Showing papers by "Missouri University of Science and Technology published in 2020"
TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.
1,189 citations
TL;DR: In this paper, the authors reported the observation of a compact binary coalescence involving a 222 −243 M ⊙ black hole and a compact object with a mass of 250 −267 M ⋆ (all measurements quoted at the 90% credible level) The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network.
Abstract: We report the observation of a compact binary coalescence involving a 222–243 M ⊙ black hole and a compact object with a mass of 250–267 M ⊙ (all measurements quoted at the 90% credible level) The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network The source was localized to 185 deg2 at a distance of ${241}_{-45}^{+41}$ Mpc; no electromagnetic counterpart has been confirmed to date The source has the most unequal mass ratio yet measured with gravitational waves, ${0112}_{-0009}^{+0008}$, and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system The dimensionless spin of the primary black hole is tightly constrained to ≤007 Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence We estimate a merger rate density of 1–23 Gpc−3 yr−1 for the new class of binary coalescence sources that GW190814 represents Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries
913 citations
TL;DR: It is inferred that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M⊙, which can be considered an intermediate mass black hole (IMBH).
Abstract: On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85_{-14}^{+21} M_{⊙} and 66_{-18}^{+17} M_{⊙} (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M_{⊙}. We calculate the mass of the remnant to be 142_{-16}^{+28} M_{⊙}, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3_{-2.6}^{+2.4} Gpc, corresponding to a redshift of 0.82_{-0.34}^{+0.28}. The inferred rate of mergers similar to GW190521 is 0.13_{-0.11}^{+0.30} Gpc^{-3} yr^{-1}.
876 citations
TL;DR: In this article, the authors reported the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO and Virgo's third observing run.
Abstract: We report the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO’s and Virgo’s third observing run. The signal was recorded on April 12, 2019 at 05∶30∶44 UTC with a network signal-to-noise ratio of 19. The binary is different from observations during the first two observing runs most notably due to its asymmetric masses: a ∼30 M⊙ black hole merged with a ∼8 M⊙ black hole companion. The more massive black hole rotated with a dimensionless spin magnitude between 0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit gravitational waves with stronger contributions from higher multipoles, and indeed we find strong evidence for gravitational radiation beyond the leading quadrupolar order in the observed signal. A suite of tests performed on GW190412 indicates consistency with Einstein’s general theory of relativity. While the mass ratio of this system differs from all previous detections, we show that it is consistent with the population model of stellar binary black holes inferred from the first two observing runs.
507 citations
TL;DR: The GW190521 signal is consistent with a binary black hole (BBH) merger source at redshift 0.13-0.30 Gpc-3 yr-1.8 as discussed by the authors.
Abstract: The gravitational-wave signal GW190521 is consistent with a binary black hole (BBH) merger source at redshift 0.8 with unusually high component masses, 85-14+21 M o˙ and 66-18+17 M o˙, compared to previously reported events, and shows mild evidence for spin-induced orbital precession. The primary falls in the mass gap predicted by (pulsational) pair-instability supernova theory, in the approximate range 65-120 M o˙. The probability that at least one of the black holes in GW190521 is in that range is 99.0%. The final mass of the merger (142-16+28 M o˙) classifies it as an intermediate-mass black hole. Under the assumption of a quasi-circular BBH coalescence, we detail the physical properties of GW190521's source binary and its post-merger remnant, including component masses and spin vectors. Three different waveform models, as well as direct comparison to numerical solutions of general relativity, yield consistent estimates of these properties. Tests of strong-field general relativity targeting the merger-ringdown stages of the coalescence indicate consistency of the observed signal with theoretical predictions. We estimate the merger rate of similar systems to be 0.13-0.11+0.30 Gpc-3 yr-1. We discuss the astrophysical implications of GW190521 for stellar collapse and for the possible formation of black holes in the pair-instability mass gap through various channels: via (multiple) stellar coalescences, or via hierarchical mergers of lower-mass black holes in star clusters or in active galactic nuclei. We find it to be unlikely that GW190521 is a strongly lensed signal of a lower-mass black hole binary merger. We also discuss more exotic possible sources for GW190521, including a highly eccentric black hole binary, or a primordial black hole binary.
347 citations
TL;DR: A survey of various techniques suggested for compressing and accelerating the ML and DL models is presented and the challenges of the existing techniques are discussed and future research directions in the field are provided.
