In the past 17 years, the larger‐scale production of graphene and graphene family materials has proven difficult and costly, thus slowing wider‐scale commercial applications. The quality of the ...graphene that is prepared on larger scales has often been poor, demonstrating a need for improved quality controls. Here, current industrial graphene synthetic and analytical methods, as well as recent academic advancements in larger‐scale or sustainable synthesis of graphene, defined here as weights more than 200 mg or films larger than 200 cm2, are compiled and reviewed. There is a specific emphasis on recent research in the use of flash Joule heating as a rapid, efficient, and scalable method to produce graphene and other 2D nanomaterials. Reactor design, synthetic strategies, safety considerations, feedstock selection, Raman spectroscopy, and future outlooks for flash Joule heating syntheses are presented. To conclude, the remaining challenges and opportunities in the larger‐scale synthesis of graphene and a perspective on the broader use of flash Joule heating for larger‐scale 2D materials synthesis are discussed.
Although studied for decades, 2D materials have often struggled to surpass small scales. Recent progress in the larger‐scale synthesis of graphene represents an exciting paradigm shift. An overview of scalable graphene powder and film production is provided, addressing industrial and academic trends, and specifically highlighting flash Joule heating methods.
•Establishment of the relationship between main and aftershock intensities through statistical analysis, laying the foundation for subsequent probabilistic assessments.•Introduction of a multi- ...dimensional fragility analysis method based on vine-copula correlation analysis, circumventing the repetitive calculations and the linear fitting errors associated with traditional fragility analyses.•Conducting a multi-dimensional fragility analysis in the context of nuclear power plants, quantifying the impact of aftershocks on structural fragility and assessing the influence of mainshocks on aftershock fragility.
Seismic resilience of critical infrastructure, such as nuclear power plants, is paramount in ensuring nuclear safety. This study presents a comprehensive analysis of the seismic fragility of nuclear power plants under sequential earthquakes, employing the innovative vine-copula theory. The methodology integrates advanced modeling techniques, including layered shell elements and plastic damage softening constitutive modeling, to capture the intricate behavior of nuclear power plants under seismic loading. The seismic sequence is derived from the Wenchuan earthquake data, considering both mainshocks and aftershocks. A set of random seismic peak ground accelerations (PGAs) is generated based on the distribution of giant earthquake PGAs. Utilizing seismic attenuation theory, corresponding random aftershock PGAs are generated. The resulting mainshock-aftershock sequence, modulated within the real seismic sequence, serves as the input for numerical simulations. The vine-copula theory is employed for multi-dimensional fragility analysis, providing a flexible framework to model the complex nonlinear dependencies among structural response parameters. The vine-copula model is applied to fit seismic response data, allowing the construction of fragility surfaces under sequential earthquakes. This approach, rooted in performance-based earthquake engineering (PBEE), enables a more realistic representation of the seismic risk profile. The findings demonstrate that seismic fragility trends for nuclear power plants increase with higher mainshock and aftershock intensity measures (IMs). The impact of aftershocks on the structural performance, often overlooked in traditional studies, is elucidated through the proposed methodology. The study contributes valuable insights into nuclear safety assessments by quantifying the influence of sequential earthquakes on the fragility of nuclear power plants.
•The paper is a comprehensive review of advances in various stages of SHM.•It focuses on execution of recent wireless DAQ’s and AI resources in RCC strcutures.•It also indicates lag in execution of ...SHM techniques despite advanced researches.
Structural Health Monitoring is gaining popularity in recent times because of advancements in technology and the increasing need for repair and rehabilitation. The shift from conventional wired technologies to advanced wireless technologies is also gradually increasing in the past decade. These sensor networks are economical when used for monitoring huge structures with high design life and safety requirements like highway and roadway bridges, multi-story buildings, chimneys, offshore platforms, and nuclear reactors. Smart sensors when paired along with Artificial Intelligence tools like Artificial Neural Networks, Machine Learning, Deep Learning, and its derivatives Convolutional Neural Networks, Hybrid Intelligence, Cloud Computing make the monitoring system completely automated. This paper is a comprehensive review of advances in data acquisition, processing, diagnosis, and retrieval stages of Structural Health Monitoring both academically and commercially. The review primarily focuses on the recently used wireless data acquisition system and execution of AI resources for data prediction and data diagnosis in RCC buildings and bridges. The review also indicates the lag in real-world execution of structural health monitoring technologies despite advances in academia and insists on the development of standards to gel the gap.
