The highly selective catalytic hydrogenation of CO[subscript 2] to CO over transition metal nitrides
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This study aims to isolate interactions between plies and tows and reveal fundamental physics involved in transverse impact on fiber-reinforced composite (FRC) structures. Composite strips were sectioned from large panels and characterized by optical photography, three-dimensional synchrotron X-ray computed tomography, and scanning electron microscopy (SEM). Each strip was impacted perpendicularly by a right circular cylinder (RCC) projectile at a velocity ranging from ~ 150 to 600 m/s. The global strip behavior, as well as localized deformation and failure of the strip near the projectile corner, were both captured by high-speed optical imaging. S-Glass FRC strips were observed to fail in tension ahead of the RCC projectiles’ flat surfaces while Kevlar® FRC strips fractured at the projectile corners. The concept of critical velocity region previously used for impact on yarns was introduced to define different failure modes of each composite strip type. The strip damage extent was found to increase with the impact velocity and reach the maximum at the upper limit of the critical velocity region. Above the critical velocity region, the damage extent decreased with impact velocity. Wave propagations and load histories in the composite strips during impact were quantified and compared with Smith’s theory. Finally, critical velocities of single fibers, yarns, and composite strips and ballistic limits of single-ply and multi-ply composite panels were compared to provide insight into the design of impact-resistant fabrics and composites.
Here, we report the application of high-speed synchrotron X-ray phase contrast imaging (PCI) in real-time damage characterization for glass fiber reinforced composites (GFRCs) subjected to dynamic loading. Dynamic single-edge notched bending (DSENB) experiments on pre-notched S-2 GFRCs were performed on a modified Kolsky compression bar. During loading, the synchrotron X-ray beam penetrated through the composite specimen from the side to detect damage evolution inside the material. Entire dynamic events were recorded by a high-speed camera as image sequences.0° and 90° unidirectional and cross-ply composites were investigated. An optical imaging technique was also employed to capture similar dynamic events in comparison with the radiographic imaging. It is demonstrated that high-speed X-ray PCI had sufficient phase contrast to characterize a crack initiation at a 20-μm spatial resolution within 920 ns and track the crack geometry during propagation, thereby providing reliable data to quantify the dynamic damage resistance of GFRCs. Furthermore, being capable of recognizing microscopic damage-related features at a sub-10-μm resolution, high-speed X-ray PCI provided fundamental material failure mechanisms to reveal the essential of macroscale structural failure of composites. It can also track the damage evolution inside and between individual plies of laminated composites. However, current high-speed X-ray PCI technique only supports in-situ observation and the high timing and spatial resolutions are limited within a field of view of ~2.5 mm in square, preventing its application in the three-dimensional and larger-area damage detection for GFRC structures.
Here, the dynamic fracture of S-2 glass fiber-reinforced polymer matrix composites (FRPMCs) was investigated in this study. The matrix ductility was improved by a recently developed network topology modification technique via mixing partially reacted substructures (mPRS). The composite material was manufactured and characterized by micro-CT scanning and scanning electron microscopy (SEM). Dynamic single-edge notched bending (d-SENB) experiments were performed on the composites by using a modified split-Hopkinson pressure bar. Each specimen’s fracture process was visualized by ultrafast X-ray imaging. Such in-situ radiography enabled identifying the damage initiation below 50-micron scale and inspecting its propagation through the internal structures of opaque composites, thereby accurately quantifying the composites’ mechanical properties. Furthermore, the identical d-SENB experiments were designed and the digital image correlation (DIC) was employed to monitor the stress wave propagation on the composite specimens. The force and deflection measurements were modified and correlated to the physical damage processes. Besides, quasi-static SENB experiments were conducted to identify the loading rate effects on the composites’ fracture behaviors. The force and deflection history, bending stiffness, energy dissipation, and fracture toughness at different loading rates were quantified and compared. Finally, post-fracture analysis by micro-CT scanning and SEM provided physical observations on the variation of the fracture morphology by different loading rates.
We propose a method to measure multiple dynamic material constitutive parameters of unidirectional fiber reinforced composites (FRCs) in a single experiment. Dynamic short-beam shear (DSBS) experiments were performed on a modified Kolsky compression bar, with integration of high-speed imaging and digital image correlation (DIC). The unidirectional FRCs investigated were S-2 glass fiber reinforced matrix of TGDDM-Jeffamine® D230 with monoamine functionalized partially reacted substructures (mPRS) and commercially available SC-15. Analytical solutions of normal and shear strains of a composite beam were derived based on Timoshenko beam theory, assuming material to be transversely isotropic and have different moduli in tension and compression in each principle material orientation. Tensile and compressive moduli were inversely computed through monitoring normal strain slope when specimen was constantly loaded at a speed of ~7.3 m/s within a small deflection. Non-linear shear stress-strain behavior of the composite was described via Ramberg–Osgood equation. Finite element (FE) analysis was conducted in ABAQUS, simultaneously defining via user subroutine UMAT the transverse isotropy of material, bi-modulus constitutive model, and non-linear shear stress-strain relation. The method proposed in this work was validated by comparing strain distributions computed by FE model and DIC measurements. Comparing with traditional dynamic tensile, compressive, and shear experiments on FRCs, this method significantly simplifies the specimen preparation and design of complicated gripping fixtures for multiple experiments. Here, systematic errors resulting from variations of specimen geometry and dimension, loading direction, and instrumentation are reduced, thereby providing compatible data for numerical studies on impact behavior of composites.