Abstract: In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. For instance, size of the pre-trained VGG16 model trained on the ImageNet dataset is more than 500 MB. Resource-constrained devices such as mobile phones and internet of things devices have limited memory and less computation power. For real-time applications, the trained models should be deployed on resource-constrained devices. Popular convolutional neural network models have millions of parameters that leads to increase in the size of the trained model. Hence, it becomes essential to compress and accelerate these models before deploying on resource-constrained devices while making the least compromise with the model accuracy. It is a challenging task to retain the same accuracy after compressing the model. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. We have also discussed the challenges of the existing techniques and have provided future research directions in the field.
221 citations
TL;DR: In 2019, the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO), joined by the Advanced Virgo detector, began the third observing run, a year-long dedicated search for gravitational radiation as discussed by the authors.
Abstract: On April 1st, 2019, the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO), joined by the Advanced Virgo detector, began the third observing run, a year-long dedicated search for gravitational radiation. The LIGO detectors have achieved a higher duty cycle and greater sensitivity to gravitational waves than ever before, with LIGO Hanford achieving angle-averaged sensitivity to binary neutron star coalescences to a distance of 111 Mpc, and LIGO Livingston to 134 Mpc with duty factors of 74.6% and 77.0% respectively. The improvement in sensitivity and stability is a result of several upgrades to the detectors, including doubled intracavity power, the addition of an in-vacuum optical parametric oscillator for squeezed-light injection, replacement of core optics and end reaction masses, and installation of acoustic mode dampers. This paper explores the purposes behind these upgrades, and explains to the best of our knowledge the noise currently limiting the sensitivity of each detector.
214 citations
TL;DR: In this paper, the authors provide an overview of the research progress in metallic FGMs fabricated by laser metal deposition (LMD), an AM process that is widely used in metallic materials.
Abstract: Functionally graded materials (FGMs) have attracted much research interest in the industry due to their graded material properties, which result from gradually distributed compositions or structures. In recent years, metallic FGMs have been widely studied, and additive manufacturing (AM) has become an important approach to build metallic FGMs. This paper aims to provide an overview of the research progress in metallic FGMs fabricated by laser metal deposition (LMD), an AM process that is widely used in metallic materials. Firstly, the unique material properties and advantages of FGMs are introduced. Then, typical recent findings in transition path design, fabrication, and characterization for different types of metallic FGMs via LMD are summarized and discussed. Finally, challenges in fabricating metallic FGMs via LMD are discussed, and other related aspects in the area of FGMs such as model representation and numerical simulation are proposed for further investigation.
165 citations
TL;DR: The development and advancement of information technology provide a technical platform for education reform and opportunities for innovation in education as discussed by the authors, and the global COVID-19 pandemic has created a...
Abstract: The development and advancement of information technology provide a technical platform for education reform and opportunities for innovation in education. The global COVID-19 pandemic has created a...
151 citations
TL;DR: In this article, the pore formation mechanism during the laser powder bed fusion (LPBF) process is investigated. And the results provide direct evidence and insight into pore forming mechanisms during the LPBF process, which may guide the development of pore elimination/mitigation approaches.
Abstract: Laser powder bed fusion (LPBF) is a 3D printing technology that can print parts with complex geometries that are unachievable by conventional manufacturing technologies. However, pores formed during the printing process impair the mechanical performance of the printed parts, severely hindering their widespread application. Here, we report six pore formation mechanisms that were observed during the LPBF process. Our results reconfirm three pore formation mechanisms - keyhole induced pores, pore formation from feedstock powder and pore formation along the melting boundary during laser melting from vaporization of a volatile substance or an expansion of a tiny trapped gas. We also observe three new pore formation mechanisms: (1) pore trapped by surface fluctuation, (2) pore formation due to depression zone fluctuation when the depression zone is shallow and (3) pore formation from a crack. The results presented here provide direct evidence and insight into pore formation mechanisms during the LPBF process, which may guide the development of pore elimination/mitigation approaches. Since certain laser processing conditions studied here are similar to the situations in high energy density laser welding, the results presented here also have implications for laser welding.
131 citations
TL;DR: The use of ultra-high performance concrete (UHPC) to strength existing reinforced concrete (RC) structures in flexure has been explored in recent decades as discussed by the authors, and the state of research on the flexural strengthening of RC beams or slabs with UHPC was presented.
TL;DR: This paper will discuss AI ethics by looking at the ethics of AI and ethical AI, an AI that performs and behaves ethically and some of the necessary features and characteristics of an ethical AI.