Volatile nuclear wastes, such as iodine, have received worldwide attention because it poses risks to public safety and pollutes the environment. The efficient capture of radioactive iodine is of ...vital importance for the safe utilization of nuclear power. Herein, we report a series of stable covalent organic framework (COF) materials with high efficiency to capture radioactive iodine species. Results indicated that all COFs showed high iodine adsorption, which reached up to 5.82 g g
in vapor and 99.9 mg g
in solution, suggesting that all COFs can be an effective potential adsorbent for the removal of iodine. Furthermore, all COFs are renewable due to the excellent recycling performance. Moreover, all COFs are suitable for large-scale synthesis at room temperature, which have potential for practical applications. Theoretical calculations were also performed to analyze the relationship between iodine molecules and COFs, offering mechanisms underlying the potent adsorption abilities of COFs.
•An analysis model that can analyze accidents or events from the viewpoint of nuclear safety culture was proposed.•Events that had occurred at nuclear power plants for the past six years were ...analyzed through a nuclear safety culture review process.•After accdent analysis modeling was completed, conditional probabilities were calculated for nodes based on the analysis data from the plant events.•Sensitivity analysis was performed on the attributes of nuclear safety culture classified into the individual, manager and system layers, discovering which safety culture attributes are more effective in preventing accidents at nuclear power plants.
As technology advances over time, systems and processes in industries have been becoming increasingly complex, and the likelihood of errors has been increasing in those work activities that workers perform without a proper understanding of them. In such an environment, a minor error in combination with weak barrier systems could lead to an unexpected accident. If a human error induced accident has failed to be prevented from occurring despite multiple barriers in place, then we should look for problems underlying in our organization. In the nuclear industry, however, accident cause analyses have been mostly focused on failed equipment, improper designs, or personal mistakes. In order to prevent recurrence, detailed cause analysis and timely corrective actions are crucial. It is also necessary to disseminate the lessons learned (i.e. operating experience) from accident analyses throughout the organization. High-reliability industries have experienced numerous near-misses and minor problems that might have otherwise resulted in major accidents. Due to organizational culture, however, lessons learned from such issues were often overlooked or considered insignificant to ensure that weaknesses could be identified in terms of safety culture (or organizational management). Therefore, methods are necessary to help analyze accidents in terms of safety culture. This paper proposes an accident analysis model based on a Bayesian network that can be used to quantitatively analyze root causes of an accident (or event) occurring at a nuclear power plant from the perspective of nuclear safety culture rather than equipment. For the purpose of this study, a case study was conducted on those actual events that had occurred at nuclear power plants. Sensitivity analysis was performed on the safety culture attributes categorized into individual, manager and system layers, which discovered more effective attributes for preventing accidents at nuclear power plants. If the analysis results of accidents (or events) at nuclear power plants based on this model are collected in an organization's database, the data may be used to monitor nuclear safety culture degradation signs and contribute to enhanced plant safety.
Adenine base editors (ABEs) catalyze A-to-G transitions showing broad applications, but their bystander mutations and off-target editing effects raise safety concerns. Through structure-guided ...engineering, we found ABE8e with an N108Q mutation reduced both adenine and cytosine bystander editing, and introduction of an additional L145T mutation (ABE9), further refined the editing window to 1-2 nucleotides with eliminated cytosine editing. Importantly, ABE9 induced very minimal RNA and undetectable Cas9-independent DNA off-target effects, which mainly installed desired single A-to-G conversion in mouse and rat embryos to efficiently generate disease models. Moreover, ABE9 accurately edited the A
position of the protospacer sequence in pathogenic homopolymeric adenosine sites (up to 342.5-fold precision over ABE8e) and was further confirmed through a library of guide RNA-target sequence pairs. Owing to the minimized editing window, ABE9 could further broaden the targeting scope for precise correction of pathogenic single-nucleotide variants when fused to Cas9 variants with expanded protospacer adjacent motif compatibility. bpNLS, bipartite nuclear localization signals.
Partial oxidation of methane (POM) offers a promising option to produce syngas for downstream processes such as hydrogen production and Fischer-Tropsch processes. POM in fixed-bed reactors requires ...an oxygen separation plant with high operation cost and safety risks. On the contrary, membrane reactors can provide an improved process by integrating both oxygen separation and catalytic reaction processes. With many advantages including high purity and efficient oxygen separation from the air at the catalytic reaction conditions, mixed ionic-electronic conducting membranes (MIEC) caught great attention in the scientific research field over the past two decades. In this review, POM using different catalysts in fixed-bed reactors was firstly summarized with emphasizing on perovskite-based catalysts, and then the material screening of MIEC membrane reactors was introduced and linked to the selection of conventional and perovskite catalysts. The catalytic activity, reaction mechanisms, and emerging challenges have been analyzed. Furthermore, future research directions have been outlined by highlighting the effect of electronic properties, continuous reduction-oxidation in the presence of oxygen flux, and chemical reaction mechanism on membrane/catalyst.
