Engineering oxygen vacancy formation and distribution is a powerful route for controlling the oxygen sublattice evolution that affects diverse functional behavior. The controlling of the oxygen vacancy formation process is particularly important for inducing topotactic phase transitions that occur by transformation of the oxygen sublattice. Here we demonstrate an epitaxial nanocomposite approach for exploring the spatial control of topotactic phase transition from a pristine perovskite phase to an oxygen vacancy-ordered brownmillerite (BM) phase in a model oxide La 0.7 Sr 0.3 MnO 3 (LSMO). Incorporating a minority phase NiO in LSMO films creates ultrahigh density of vertically aligned epitaxial interfaces that strongly influence the oxygen vacancy formation and distribution in LSMO. Combined structural characterizations reveal strong interactions between NiO and LSMO across the epitaxial interfaces leading to a topotactic phase transition in LSMO accompanied by significant morphology evolution in NiO. Using the NiO nominal ratio as a single control parameter, we obtain intermediate topotactic nanostructures with distinct distribution of the transformed LSMO-BM phase, which enables systematic tuning of magnetic and electrical transport properties. The use of self-assembled heterostructure interfaces by the epitaxial nanocomposite platform enables more versatile design of topotactic phase structures and correlated functionalities that are sensitive to oxygen vacancies.
Background Biochar ozonization was previously shown to dramatically increase its cation exchange capacity, thus improving its nutrient retention capacity. The potential soil application of ozonized biochar warrants the need for a toxicity study that investigates its effects on microorganisms. Results In the study presented here, we found that the filtrates collected from ozonized pine 400 biochar and ozonized rogue biochar did not have any inhibitory effects on the soil environmental bacteria Pseudomonas putida, even at high dissolved organic carbon (DOC) concentrations of 300 ppm. However, the growth of Synechococcus elongatus PCC 7942 was inhibited by the ozonized biochar filtrates at DOC concentrations greater than 75 ppm. Further tests showed the presence of some potential inhibitory compounds (terephthalic acid and p -toluic acid) in the filtrate of non-ozonized pine 400 biochar; these compounds were greatly reduced upon wet-ozonization of the biochar material. Nutrient detection tests also showed that dry-ozonization of rogue biochar enhanced the availability of nitrate and phosphate in its filtrate, a property that may be desirable for soil application. Conclusion Ozonized biochar substances can support soil environmental bacterium Pseudomonas putida growth, since ozonization detoxifies the potential inhibitory aromatic molecules. Graphical Abstract
Background China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. Methods We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. Results 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 Mg·ha‒1) and 64 years (232.04 Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 Tg C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. Conclusions This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.
Magnetic semimetals are very promising for potential applications in novel spintronic devices. Nevertheless, realizing tunable topological states with magnetism in a controllable way is challenging. Here, we report novel magnetic states and the tunability of topological semimetallic states through the control of Eu spin reorientation in Eu1−xSrxMn1−zSb2. Increasing the Sr concentration in this system induces a surprising reorientation of noncollinear Eu spins to the Mn moment direction and topological semimetallic behavior. The Eu spin reorientations to distinct collinear antiferromagnetic orders are also driven by the temperature/magnetic field and are coupled to the transport properties of the relativistic fermions generated by the 2D Sb layers. These results suggest that nonmagnetic element doping at the rare earth element site may be an effective strategy for generating topological electronic states and new magnetic states in layered compounds involving spatially separated rare earth and transition metal layers.
The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.
Background Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. Results This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration’s Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. Conclusions To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.
This paper describes a system for automated identification of the optimal stable cutting parameters in milling through Bayesian machine learning and closed-loop control. The closed-loop control system consists of a process monitoring architecture, an analysis framework, and a feedback mechanism. The analysis framework consists of a Bayesian machine learning algorithm that learns a stability map given test results. The learned stability map is used to select parameters for stability testing using an expected improvement in the material removal rate criterion. The test parameters are communicated to the machine controller to complete the test cut through a feedback mechanism. The test cuts were monitored using an audio signal; the stability of the test cut was determined by analyzing the frequency content of the audio signal. The test result was fed back to the Bayesian learning algorithm to complete the loop. Experimental results demonstrate that the system can identify the optimal stable parameters without information about the cutting force model or the structural dynamics. The system provides a low-cost method for optimal stable parameter identification in an industrial environment.