Abstract: Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept of “machine ethics” was proposed around 2006, AI ethics is still in the infancy stage. AI ethics is the field related to the study of ethical issues in AI. To address AI ethics, one needs to consider the ethics of AI and how to build ethical AI. Ethics of AI studies the ethical principles, rules, guidelines, policies, and regulations that are related to AI. Ethical AI is an AI that performs and behaves ethically. One must recognize and understand the potential ethical and moral issues that may be caused by AI to formulate the necessary ethical principles, rules, guidelines, policies, and regulations for AI (i.e., Ethics of AI). With the appropriate ethics of AI, one can then build AI that exhibits ethical behavior (i.e., Ethical AI). This paper will discuss AI ethics by looking at the ethics of AI and ethical AI. What are the perceived ethical and moral issues with AI? What are the general and common ethical principles, rules, guidelines, policies, and regulations that can resolve or at least attenuate these ethical and moral issues with AI? What are some of the necessary features and characteristics of an ethical AI? How to adhere to the ethics of AI to build ethical AI?
TL;DR: A detailed review of asphaltene properties, characteristics, and previous studies to construct a guideline to asphalte and its impact on oil recovery is provided in this article. But, the review is limited to the use of micro-and nanofluidics.
Abstract: Asphaltene is a component of crude oil that has been reported to cause severe problems during production and transportation of the oil from the reservoir. It is a solid component of the oil that has different structures and molecular makeup which makes it one of the most complex components of the oil. This research provides a detailed review of asphaltene properties, characteristics, and previous studies to construct a guideline to asphaltene and its impact on oil recovery. The research begins with an explanation of the main components of crude oil and their relation to asphaltene. The method by which asphaltene is quantified in the crude oil is then explained. Due to its different structures, asphaltene has been modeled using different models all of which are then discussed. All chemical analysis methods that have been used to characterize and study asphaltene are then mentioned and the most commonly used method is shown. Asphaltene will pass through several phases in the reservoir beginning from its stability phase up to its deposition in the pores, wellbore, and facilities. All these phases are explained, and the reason they may occur is mentioned. Following this, the methods by which asphaltene can damage oil recovery are presented. Asphaltene rheology and flow mechanism in the reservoir are then explained in detail including asphaltene onset pressure determination and significance and the use of micro- and nanofluidics to model asphaltene. Finally, the mathematical models, previous laboratory, and oilfield studies conducted to evaluate asphaltene are discussed. This research will help increase the understanding of asphaltene and provide a guideline to properly study and model asphaltene in future studies.
TL;DR: It is found that sodium ion (Na+)–gated water-conduction nanochannels could be created by assembling NaA zeolite crystals into a continuous, defect-free separation membrane through a rationally designed method.
Abstract: Robust, gas-impeding water-conduction nanochannels that can sieve water from small gas molecules such as hydrogen (H2), particularly at high temperature and pressure, are desirable for boosting many important reactions severely restricted by water (the major by-product) both thermodynamically and kinetically. Identifying and constructing such nanochannels into large-area separation membranes without introducing extra defects is challenging. We found that sodium ion (Na+)–gated water-conduction nanochannels could be created by assembling NaA zeolite crystals into a continuous, defect-free separation membrane through a rationally designed method. Highly efficient in situ water removal through water-conduction nanochannels led to a substantial increase in carbon dioxide (CO2) conversion and methanol yield in CO2 hydrogenation for methanol production.
TL;DR: In this article, Zhao et al. reported in-situ characterization of melt-flow dynamics in every location of the entire melt pool in laser metal additive manufacturing by populous and uniformly dispersed micro-tracers through high-resolution synchrotron x-ray imaging.
Abstract: Melt flow plays a critical role in laser metal additive manufacturing, yet the melt flow behavior within the melt pool has never been explicitly presented. Here, we report in-situ characterization of melt-flow dynamics in every location of the entire melt pool in laser metal additive manufacturing by populous and uniformly dispersed micro-tracers through in-situ high-resolution synchrotron x-ray imaging. The location-specific flow patterns in different regions of the melt pool are revealed and quantified under both conduction-mode and depression-mode melting. The physical processes at different locations in the melt pool are identified. The full-field melt-flow mapping approach reported here opens the way to study the detailed melt-flow dynamics under real additive manufacturing conditions. The results obtained provide crucial insights into laser additive manufacturing processes and are critical for developing reliable high-fidelity computational models.
