Growth and Characterization of Novel Scintillation Materials Project #: 22-161 Year 1 of 1 (feasibility study)
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This poster presents an overview of the general methodology to be implemented in Task 9.0, Advanced Modeling Support of CDR Pilot Projects, of the Carbon Dioxide Removal program (CDR FWP22). Specifically, the poster presents the multi-scale modelling framework and optimization approaches to develop DAC systems, and how this can support bench/pilot scale tests that improve data collection and minimize uncertainty for scale-up of DAC technologies.
This poster presents results of Task 9.0, Advanced Modeling Support of CDR Pilot Projects, of the Carbon Dioxide Removal program (CDR FWP23). Specifically, the poster presents the multi-scale modelling framework to investigate alternate processes configuration for DAC applications including Vacuum-Assisted Temperature Swing Adsorption (TVSA) process and sweep gas for regeneration.
In recent years several accelerator facilities have been limiting their beam power output not due to constraint of the accelerators but by concerns on the survivability of their targets, which in turn has led to R&D efforts into new, more resistant materials. One of these novel materials are High Entropy Alloys, the main focus of the internship project. This paper details the activities performed during the CCI 10-week internship by the authors, as well as to discuss the results obtained from said internship. The project consisted of the physical characterization of 12 batches of High Entropy Alloys (HEAs); the methods used to analyze the materials were Nanoindentation, which allows to determine the elastic modulus of the material and its hardness, and Differential Scanning Calorimetry (DSC), allowing the determination of specific heat.
There is currently no widely agreed detailed general method for licensing a novel plant incorporating novel materials (or materials being deployed in novel environments); in many such situations, there are no directly applicable engineering code cases for decision-makers (including regulators) to rely on. It may be possible to develop an approach to licensing such a plant that is based on the Reliability and Integrity Management (RIM) approach delineated in ASME BPVC Section XI Division 2. NRC Regulatory Guide 1.246, Rev.0, endorses, with conditions, the subject portion of the ASME Code. But that portion of the ASME code is written at a very high level, and there are fundamental technical challenges associated with applying it literally to licensing a real plant. In the RIM approach, applicants need to do the following (among other things): • Allocate target reliabilities to structures, systems, and components (SSCs) that collectively support the top-level plant safety and availability objectives; • Understand failure modes of those SSCs, and the degradation mechanisms that could lead to those failure modes; • Develop and propose a program of surveillances that will identify degradation prior to failure of SSCs; • Provide a means of reporting results, taking actions for anomalous or undesirable conditions, and give the regulator assurance of continued safe operations. Applying RIM in a specific case will call for advances in the state of practice of relating physical observables to functional reliability of certain component types. Except where a given level of damage corresponds to a failed (or nearly failed) state, it is not practical to establish a precise quantitative relationship between physical observables and reliability. This paper focuses on managing risk of passive component failures based on observable damage parameters. A simple approach to cumulative damage modeling will be illustrated with a view to possible use in RIM applications.
Abstract Molecular beam epitaxy (MBE), a workhorse of the semiconductor industry, has progressed rapidly in the last few decades in the development of novel materials. Recent developments in condensed matter and materials physics have seen the rise of many novel quantum materials that require ultra-clean and high-quality samples for fundamental studies and applications. Novel oxide-based quantum materials synthesized using MBE have advanced the development of the field and materials. In this review, we discuss the recent progress in new MBE techniques that have enabled synthesis of complex oxides that exhibit ‘quantum’ phenomena, including superconductivity and topological electronic states. We show how these techniques have produced breakthroughs in the synthesis of 4d and 5d oxide films and heterostructures that are of particular interest as quantum materials. These new techniques in MBE offer a bright future for the synthesis of ultra-high quality oxide quantum materials.
Characterization and testing of advanced plasma-facing materials (PFMs) for Fusion Pilot Plants (FPP) is being conducted at the DIII-D National Fusion Facility through the ongoing two-year FPP Candidate Materials Thrust. Year one tested 17 novel materials utilizing the Divertor Materials Evaluation System (DiMES), with samples analyzed pre- and post-experiment via SEM, EDS, and confocal microscopy. Repeatable reference discharges were developed to ensure uniformity between experiments, including a new strike-point rastering scenario to provide more uniform heat/particle flux across DiMES during ELMing H-mode discharges. Various sample geometries and temperatures were used to achieve FPP-relevant conditions, including samples angled 10° towards the incident plasma flux and pre-heating up to 500 °C. The first exposure of liquid lithium (Li) capillary porous structures in a tokamak demonstrated uniform emission of Li vapor and suppression of Li droplets in H-mode when preheated to 350 °C. Dispersoid-strengthened W with 1 wt% TaC, TiC, and ZrC exposed to H-mode showed cracking and dispersoid ejection for all varieties except TiC, providing a clear down-selection. Ultra-high temperature ceramic materials TiB 2 and ZrB 2 showed minimal degradation under L-mode exposure. Silicon carbide (SiC) fiber composites showed arcing along edges, while CVD SiC remained pristine. Atmospheric plasma-sprayed W and SiC coatings endured H-mode exposure without macroscopic delamination; SiC exhibited granular ejection, while W showed increased outgassing. Additional W-based alloys were stress tested in H-mode, including Ni-based W heavy alloys, W f SiC f /W composites, W multi-principle element alloys, and functionally-graded W/SiC, to varying degrees of success.