Topological semimetals are a frontier of quantum materials. In multiband electronic systems, topological band crossings can form closed curves, known as nodal lines. In the presence of spin–orbit coupling and/or symmetry-breaking operations, topological nodal lines can break into Dirac/Weyl nodes and give rise to interesting transport properties, such as the chiral anomaly and giant anomalous Hall effect. Recently, the time-reversal symmetry-breaking induced Weyl fermions are observed in a kagome-metal Co 3 Sn 2 S 2 , triggering interests in nodal-line excitations in multiband kagome systems. Here, using first-principles calculations and symmetry-based indicator theories, we find six endless nodal lines along the stacking direction of kagome layers and two nodal rings in the kagome plane in nonmagnetic Ni 3 In 2 S 2 . The linear dipsersive electronic structure, confirmed by angle-resolved photoemission spectroscopy, induces large magnetoresistance up to 2000% at 9 T. Our results establish a diverse topological landscape of multiband kagome metals.
High entropy alloys (HEAs) are promising materials for various applications including nuclear reactor environments. Thus, understanding their behavior under irradiation and exposure to different environments is important. Here, two sets of near-equiatomic CoCrCuFeNi thin films grown on either SiO 2 /Si or Si substrates were irradiated at room temperature with 11.5 MeV Au ions, providing similar behavior to exposure to inert versus corrosion environments. The film grown on SiO 2 had relatively minimal change up to peak damage levels above 500 dpa, while the film grown on Si began intermixing at the substrate–film interface at peak doses of 0.1 dpa before transforming into a multi-silicide film at higher doses, all at room temperature with minimal thermal diffusion. The primary mechanism is radiation-enhanced diffusion via the inverse Kirkendall and solute drag effects. The results highlight how composition and environmental exposure affect the stability of HEAs under radiation and give insights into controlling these behaviors.
This manuscript further develops a recent methodology, denoted by Physics-guided Coverage Mapping (PCM), to support model validation for neutronic depletion calculations. The overarching goal of model validation is to develop confidence in model predictions for the application of interest via fusion of both simulation results and measurements from scaled-down experiments, and whenever possible to improve predictions by explaining the observed discrepancies. This manuscript focuses on the isotopic depletion problem, that is how to improve the predictions of depleted fuel isotopic across the range of expected burnup based on a limited number of post-irradiation measurements. PCM employs an information theoretic approach, capable of directly transferring, i.e., without performing model inversion, biases and their uncertainties from the available measurements to the quantities of interest (QoIs), representing the isotopic concentrations at different burnup values and/or different irradiation spectra. It precludes the need for sensitivity coefficients and only requires forward model executions, and can be applied using non-informative priors, often required by Bayesian-based methods. This is achieved via a mapping kernel relating a number of predictor variables, the concentrations of single or multiple isotopes at certain burnup, to the QoIs, the isotopic concentrations at target burnup such as end of life. Proof-of-principle calculations are demonstrated using both representative PWR and BWR lattice models, where the goal is to employ measurements at given burnup value(s) from one lattice to predict the isotopic concentrations across burnup for the same or the other lattice. Results show 50% to 90% reduction in uncertainties of isotopic concentrations across burnup as compared to the prior uncertainty.
Many nuclear facilities, such as spent fuel storage dry casks and nuclear reactor pressure vessels, are entirely sealed by metal layers to prevent harmful radiation. For safety and security operations, the temperature, pressure, radiation, and humidity inside the vessel needs to be closely monitored. However, no practical technology is currently available to realize the through-wall data communication and monitoring for these vessels due to the inside harsh environment of high temperature and nuclear radiation. In this paper, an innovative self-powered wireless through-wall data communication system for the nuclear environment is presented, which demonstrates a successful solution to such challenges. The presented system is composed of four modules, i.e., energy harvester with power management circuits, ultrasound wireless communication using high-temperature piezoelectric transducers, electronic circuits for sensing and data transmission, and radiation shielding for electronics. Constitutive functions of each module were firstly designed and followed by the system integration. Experiments were conducted subsequently to validate the designed functions and evaluate the performance of the integrated system. Results showed that the average power of over 40 mW was harvested from the thermal flow inside the nuclear spent fuel canisters which could provide enough energy to operate the sensing and data communication systems. The gamma radiation test results showed that the thermoelectric energy harvester and ultrasound transceivers can withstand radiation dosing over 100 Mrad. Furthermore, temperature shock tests demonstrated that the entire system including the shielded electronics can survive and maintain their functionalities at temperatures as high as 195℃. Under the in-lab mocked-up high temperature conditions and radiation shielding, the proposed system is foreseen to survive and operate stably for fifty years inside a nuclear spent fuel canister, and send the frequency modulated data out of the canister for 3 s in every 10 min.