TL;DR: The results show that adoption of more diverse datasets and consideration of more factors in conventional models can improve their prediction performance, and the ensemble ML model established on a large database, can easily consider the twelve influencing factors in the database, and has superior prediction performance and better time-efficiency compared to conventional models.
TL;DR: It is shown that the ensemble ML model unfailingly produces more accurate predictions compared to standalone models and was used to develop optimal mixture designs for RCA concretes that satisfy imposed target MOE.
TL;DR: Selective hydrogenation of cinnamaldehyde (CAL) is an important reaction because the main products, cinnamic alcohol (COL) and hydrocinnamonaldehyde (HCAL), are used in various fields, such as produc...
Abstract: Selective hydrogenation of cinnamaldehyde (CAL) is an important reaction because the main products, cinnamic alcohol (COL) and hydrocinnamaldehyde (HCAL), are used in various fields, such as produc...
TL;DR: In this paper, the authors perform Bayesian model selection on a wide range of theoretical predictions for the neutron star equation of state, and find that all scenarios from prompt collapse to long-lived or even stable remnants are possible.
Abstract: GW170817 is the very first observation of gravitational waves originating from the coalescence of two compact objects in the mass range of neutron stars, accompanied by electromagnetic counterparts, and offers an opportunity to directly probe the internal structure of neutron stars. We perform Bayesian model selection on a wide range of theoretical predictions for the neutron star equation of state. For the binary neutron star hypothesis, we find that we cannot rule out the majority of theoretical models considered. In addition, the gravitational-wave data alone does not rule out the possibility that one or both objects were low-mass black holes. We discuss the possible outcomes in the case of a binary neutron star merger, finding that all scenarios from prompt collapse to long-lived or even stable remnants are possible. For long-lived remnants, we place an upper limit of 1.9 kHz on the rotation rate. If a black hole was formed any time after merger and the coalescing stars were slowly rotating, then the maximum baryonic mass of non-rotating neutron stars is at most 3.05M⊙, and three equations of state considered here can be ruled out. We obtain a tighter limit of 2.67M⊙ for the case that the merger results in a hypermassive neutron star.
TL;DR: In this paper, a worker-centered system consisting of multi-modal augmented reality (AR) instructions with the support of a deep learning network for tool detection is introduced to reduce time and error in an assembly task.
Scuola Normale Superiore di Pisa1, Swinburne University of Technology2, Missouri University of Science and Technology3, Australian Research Council4, Monash University5, Polish Academy of Sciences6, University of Western Australia7, University of Minnesota8, California Institute of Technology9, Northwestern University10, University of Chicago11, University of Glasgow12, Max Planck Society13, ETH Zurich14, Montclair State University15, University of Paris16, University of Rome Tor Vergata17, University of the Balearic Islands18, Columbia University19, Massachusetts Institute of Technology20, Albert Einstein Institution21, University of Pisa22
TL;DR: In this paper, a review of machine learning techniques for the analysis of ground-based gravitational-wave detector data is presented, including techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational wave searches, methods for fast measurements of the astrophysical parameters of gravitational wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise.
Abstract: Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.
TL;DR: In this article, the features and formation mechanisms of five unique types of spatter during the LPBF process were revealed by in-situ high-speed, high-energy x-ray imaging.
Abstract: Spatter causes defect formation, powder redistribution and contamination in laser powder bed fusion (LPBF) additive manufacturing process It is critical to distinguish different types of spatter and understand their features and formation mechanisms This work reveals the features and formation mechanisms of five unique types of spatter during the LPBF process by in-situ high-speed, high-energy x-ray imaging Spatters observed during LPBF testing are quantified by their speed, size, and direction Distinct quantifiable characteristics for each type of spatter are identified Effects of the laser power, scan speed, and ambient pressure on spatter formation and features are unraveled A spatter formation map for AlSi10Mg alloy is constructed
TL;DR: In this paper, the feasibility of the inter-fracture injection and production (IFIP) method to increase oil production rates of horizontal wells is investigated, and the suggested well completion scheme for the IFIP methods is presented.