The emergence of high-entropy materials has inspired the exploration of novel materials in diverse technologies. In electrochemical energy storage, high-entropy design has shown advantageous impacts on battery materials such as suppressing undesired short-range order, frustrating energy landscape, decreasing volumetric change and reducing the reliance on critical metals. This comment addresses the definition and potential improper use of the term “high entropy” in the context of battery materials design, highlights the unique properties of high-entropy materials in battery applications, and outlines the remaining challenges in the synthesis, characterization, and computational modeling of high-entropy battery materials.
Since the discovery of graphene, research on two-dimensional (2D) quantum materials has generated a plethora of novel materials with the potential to significantly change our lives. Owing to their unique quantum, electronic, and excitonic properties, biosensing and bio-imaging are two specific areas where these quantum materials have started to make a large impact. This article provides a holistic overview into the properties, synthesis methods, biological applications, and toxicology studies of commonly studied quantum materials. Recent research into the biological applications of graphene and transition metal dichalcogenides is highlighted and their unique electronic properties are explored. Though tremendous research is generated in discovering new 2D materials, toxicology studies in the area are lacking. Historically, toxicology studies have lagged significantly behind new material discovery often with dire consequences. The product development cycle of polymeric materials is a testament to the negative consequences of having a reactionary approach to toxicology. With 2D materials set to become a widespread part of everyday life in the near future, we comment on the need for a proactive approach to examining and understanding their toxicology.
Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.
Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.
Even though the development of novel materials that mimic nature is widely used in a variety of engineering and scientific fields, the relationship between effective material properties and underlying, often complex pore morphology is still not fully understood. To address this knowledge gap and accelerate the development of novel nature-inspired materials, this paper adopts a higher-order asymptotic homogenization method to numerically investigate the effect of complex micropore morphology on the effective mechanical properties of a porous system. Specifically, we create unique pore morphologies with varying levels of complexity that serve as a more realistic representation of natural materials. Here, we then use the second-order homogenization method to capture the role of pore size, shape, orientation, and distribution on effective properties. By creating different pore morphologies, we systematically studied the relationship between morphology and effective mechanical properties. The results highlight the necessity of higher-order parameters to fully capture the role of realistic pore morphologies on effective mechanical properties and provide a path forward in the design of nature-inspired materials.
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS 2 . Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition in MoS 2 which are important for memristive neuromorphic applications, among others. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.
The program goal was the development and demonstration of a novel material coating system using an innovative atmospheric cold microwave plasma jet to enable surface functionalization for corrosion protection, wear resistance and improved bonding strength. The technical approach was directed towards proof-of-concept demonstration of advanced point-of-manufacturing material bonding to improve adhesive joint performance subject to harsh environmental exposure. The assembled team, consisting of the researchers at the University of Illinois, end-users at General Motors and small business manufacturer Starfire Industries, successfully demonstrated a single, cost-effective, high-volume production process that meets the requirements of multi-material combinations relevant to the energy-intensive transportation industry (e.g. Al-steel, Mg-steel, Al-Mg and Al-CFRP joints for body-in-white structures) while eliminating offline pretreatment for Al and Mg alloy substrates that are energy intensive, have chemical waste disposal issues, and add cost. Technical demonstration of dissimilar material joining while maintaining bonding strength and cohesion after long-duration environmental exposure was accomplished for all materials of interest. This novel approach has broad applicability for manufacturing in automotive, aerospace, marine, railcar, and specialty consumer goods, etc.
Two-dimensional (2D) van der Waals (vdW) heterostructures offer new platforms for exploring novel physics and diverse applications ranging from electronics and photonics to optoelectronics at the nanoscale. The studies to date have largely focused on transition-metal dichalcogenides (TMDCs) based samples prepared by mechanical exfoliation method, therefore it is of signi ficant interests to study high-quality vdW heterostructures using novel materials prepared by a versatile method. Here, we report a two-step vapor phase growth process for the creation of high-quality vdW heterostructures based on perovskites and TMDCs, such as 2D Cs 3 Bi 2 I 9 /MoSe 2 , with a large lattice mismatch. Supported by experimental and theoretical investigations, we discover that the Cs 3 Bi 2 I 9 /MoSe 2 vdW heterostructure possesses hybrid band alignments consisting of type -I and type-II heterojunctions because of the existence of defect energy levels in Cs 3 Bi 2 I 9 . More importantly, we demonstrate that the type-II heterojunction in the Cs 3 Bi 2 I 9 /MoSe 2 vdW heterostructure not only shows a higher interlayer exciton density, but also exhibits a longer interlayer exciton lifetime than traditional 2D TMDCs based type-II heterostructures. We attribute this phenomenon to the reduced overlap of electron and hole wavefunctions caused by the large lattice mismatch. Finally, our work demonstrates that it is possible to directly grow high-quality vdW heterostructures based on entirely different materials which provide promising platforms for exploring novel physics and cutting-edge applications, such as optoelectronics, valleytronics, and high-temperature super fluidity.