In this article, proton, neutron, and gamma-ray shielding competences of six glasses with PbO or PbO/Bi2O3 heavy metal oxides namely as Glass 1 to Glass 6 have been investigated via WinXcom and ...EXABCal computer codes. The maximum values of μm were equal to 50.6, 53.06, 72.51, 80.67, 99.54, and 102.35 cm2 g-1 at 0.015 MeV for Glass 1 to Glass 6, respectively. The μm of the glass's trend in ascending order for Glass 1 to Glass 6. Results of HVL and MFP of the glass's were trend in descending order for Glass 1 to Glass 6. The Zeff of glasses varied from 14.49 (1.5 MeV) to 37.78 (0.1 MeV) and from 43.93 (2 MeV) to 65.62 (0.02 MeV) for Glass 1 and Glass 6, respectively. The f-factor of Glass 5 and Glass 6 was maximum for throughout the energy spectrum. The buildup factors are low and grow with the penetration depth rapidly up to the maximum depth of 40 mfp. The values of ΣR were varied from 0.0947 to 0.1155 cm−1 for Glass1 to Glass 6, respectively. Glass 2 and Glass 6 are preferred fast neutron shield when compared with ordinary concrete, water, and graphite. Generally, the molar concentration of PbO or PbO + Bi2O3 improved the shielding capacity of the investigated glasses.
Computational codes that simulate steady-states and transients in nuclear reactors are key to supporting the safety of nuclear power plants not only for today’s, but also for future projects, such as ...SMRs. At the same time, they contribute to increasing efficiency by using the project reserves of power plants. Computational tools perform simulation of various physical phenomena of a nuclear reactor. One of the advanced simulation tools are subchannel codes. The subchannel analysis approach is a proven method for the determination of safety criteria margins, resulting in key thermohydraulic parameters of the nuclear reactor, such as the departure from the nucleate boiling ratio. This research simulated a steady state and transient event (loss of flow accident) in the pressurized water reactor VVER-1000, using the codes SubChanFlow 3.5 and VIPRE-01 for one fuel assembly. The results were subsequently compared as a benchmark. Boundary conditions were calculated using data from the VVER-1000 nuclear power plant model in TRACE code. In general, SubChanFlow has been shown to be more conservative than VIPRE and can be used to evaluate and compare future analysis of various transients. Two independent models were developed to simulate the LOFA scenario, taking into account code differences. In general, the differences in the results can be explained by the different approaches of the crossflow models in the subchannel codes. Nevertheless, the departure from nucleate boiling ratio was calculated using the OKB correlation and the results did not exceed the safety limit criteria.
•Benchmark of two subchannel codes VIPRE and SubChanFlow.•Analysis of Loss of Flow Accident for VVER-1000.•Use of OKB correlation.
•Four transient identification models based on machine learning are developed for a liquid-fueled MSR system.•The transient identification models are trained, optimized and validated with the ...generated datasets.•The robustness of the transient identification models is tested under different levels of Gaussian white noise.
Safety is the most important aspect of nuclear power plants. Rapid identification and effective prevention of accidents in nuclear reactor system is a significant method to enhance the safety of the current fleet of reactors. Machine learning (ML) has been introduced in engineering applications of nuclear power plants and is becoming increasingly practical and powerful in recent years. Consequently, ML can also benefit rapid transient identification in nuclear power plants. The feasibility of ML-based identification models to identify transient events in liquid-fueled Molten Salt Reactor (MSR) is presented. Four transient identification models based on recurrent neural network (RNN), support vector machine (SVM), decision tree (DT) and k-nearest neighbor (KNN) were developed and validated. RELAP5-TMSR code was used to generate datasets including eleven operation conditions, and these datasets were used to train, optimize, and validate the identification models. Four metrics including accuracy, precision, recall and F1 score were utilized to evaluate all four identification models. Moreover, the robustness of the models under noise was tested. The four ML-based models were successfully applied to transient identification of liquid-fueled MSRs. The KNN-based model has the best performance and achieves high test scores under noise. In the future, these proposed intelligent identification models will have good potential and prospects in supporting the operation of nuclear power plants.
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