Abstract: Well production rates decline quickly in the tight reservoirs, and enhanced oil recovery (EOR) is needed to increase productivity. Conventional flooding from adjacent wells is inefficient in the tight formations, and Huff-n-Puff also fails to achieve the expected productivity. This paper investigates the feasibility of the inter-fracture injection and production (IFIP) method to increase oil production rates of horizontal wells. Three multi-fractured horizontal wells (MFHWs) are included in a cluster well. The fractures with even and odd indexes are assigned to be injection fractures (IFs) and recovery fractures (RFs). The injection/production schedule includes synchronous inter-fracture injection and production (s-IFIP) and asynchronous inter-fracture injection and production (a-IFIP). The production performances of three MFHWs are compared by using four different recovery approaches based on numerical simulation. Although the number of RFs is reduced by about 50% for s-IFIP and a-IFIP, they achieve much higher oil rates than depletion and CO2 Huff-n-Puff. The sensitivity analysis is performed to investigate the impact of parameters on IFIP. The spacing between IFs and RFs, CO2 injection rates, and connectivity of fracture networks affect oil production significantly, followed by the length of RFs, well spacing among MFHWs, and the length of IFs. The suggested well completion scheme for the IFIP methods is presented. This work discusses the ability of the IFIP method in enhancing the oil production of MFHWs.
TL;DR: In this article, a comprehensive literature review of CO2 enhanced oil recovery (CO2-EOR) potential in tight reservoirs is presented, showing that compared with Bakken and Eagle Ford formation, China's tight oil reservoirs feature higher mud content and oil viscosity while they have a lower brittleness index of rock and formation pressure coefficient, leading to confined stimulated reservoir volume and further limited CO2-oil contact.
TL;DR: In this article, a broad set of issues including technologies, material supplies, investment strategies, communal concerns, innovations, modeling considerations, and policy trends are considered to help contextualize policy decisions and regulatory responses.
Abstract: The global transition to a low-carbon economy will involve changes in material markets and supply chains on a hitherto unknown scale and scope. With these changes come numerous challenges and opportunities related to supply chain security and sustainability. To help support decision-making as well as future research, this study employs a problem-oriented perspective while reviewing academic publications, technical reports, legal documents, and published industry data to highlight the increasingly interconnected nature of material needs and geopolitical change. The paper considers a broad set of issues including technologies, material supplies, investment strategies, communal concerns, innovations, modeling considerations, and policy trends to help contextualize policy decisions and regulatory responses. Policy options are outlined for each topical section, as well as areas for further research. Together, these recommendations serve to help guide the complex, interdisciplinary approach to materials required for a low-carbon transition.
TL;DR: The basic concepts on spontaneous emulsification, including mechanisms, methods and influenced parameters, are reviewed, which are relevant for fundamental applications for industrials and the description of importance for emulsions in EOR processes from advances and categories to improving oil recovery mechanisms.
TL;DR: In this article, a flexural test was conducted to investigate crack resistance, loading bearing capacity, deformation characteristics and failure modes of damaged reinforced concrete (RC) beams strengthened by reinforced ultra-high performance concrete (UHPC) layer (Abbreviated as UC).
Abstract: The flexural test was conducted to investigate crack resistance, loading bearing capacity, deformation characteristics and failure modes of damaged reinforced concrete (RC) beams strengthened by reinforced ultra-high performance concrete (UHPC) layer (Abbreviated as UC). Also, their mechanical properties were compared with those of the unstrengthened RC beam (Abbreviated as I–C). Additionally, the effects of pre-damage degrees in the RC beams and three strategies for the improvement of toughness of the reinforced UHPC layer on the cracking and flexural performance of UC were discussed. The results showed that UC acted monolithically under flexure without interfacial debonding before typically flexural failure. Compared with I–C, cracking and ultimate loads of UC increased by 1.57–3.32 times and 1.72–2.21 times, respectively. The reinforced UHPC layer effectively suppressed the cracking of the RC beam, making crack width in the RC beam propagate slowly with the load. Moreover, the severer the pre-damage degree of the RC beam, the smaller improvement of the flexural performance of UC. The addition of steel wire mesh, orientation of steel fibers and moderate-temperature steam curing further improved the cracking and flexural performance of UC with the most evident improvement in the addition of steel wire mesh. Finally, the theoretical model and formula were proposed for the calculation of load bearing capacity of UC with consideration of the influence of the pre-damage degrees in the RC beams, and the experimental results verified that the theoretical formula can accurately predict the load bearing capacity of UC.
TL;DR: In this article, a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437-4715, PSR J0711-6830, and PSRJ0737-3039A), was presented.