We studied a series of potassium-iodide clusters with formulas of (KI)nI- and (KI)nK+ (n = 1-3) by quantum chemical calculations. The calculated vertical detachment energies (VDEs) of (KI)nI- clusters are all higher than those of halogen anions, and thus can be classified as superhalogen anions; while the calculated vertical electron affinities (VEAs) of (KI)nK+ clusters are all lower than those of alkali metal cations, and thus they can be recognized as superalkali cations. Our calculated VEA for (KI)K+, i.e., K2I+, agrees well with the previously measured value by thermal ionization mass spectrometry. We confirmed the theoretical predictions by measuring the negative ion photoelectron (NIPE) spectra of (KI)nI- anions, from which the measured VDEs are found to be in good agreement with the theoretically calculated values. In addition, we also explored the possibility of supersalts formed by these superhalogen anions and superalkali cations, for example, K3I3 can be regarded as a supersalt formed by K2I+ and KI2– subunits.
This paper explores the role of electricity in achieving economy-wide net-zero CO2 emissions by 2050 in the United States based on results from 17 models as part of the 37th Stanford Energy Modeling Forum (EMF-37). In the study's Net-Zero scenario, the models use diverse pathways to achieve net-zero emissions by 2050, with gross energy-related residual emissions ranging from 17.2 to 66.6 % of 2020 levels. Electricity consistently emerges as central to achieving net-zero, with models projecting rapid electrification of end-uses and rapidly declining CO2 intensity of electricity. However, the extent of electrification and the technology mix to decarbonize the power sector vary considerably across models. In the Net-Zero scenario, electricity is projected to evolve from ~20 % of final energy in 2020 to 17-63 % in 2050 across the models driven by electrification in all sectors-buildings, industry, and transportation-and, to a lesser extent by direct air capture. By 2050, total electricity consumption increases by 24-176 % (relative to 2020), accompanied by significant expansion in renewable electricity production. Together, solar and wind generation grows by 175-834 %, supplying 45-90 % of total electricity in 2050, with wind achieving slightly higher shares than solar. Electricity storage technologies are deployed at scale to support wind and solar generation. The electricity generation mix varies across models: some project almost complete reliance on renewables, while others see a substantial role for natural gas, often with carbon capture and storage. This paper synthesizes the rich diversity of modeling approaches and results, highlighting differing views on how key drivers of electricity demand and supply might evolve.
In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of "centralized dispatching and hierarchical management", this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment.
Selection, testing and validation of electrolyte candidates for Li-ion cells are discussed, based on a 10-minute target for extreme fast charge (XFC). A combination of modeling and laboratory measurements create a timely and synergistic approach to identifying candidate electrolyte formulations. Multi-solvent systems provide a balanced set of properties, wherein lower molecular-weight solvents offer reduced viscosity, increased species diffusivity, and mitigation of concentration polarization at high charge rates. Carefully selected formulations can exhibit peak conductivity and usable conductivity range of two to three times that of the baseline EC-EMC (3:7, wt.) + LiPF 6 . Candidates are also chosen based on stability and longevity within the cell environment. Lab testing coincides with property predictions from the Advanced Electrolyte Model (AEM) and a macro-scale cell model. Furthermore, cell testing utilized coin and pouch cells having NMC532 or NMC811 cathodes with graphite electrodes. Results indicate combinations of low-molecular weight solvents are key for fast-charge electrolytes as they extend the useful conductivity range to both low and higher salt concentrations, and possess higher self-diffusivities compared to conventional solvents. This reduces impacts from concentration polarization. The choice of electrolyte also influences the tendency for lithium metal deposition at the anode, as showcased by experimental and modeling results herein.
Black carbon (BC), by the combustion of fossil fuels and biomass, has profound effects on climate change and glacier retreat in industrial eras. In the present study, we report refractory BC (rBC) in an ice core spanning 1850–2014, retrieved from the Hariqin Glacier of the Tanggula Mountains in the central Tibetan Plateau, measured using a single particle soot photometer (SP2). The rBC concentration shows a three-fold increase since the 1950s. The mean rBC concentration was 0.71±0.52 ng mL-1 during 1850s–1940s and 2.11±1.60 ng mL-1 during 1950s–2010s. The substantial increase in rBC since the 1950s is consistent with rBC ice core records from the Tibetan Plateau and Eastern Europe. According to the predominant atmospheric circulation patterns over the glacier and timing of changes in regional emissions, the post-1950 amplification of rBC concentration in the central Tibetan Plateau most likely reflects increases in emissions in Eastern Europe, former USSR, the Middle East, and South Asia. Despite the low-level background rBC concentrations in the ice cores from the Tibetan Plateau, the present study highlights a remarkable increase in anthropogenic BC emissions in recent decades and the consequent influence on glaciers in the Tibetan Plateau.
Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.
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The overall survival rate of gliomas has not significantly improved despite new effective treatments, mainly due to tumor heterogeneity and drug delivery. Here, we perform an integrated clinic-genomic analysis of 1, 477 glioma patients from a Chinese cohort and a TCGA cohort and propose a potential prognostic model for gliomas. We identify that SBS11 and SBS23 mutational signatures are associated with glioma recurrence and indicate worse prognosis only in low-grade type of gliomas and IDH-Mut subtype. We also identify 42 genomic features associated with distinct clinical outcome and successfully used ten of these to develop a prognostic risk model of gliomas. The high-risk glioma patients with shortened survival were characterized by high level of frequent copy number alterations including PTEN, CDKN2A/B deletion, EGFR amplification, less IDH1 or CIC gene mutations, high infiltration levels of immunosuppressive cells and activation of G2M checkpoint and Oxidative phosphorylation oncogenic pathway.