An abundant source of CH4 can be found in natural hydrate deposits. Recent demonstration of CH4 recovery from hydrates via CO2 exchange has revealed the potential as a fuel source that also provides a medium for carbon sequestration. It is vital to understand the structural and dynamic impacts of guest variation in CH4, CO2, and mixed hydrates and link the results to the stability of various deposits in nature, harvesting methane, and sequestering CO2. Molecular vibrations are examined in CH4, CO2, and mixed CH4-CO2 hydrates at 5 and 190 K and Xe hydrates for comparison. Inelastic neutron scattering (INS) is an ideal spectroscopy technique to observe the dynamic modes in the hydrate structure and enclathrated CH4, as it is extremely sensitive to ¹H. The presence of CO2 in hydrates tightens the lattice. It introduces more active librational modes to the host lattice, while hindering the motion of CH4 in mixed CH4-CO2 hydrate at 5 K. At 190 K, a large broadening of the CH4 librational modes indicates disorder in the structure leading to dissociation.
We report an ion-irradiation study of a compositionally complex (high-entropy) pyrochlore oxide. The damage produced from 4 MeV Au²⁺ ion irradiation on single crystal (Yb0.2Tm0.2Lu0.2Ho0.2Er0.2)2Ti2O7 aligned along the  direction is investigated at room temperature by Rutherford backscattering spectrometry in channeling mode (RBS/C). Damage profiles based on RBS/C are presented and compared to single-component pyrochlore titanate oxides to evaluate the relative resistance to irradiation-induced amorphization. The results show that this high-entropy pyrochlore goes amorphous at a dose of 0.13 dpa, which is comparable to that of single-component pyrochlores previously studied. Transmission electron microscopy images unveil the damaged surface layer, which is consistent with the RBS/C results.
Scale formation that drastically increases thermal resistance and reduces freshwater production remains a critical challenge in thermal desalination. Novel designs of falling film evaporator and optimal operating condition hold great promise to mitigate scale formation, and increase heat transfer performance and fresh water production. In this work, CFD simulation based machine learning and multi-objective optimization are performed to identify optimal conditions and tube arrangement for evaporator. Non-dominated sorting genetic algorithm is adopted to determine and analyze the optimal pareto front for multiple objectives in desalination criteria. The errors of training, validation, and testing set are computed to identify an optimal hyperparameter set. For performance ratio, fouling resistance, and water production rate, the average relative error is 2.26%, 3.67%, and 3.24%. At pareto front, both performance ratio and water production rate increase at high temperature with fouling resistance (thermal resistance of the fouling layer) increasing as well. Tradeoffs between mitigating scale formation and enhancing desalination performance are evaluated in optimizations for different objectives. Potential optima are identified and can be applied as guidelines to determine evaporator design and system operating conditions.
Time-varying voltage flicker caused by non-linear and fluctuating loads have adverse impact on the power grid and lighting equipment. This paper proposes a time-varying voltage flicker analysis method based on analytic-adaptive variational mode decomposition (AAVMD). A novel improved square demodulation method based on analytic mode decomposition (AMD), is proposed to quickly extract the time-varying flicker envelope signals, which simplify the computational process of voltage flicker without the analog filter. Then, the adaptive variational mode decomposition (AVMD), using the energy loss coefficient and energy difference as a criterion for determining the number of modal decompositions, is proposed to detect the time-varying voltage flicker envelope which effectively avoids the pattern confusion problem of Hilbert-Huang transform (HHT). The accuracy and validity of the proposed method are demonstrated in a National Instrument-PXI system under the conditions of single frequency flicker modulation, time-varying flicker modulation magnitude, multi-frequency flicker modulation, fundamental frequency deviation and white noise interference.
