are allowed in the Higgs-portal scalar DM model. We find that the allowed parameter region becomes more severely constrained and even disappears in the RS cosmology, while the GB cosmological effect significantly enlarges the allowed region. Upon discovering Higgs-portal DM, we can determine transition temperature in the GB brane-world cosmology.

Liu, Taoli↗

Cold freeze out of superheavy dark matter and Hubble tension

We present a unified framework, the "X miracle", in which dark matter consists of superheavy, nonthermal X particles whose relic abundance is determined not by the conventional weak-scale, semi-relativistic ("hot") freeze-out of WIMPs, but by annihilation or decay occurring within the smallest and earliest gravitationally bound objects. Unlike thermal WIMPs, which decouple at velocities of order 0.3c with relic abundance ρ∞ set by weak-scale interactions, X particles are produced nonthermally with an initial overabun dance ρ ini >> ρ ∞ . They become nonrelativistic extremely early, redshift to ultra-cold velocities, allowing collapse into compact bound structures characterized by a novel quantum gravitational scale, r X = 4$\hbar$ 2 $/Gm^3_X$ = 10 −13 m $\hbar$$/m_Xc$, much larger than the Compton wavelength. The framework predicts a particle mass of 10 12 GeV and an enhanced cross section of 10 −21 m 3 /s. Overlapping particle wavefunctions in these compact structures drive annihilation or decay into additional radiation, leading to a "cold" freeze-out that converts most of ρ ini into radiation while leaving a relic density ρ ∞ . Solutions to the Boltzmann equation indicate that an extreme ("big") depletion, with only one particle in a billion surviving, yields an additional radiation contribution $ΔN_{eff}$ ≈ 0.4, which could help alleviate the Hubble tension. For particles of 10 12 GeV, the scenario predicts a dark coupling constant α X = 0.09 that is responsible for an instanton-induced decay process, consistent with current UHECR bounds. Early collapse at 10 −6 s may release binding energy as high-frequency (100kHz) gravitational waves or ultralight GUT-scale axions (10 −9 eV). Superheavy sterile neutrinos provide a natural particle realization, linking dark matter to neutrino mass and baryogenesis. If gravitationally produced, this framework favors high-scale inflation and effi cient reheating. The "X miracle" thus demonstrates that dark matter need not be weak-scale: gravitational dynamics can control freeze-out and evolution, producing multi-messenger observational signatures in UHECRs, axions, gravitational waves, and small-scale structures.

Xu, Zhijie Jay [Pacific Northwest National Laborat↗

Neutrinos in Stellar Astrophysics (in The Encyclopedia of Cosmology)

The physics of the mysterious and stealthy neutrino is at the heart of many phenomena in the cosmos. These particles interact with matter and with each other through the aptly named weak interaction. At typical astrophysical energies the weak interaction is some twenty orders of magnitude weaker than the electromagnetic interaction. However, in the early universe and in collapsing stars neutrinos can more than make up for their feeble interaction strength with huge numbers. Neutrinos can dominate the dynamics in these sites and set the conditions that govern the synthesis of the elements. Here we journey through the history of the discovery of these particles and describe their role in stellar evolution and collapse, the big bang, and multi-messenger astrophysics. Neutrino physics is at the frontier of elementary particle physics, nuclear physics, astrophysics and cosmology. All of these fields overlap in the neutrino story.

Haxton, Wick↗

Scanning Probe Microscopy in the Information Age

Scanning probe microscopy (SPM) has brought scientific exploration, visualization, and control to nanoscale. A plethora of measurement approaches and techniques devised around SPM enabled probing structure and functionalities crucial for virtually all areas of science. While high-resolution electron microscopy techniques preceded scanning probes, SPM effectively opened the door to probing the structure–property relationship on length scales spanning 100s of micrometers down to a single atom. As the complexity of the material systems and the scientific questions increased, so too did the SPM techniques. Substantial efforts have been dedicated toward the development of better instrumentation platforms, probes, and novel spectroscopic measurements; however, many opportunities related to data processing and acquisition in SPM are just being realized now. This chapter will describe the recent progress in data-driven SPM by giving a broad overview of the field, the foundation of the scanning tunneling microscope (STM) and the atomic force microscope (AFM), as well as a description behind the operating principles and dynamic modes. A comprehensive overview of the multi-frequency techniques, Band excitation, and general acquisition mode SPM is given, setting the context for the application of big data, machine learning, and artificial intelligence in the field of dynamic SPM.

Collins, Liam↗