Abstract: We present a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437-4715, PSR J0711-6830, and PSR J0737-3039A) and two young pulsars: the Crab pulsar (J0534+2200) and the Vela pulsar (J0835-4510). We use data from the third observing run of Advanced LIGO and Virgo combined with data from their first and second observing runs. For the first time, we are able to match (for PSR J0437-4715) or surpass (for PSR J0711-6830) the indirect limits on gravitational-wave emission from recycled pulsars inferred from their observed spin-downs, and constrain their equatorial ellipticities to be less than 10-8. For each of the five pulsars, we perform targeted searches that assume a tight coupling between the gravitational-wave and electromagnetic signal phase evolution. We also present constraints on PSR J0711-6830, the Crab pulsar, and the Vela pulsar from a search that relaxes this assumption, allowing the gravitational-wave signal to vary from the electromagnetic expectation within a narrow band of frequencies and frequency derivatives.
TL;DR: Overall, these results demonstrate that the pore environment of a metal–organic framework can be designed to afford precise control over the size, structure and spatial arrangement of inorganic clusters.
Abstract: The size-dependent and shape-dependent characteristics that distinguish nanoscale materials from bulk solids arise from constraining the dimensionality of an inorganic structure1–3. As a consequence, many studies have focused on rationally shaping these materials to influence and enhance their optical, electronic, magnetic and catalytic properties4–6. Although a select number of stable clusters can typically be synthesized within the nanoscale regime for a specific composition, isolating clusters of a predetermined size and shape remains a challenge, especially for those derived from two-dimensional materials. Here we realize a multidentate coordination environment in a metal–organic framework to stabilize discrete inorganic clusters within a porous crystalline support. We show confined growth of atomically defined nickel(ii) bromide, nickel(ii) chloride, cobalt(ii) chloride and iron(ii) chloride sheets through the peripheral coordination of six chelating bipyridine linkers. Notably, confinement within the framework defines the structure and composition of these sheets and facilitates their precise characterization by crystallography. Each metal(ii) halide sheet represents a fragment excised from a single layer of the bulk solid structure, and structures obtained at different precursor loadings enable observation of successive stages of sheet assembly. Finally, the isolated sheets exhibit magnetic behaviours distinct from those of the bulk metal halides, including the isolation of ferromagnetically coupled large-spin ground states through the elimination of long-range, interlayer magnetic ordering. Overall, these results demonstrate that the pore environment of a metal–organic framework can be designed to afford precise control over the size, structure and spatial arrangement of inorganic clusters. The pore space in the metal–organic framework Zr6O4(OH)4(bpydc)6 can be used as a scaffold to grow precisely defined atomically thick sheets of metal halide materials, taking advantage of multiple binding sites to direct complexation of the metal ions; these metal halide nanosheets fill the size gap between discrete molecular magnets and bulk magnetic materials, with potentially unusual magnetic properties arising from this size regime.
TL;DR: A facile, label-free, and ultrasensitive electrochemical DNA biosensor has been developed, based on "urchin-like" carbon nanotube-gold nanoparticle (CNT-AuNP) nanoclusters, for signal amplification.
Abstract: Nanomaterials have been extensively utilized in biosensing systems for highly sensitive and selective detection of a variety of biotargets. In this work, a facile, label-free, and ultrasensitive electrochemical DNA biosensor has been developed, based on "urchinlike" carbon nanotube-gold nanoparticle (CNT-AuNP) nanoclusters, for signal amplification. Specifically, electrochemical polymerization of dopamine (DA) was employed to modify a gold electrode for immobilization of DNA probes through the Schiff base reaction. Upon sensing the target nucleic acid, the dual-DNA (reporter and linker) functionalized AuNPs were introduced into the sensing system via DNA hybridization. Afterward, the end-modified single-wall carbon nanotubes with DNA (SWCNT-DNA) were attached to the surface of the AuNPs through linker-DNA hybridization that formed 3D radial nanoclusters, which generated a remarkable electrochemical response. Because of the larger contact surface area and super electronic conductivity of CNT-AuNP clusters, this novel designed 3D radial nanostructure exhibits an ultrasensitive detection of DNA, with a detection limit of 5.2 fM (a linear range of from 0.1 pM to 10 nM), as well as a high selectivity that discriminates single-mismatched DNA from fully matched target DNA under optimal conditions. This biosensor, which combines the synergistic properties of both CNTs and AuNPs, represents a promising signal amplification strategy for achieving a sensitive biosensor for DNA detection and diagnostic applications.