The interrogation of materials with X-rays or neutrons to determine the structure, energetics, and dynamics of materials is fundamental to advancing materials' physical and chemical science and developing innovative material technologies. A transcending challenge in developing novel materials is that progress hinges on understanding the structure and dynamics across multiple time and length scales in complex materials that feature multiple components, interfaces, and compositions. Despite the ever-growing demands on materials’ characterization, existing approaches are almost exclusively based on isolated X-ray or neutron scattering, i.e., an approach commensurate with the more narrowly defined needs of fifty years ago. A three-day workshop sponsored by the U.S. National Science Foundation (NSF) analyzed the demand for concurrent neutron and X-ray (NeX) experiments. It was held at the Spring Hill Suites, San Jose, California, from June 2 to 4, 2022. In this workshop, 70 national and international experts ascertained the crucial need to establish NeX capabilities to advance the science of complex materials and systems in the US. Here, we illustrate the need for NeX scattering and spectroscopy experiments by showcasing examples that span areas as diverse as biomaterials, energy science, soft matter, and nanomaterials. To provide NeX capability will require new instrumentation that enables concurrent experiments. Affected areas include chemistry, soft matter, quantum materials, pure and applied chemistry, bioscience, geoscience, and applied materials. NeX benefits research outcomes due to the complementarity of the two techniques, which is essential for better model refinement. While joint refinement of data from separate neutron and X-ray experiments is critical to avoid ambiguities, especially in multiphase-multicomponent materials, concurrent experiments overcome scientific and technical barriers associated with single measurements, separated by location and, thus, time. Among all the examples, these factors introduce uncertainties in the results that complicate data analysis. [1,2] [3] While models are strongly sample-dependent, the principles of joint refinement are generally applicable to these disciplines, including the development of advanced parameterization, modeling, and analysis techniques that also consider the temporal and spatial resolutions of the two methods, leading to unambiguous data interpretation. Solutions for technical barriers must be found to realize NeX experiments, including developing robust sample environments that meet the optical requirements of neutrons and X-rays.
The Oak Ridge National Laboratory is planning to build the Second Target Station (STS) at the Spallation Neutron Source (SNS). STS will host a suite of novel instruments that complement the First Target Station’s beamline capabilities by offering an increased flux for cold neutrons and a broader wavelength bandwidth. A novel neutron imaging beamline, named the Complex, Unique, and Powerful Imaging Instrument for Dynamics (CUPI 2 D), is among the first eight instruments that will be commissioned at STS as part of the construction project. CUPI 2 D is designed for a broad range of neutron imaging scientific applications, such as energy storage and conversion (batteries and fuel cells), materials science and engineering (additive manufacturing, superalloys, and archaeometry), nuclear materials (novel cladding materials, nuclear fuel, and moderators), cementitious materials, biology/medical/dental applications (regenerative medicine and cancer), and life sciences (plant–soil interactions and nutrient dynamics). The innovation of this instrument lies in the utilization of a high flux of wavelength-separated cold neutrons to perform real time in situ neutron grating interferometry and Bragg edge imaging—with a wavelength resolution of δλ/λ ≈ 0.3%—simultaneously when required, across a broad range of length and time scales. This manuscript briefly describes the science enabled at CUPI 2 D based on its unique capabilities. The preliminary beamline performance, a design concept, and future development requirements are also presented.
The advancement of nuclear energy technology necessitates the development of novel materials and synthesis methods to produce materials which enable new fuel cycles. Alongside the maturation of R&D scale technologies to produce these materials, there is an ongoing effort to develop in situ monitoring capabilities to reduce the time to the discovery and development of these fuels. Monitoring data can be leveraged in artificial intelligence platforms to detect phenomena which lead to varied macro- and microstructural features which impact the application and performance of samples synthesized. The present study presents early-stage findings of the implementation of high-temperature, high-frame-rate infrared thermal imaging to monitor the arc-melt synthesis of novel fuels and compounds relevant to advanced nuclear reactors. The study illustrates both the challenges and opportunities of this methodology, highlighting the importance of internal standards while determining emissivity and transmission values as well as visualizing volatilization during melt synthesis.