Showing papers by "Oak Ridge National Laboratory published in 2022"

Lignin-enzyme interaction: A roadblock for efficient enzymatic hydrolysis of lignocellulosics

Abstract: Efficiently producing second-generation biofuels from biomass is of strategic significance and meets sustainability targets, but it remains a long-term challenge due to the existence of biomass recalcitrance. Lignin contributes significantly to biomass recalcitrance by physically limiting the access of enzymes to carbohydrates, and this could be partially overcome by applying a pretreatment step to directly target lignin. However, lignin typically cannot be completely removed, and its structure is also significantly altered during the pretreatment. As a result, lignin residue in the pretreated materials still significantly hindered a complete conversion of carbohydrate to its monosugars by interacting with cellulase enzymes. The non-productive adsorption driven by hydrophobic, electrostatic, and/or hydrogen bonding interactions is widely considered as the major mechanism of action governing the unfavored lignin-enzyme interaction. One could argue this type of interaction between lignin residue and the activated enzymes is the major roadblock for efficient enzymatic hydrolysis of pretreated lignocellulosics. To alleviate the negative effects of lignin on enzyme performance, a deep understanding of lignin structural transformation upon different types of pretreatments as well as how and where does lignin bind to enzymes are prerequisites. In the last decade, the progress toward a fundamental understanding of lignin-enzyme interaction, structural characterization of lignin during pretreatment and/or conformation change of enzyme during hydrolysis is resulting in advances in the development of methodologies to mitigate the negative effect of lignin. Here in this review, the lignin structural transformation upon different types of pretreatments and the inhibition mechanism of lignin in the bioconversion of lignocellulose to bioethanol are summarized. Some technologies to minimize the adverse impact of lignin on the enzymatic hydrolysis, including chemical modification of lignin, adding blocking additives, and post-treatment to remove lignin were also introduced. The production of liquid biofuels from lignocellulosic biomass has shown great environmental benefits such as reducing greenhouse gas emissions and mitigate climate change. By addressing the root causes of lignin-enzyme interaction and how to retard this interaction, it is our hope that this comprehensive review will pave the way for significantly reducing the high cost associated with the enzymatic hydrolysis process, and ultimately achieving a cost-effective and sustainable biorefinery system.

Kokkos 3: Programming Model Extensions for the Exascale Era

Abstract: As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.

Global Carbon Budget 2022

Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples

Abstract: Adaptive sampling is a method of software-controlled enrichment unique to nanopore sequencing platforms. To test its potential for enrichment of rarer species within metagenomic samples, we create a synthetic mock community and construct sequencing libraries with a range of mean read lengths. Enrichment is up to 13.87-fold for the least abundant species in the longest read length library; factoring in reduced yields from rejecting molecules the calculated efficiency raises this to 4.93-fold. Finally, we introduce a mathematical model of enrichment based on molecule length and relative abundance, whose predictions correlate strongly with mock and complex real-world microbial communities.

Recycling of natural fiber composites: Challenges and opportunities

Abstract: Natural fibers have been widely used for reinforcing polymers attributed to their sustainable nature, excellent stiffness to weight ratio, biodegradability, and low cost compared with synthetic fibers like carbon or glass fibers. Thermoplastic composites offer an advantage of recyclability after their service life, but challenges and opportunities remain in the recycling of natural fiber reinforced polymer composites (NFRPCs). This article summarized the effects of reprocessing/recycling on the material properties of NFRPCs. The material properties considered include mechanical performance, thermal properties, hygroscopic behavior, viscoelasticity, degradation, and durability. NFRPCs can generally be recycled approximately 4–6 times until their thermomechanical properties change. After recycling 7 times, the tensile strength of NFRPCs can decrease by 17%, and the tensile modulus can decrease by 28%. The mitigation approaches to overcome degradation of material properties of NFRPCs such as adding functional additives and virgin plastics are also discussed. The main challenges in these approaches such as degradation and incompatibility are discussed, and an effort is made to provide a rationale for reprocessing/recyclability assessment. Future applications of NFRPCs such as additive manufacturing and automotive part use are discussed.

Realizing excellent energy storage properties in Na0.5Bi0.5TiO3-based lead-free relaxor ferroelectrics

Abstract: Development of lead-free dielectric capacitors with high recoverable energy storage density (Wrec), large energy storage efficiency (η), and wide usage temperature range are in high demanded for pulse power systems. Herein, we realized the enhancement of energy storage properties [high Wrec = 3.76 J/cm3, large η = 78.80 %, and broad operating temperature range (20−180 °C)] in lead-free Na0.5Bi0.5TiO3 (BNT)-based relaxor ferroelectrics via component regulation. Excellent energy storage properties mainly originate from suppressing early polarization saturation and improving dielectric breakdown strength (Eb). Domain evolution on the nanoscale offers robust support to suppression of early polarization saturation. The enhancement of Eb can be derived from the contribution of the Mg-rich phase, which is also corroborative via first-principles calculation on basis of density functional theory (DFT). We believe that these findings in this work may provide a practicable guideline to build new lead-free ceramics for electrical energy storage applications.

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Abstract: Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Abstract: Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.

Surface engineering of MXenes for energy and environmental applications

Abstract: This paper reviews the surface engineering, including surface termination groups, surface functionalization, surface defects and surface oxidation, of MXenes, and their impact on energy and environmental applications of MXenes.

Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit

Abstract: The high-temperature superconducting cuprates are governed by intertwined spin, charge, and superconducting orders. While various state-of-the-art numerical methods have demonstrated that these phases also manifest themselves in doped Hubbard models, they differ on which is the actual ground state. Finite-cluster methods typically indicate that stripe order dominates, while embedded quantum-cluster methods, which access the thermodynamic limit by treating long-range correlations with a dynamical mean field, conclude that superconductivity does. Here, we report the observation of fluctuating spin and charge stripes in the doped single-band Hubbard model using a quantum Monte Carlo dynamical cluster approximation (DCA) method. By resolving both the fluctuating spin and charge orders using DCA, we demonstrate that they survive in the doped Hubbard model in the thermodynamic limit. This discovery also provides an opportunity to study the influence of fluctuating stripe correlations on the model's pairing correlations within a unified numerical framework. Using this approach, we also find evidence for pair-density-wave correlations whose strength is correlated with that of the stripes.

MoS2 nanosheet integrated electrodes with engineered 1T-2H phases and defects for efficient hydrogen production in practical PEM electrolysis

Abstract: Low electrical conductivity and poor accessibility of MoS2 reaction sites raise great challenges in maximizing the triple-phase-boundary (TPB) sites of MoS2-based electrodes and minimizing ohmic losses for efficient hydrogen evolution reaction (HER) in practical proton exchange membrane (PEM) water electrolysis. Herein, we report a scalable hydrothermal approach to fabricate ionomer-free integrated electrodes with engineered 1 T-2 H heterophase and defect-rich MoS2 nanosheets (MoS2NSs) in-situ grown onto the carbon fiber paper (CFP). With an ultralow loading of 0.14 mg/cm2, a small voltage of 2.25 V was obtained at 2000 mA/cm2 in a practical cell with Nafion115 membrane, which outperforms all previously reported high-loading non-precious catalyst-based electrodes. Impressively, it shows 44 times higher mass activity than a high-loading and ionomer-mixed MoS2 assemblies electrode. This work builds a bridge from catalyst optimization to electrode fabrication and provides a promising direction for improving intrinsic catalytic activity, electrode conductivity and stability for practical PEM water electrolysis.

Collective neutrino oscillations on a quantum computer

Abstract: We calculate the energy levels of a system of neutrinos undergoing collective oscillations as functions of an effective coupling strength and radial distance from the neutrino source using the quantum Lanczos (QLanczos) algorithm implemented on IBM Q quantum computer hardware. Our calculations are based on the many-body neutrino interaction Hamiltonian introduced in Patwardhan et al. (Phys Rev D 99, https://doi.org/10.1103/PhysRevD.99.123013 , 2019). We show that the system Hamiltonian can be separated into smaller blocks, which can be represented using fewer qubits than those needed to represent the entire system as one unit, thus reducing the noise in the implementation on quantum hardware. We also calculate transition probabilities of collective neutrino oscillations using a Trotterization method which is simplified before subsequent implementation on hardware. These calculations demonstrate that energy eigenvalues of a collective neutrino system and collective neutrino oscillations can both be computed on quantum hardware with certain simplification to within good agreement with exact results.

High plasticity mechanism of high strain rate rolled Mg-Ga alloy sheets

Abstract: By observing the microstructure evolution of Mg-Ga alloy during tensile deformation, it is found that the prismatic slip and the pyramidal slip occur during the tensile process at room temperature, which finally leads to the plenty of dislocation accumulation. After 8% tensile deformation, the{101¯2}extension twin is the main way to coordinate the strain in the c-axis direction for the alloy with the Ga content lower than 2 wt.%, but the pyramidal slip is the main way to coordinate the strain along the c-axis direction for the alloy with the Ga content higher than 2 wt.%. The Ga addition can promote the activation of the non-basal slip, which is beneficial to the work-hardening of the alloy to achieve better plasticity. Dynamic precipitation can slightly reduce the increment of dislocations. The preparation method of high strain rate rolling (HSRR) is another important reason for the plasticity of magnesium alloy sheets, and it is an important embodiment of the application of the dislocation engineering concept in magnesium alloy. The non-basal dislocations derived from the HSRR deformation can provide the non-basal dislocation sources when magnesium alloy is deformed at room temperature, resulting in good ductility. This study can be used as a reference for preparing wrought magnesium alloy with high strength and high plasticity by Ga alloying and hot deformation.

Graphitic Aza‐Fused π‐Conjugated Networks: Construction, Engineering, and Task‐Specific Applications

Abstract: 2D π-conjugated networks linked by aza-fused units represent a pivotal category of graphitic materials with stacked nanosheet architectures. Extensive efforts have been directed at their fabrication and application since the discovery of covalent triazine frameworks (CTFs). Besides the triazine cores, tricycloquinazoline and hexaazatriphenylene linkages are further introduced to tailor the structures and properties. Diverse related materials have been developed rapidly, and a thorough outlook is necessitated to unveil the structure-property-application relationships across multiple subcategories, which is pivotal to guide the design and fabrication toward enhanced task-specific performance. Herein, the structure types and development of related materials including CTFs, covalent quinazoline networks, and hexaazatriphenylene networks, are introduced. Advanced synthetic strategies coupled with characterization techniques provide powerful tools to engineer the properties and tune the associated behaviors in corresponding applications. Case studies in the areas of gas adsorption, membrane-based separation, thermo-/electro-/photocatalysis, and energy storage are then addressed, focusing on the correlation between structure/property engineering and optimization of the corresponding performance, particularly the preferred features and strategies in each specific field. In the last section, the underlying challenges and opportunities in construction and application of this emerging and promising material category are discussed.