Dynamic pH change promoted by biogeochemical reactions in Arctic tundra soils can be a major control on the production and release of CO2 and CH4, which contribute to rising global temperatures. Large quantities of soil organic matter (SOM) in these soils are susceptible to microbial decomposition, leading to pH changes during permafrost thaw. Soil pH buffering capacity (β) modulates the extent of pH change but has not been thoroughly studied and represented in predictive ecosystem scale biogeochemical models in Arctic tundra soils. In this study, we generated titration curves for 21 acidic tundra soils from three Arctic sites across northern Alaska, United States of America. Geochemical and hydrological soil properties were evaluated, and correlations with β were developed. Strong correlations between β and both gravimetric water content (Θg) (R² = 0.847, p < 0.001) and soil water retention (SWR) (R² = 0.849, p = 0.001) indicate that the ability of soil to retain water could be associated with its buffering properties. Correlations between β and soil organic carbon (SOC) and cation exchange capacity (CEC) were also explored, and relationships to SWR are discussed. These correlations were then used with existing soil databases reporting SOC, CEC, and SWR to estimate β across Alaska soils. We further demonstrated the quantitative relationships between β and the simulated rates of biogeochemical reactions and show that lower β leads to higher soil pH and more CH4 production. Our study provides simple proxies for β in Arctic soils and highlights the importance and implications of representing soil buffering in predictive models, thereby enabling quantitative coupling between pH dynamics associated with biogeochemical reactions. Integrating β into predictive models of Arctic biogeochemical cycling may reduce model uncertainty and further our understanding of permafrost SOM degradation accelerated by warming.
This study is focused on producing biofuels from waste oils via a tandem vapor-phase hydrotreating process in a pressurized two-stage fixed bed reactor over a bifunctional Ni/Al2O3-SiO2 catalyst under 0.25 MPa H2. A 100% hydrodeoxygenation efficiency both in liquid and gas products was observed, yielding 83.9 wt% C5-C19 n-alkanes which corresponds to a 7.4-fold increase compared with that obtained from non-catalytic conversion. The hydrodeoxygenation mechanism of waste cooking oil in the catalytic tandem hydrotreating process induced by Ni/Al2O3-SiO2 was proposed. The hydropyrolysis temperature in the first reactor, hydrogenation temperature in the second reactor, reaction pressure, catalyst to waste cooking oil mass ratio, and gas hourly space velocity (GHSV) was optimized at 550 °C, 300 °C, 0.25 MPa, 3, and 56 s⁻¹, respectively. The application potential of this cascade vapor-phase hydrotreating process was evaluated by employing different waste oils such as palm kernel oil, woody oil (swida wilsoniana), soapstock, and waste lubricating oil as feedstock, giving C5 to C19 n-alkane yields ranging from 21.6 to 87.5 wt%. This work provides a novel and promising approach to upcycle waste oils into upgraded biofuels compared with conventional catalytic pyrolysis.
Material extrusion additive manufacturing is prone to introducing porosity within the structure due to the layer-by-layer construction using elliptical beads of material. This open porosity ultimately plays a role in determining the mechanical properties of printed parts. The shape, size, and amount of porosity within a printed part is influenced by a variety of factors, including nozzle diameter, infill percentage, layer height, raster orientation, and print speed. While several studies have investigated these and other parameters’ effects on mechanical performance and porosity, better understanding the interconnected relationships is crucial in balancing the various input parameters to achieve maximum strength. This work initially examined the influence of key print parameters (infill percentage and layer height) on the internal porosity of a printed Acrylonitrile Butadiene Styrene (ABS) part. Then, the print parameters and internal porosity were statistically correlated to final mechanical properties. Porosity was further classified as either open or closed to differentiate between connected voids in the mesostructure from isolated voids within the material itself. Mechanical performance increased with an increasing density and infill percentage, displaying a 224 % increase in elastic modulus and a 150 % increase in ultimate tensile strength. The contribution of layer height was found to be conditional upon the infill percentage.
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