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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Laser-assisted embedding of all-glass optical fiber sensors into bulk ceramics for high-temperature applications

We develop a laser-assisted sensor embedding process to embed all-glass optical fiber sensors into bulk ceramics for high-temperature applications. A specially designed two-step microchannel was fabricated on an Al 2 O 3 substrate for sensor embedment using a picosecond (ps) laser. An optical fiber Intrinsic Fabry-Perot Interferometer (IFPI) sensor was embedded at the bottom of the microchannel and covered by Al 2 O 3 slurry which was subsequently sintered by a CO 2 laser. The sensor spectrum was in-situ monitored during the laser sintering process to ensure the survival of the sensor and optimize the laser sintering parameters. By testing in furnace through high temperature, the embedded optical fiber shows improved stability after CO 2 laser sealing, resulting in the linear temperature response of the embedded optical fiber IFPI sensor. To improve the embedded IFPI sensor for thermal strain measurement, a dummy fiber was co-embedded with the sensing fiber to improve the mechanical bonding between the sensing fiber and the ceramic substrate so that the thermal strain of the ceramic substrate can apply on the sensing fiber. The response sensitivity, measurement repeatability and high-temperature long-term stability of the embedded optical fiber IFPI sensor were evaluated in this work.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

4-Clique network minor embedding for quantum annealers

Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Demonstrate embedding of sensors in a relevant microreactor component

This report describes the first successful embedding of sensors in stainless steel pipe specimens and a miniature hex block to support structural health monitoring of microreactor components during electrically heated testing in non-nuclear testbed facilities. Sensors were embedded with an ultrasonic additive manufacturing process that uses a combination of downward pressure and an ultrasonic scrubbing motion to bond stainless steel foils to underlying materials. After placing sensors within cavities that have been machined to the size of the sensors, the ultrasonic bonding process fills the gaps around the sensors and embeds them within the part. Both Type K thermocouples and spatially distributed fiber-optic strain sensors were successfully embedded within two pipe specimens and a miniature hex block. After the fiber-optic sensors were embedded, they were interrogated and found to have minimal signal attenuation within the embedded region—a significant improvement compared to previous trials. This is likely because of the low-bend-loss fiber that was used, which greatly reduces microbending losses that occur after the embedding process. In addition to showing minimal signal attention, the fibers also showed adequate compressive strain, which is a strong indication that the sensors are physically embedded in the parts as opposed to simply being loosely contained inside the machined cavities. The embedded fibers were able to capture the strain induced in one of the pipe specimens while the pipe was actively heated with a moving heat source.

42 ENGINEERING↗
DOE OSTI2021

Characterization of Embedded Sensors in Stainless Steel Test Articles and Design/Planning for MAGNET Testing

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes for heat rejection, which results in significant thermal stresses in the monolithic structures. This work describes the initial characterization and test plan for evaluating stainless steel test articles fabricated with embedded sensors for measuring heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing. The electrically heated testing will be performed in the non-nuclear Microreactor Agile Non-Nuclear Testbed and Single Primary Heat Extraction and Removal Emulator facilities located at Idaho National Laboratory. The goals of these tests are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. More generally, the ability to monitor component and system health during microreactor operation is attractive for providing a high sensor density to inform a limited number of microreactor operators to ultimately reduce operation and maintenance costs and move toward semi-autonomous operation. This report discusses the characterization of embedded thermocouples and fiber optic sensors in relevant test articles, including cylindrical pipes and hexagonal monolithic test articles for heat pipe-based reactors. The sensors were embedded by placing them in machined channels and then building up additional material by using ultrasonic additive manufacturing (UAM). UAM is a solid-state welding process that uses downward pressure and a lateral scrubbing motion to bond thin metal foils to a base material layer by layer. The ultrasonic welding process relies on the plastic deformation of the metal—as opposed to typical melting and solidification—to break oxide scales and bond the metal layers. The characterization of these embedded sensors included evaluating fiber optic signal attenuation, observing residual strain in the fibers, investigating microstructural and mechanical aspects, and demonstrating the sensors under various thermal loads and acoustic vibrations. Post-embedding characterization showed a fine grain structure (<1 μm) near the interfaces of the bonded foils as a result of severe deformation from the welding process. A large increase in hardness was observed at the foil interfaces and the fiber/matrix interface compared with the bulk matrix. Even when compared with the SS304 interfaces, the higher hardness observed around the embedded fiber suggests a higher degree of deformation due to the soft metal coating around the silica fiber core. The distributed fiber-optic temperature sensors and embedded thermocouples reliably measured temperature distributions during steady-state and transient thermal testing. The embedded fiber-optic sensors reliably measured strain during both transient and steady-state testing and properly identified resonant frequencies during acoustic testing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

An improved hyperbolic embedding algorithm

Because hyperbolic space has properties that make it amenable to graph representations, there is significant interest in scalable hyperbolic-space embedding methods. These embeddings enable constant-time approximation of shortest-path distances, and so are significantly more efficient than full shortest-path computations. In this article, we improve on existing landmark-based hyperbolic embedding algorithms for large-scale graphs. Whereas previous methods compute the embedding by using the derivative-free Nelder–Mead simplex optimization method, our approach uses the limited-memory BFGS (LBFGS) method, which is quasi-Newton optimization, with analytic gradients. Our method is not only significantly faster but also produces higher-quality embeddings. Moreover, we are able to include the hyperbolic curvature as a variable in the optimization. We compare our hyperbolic embedding method implementation in Python (called Hypy) against the best publicly available software, Rigel. Our method is an order of magnitude faster and shows significant improvements in the accuracy of the shortest-path distance calculations. Furthermore, tests are performed on a variety of real-world networks, and we show the scalability of our method by embedding a graph with 1.8 billion edges and 65 million nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Efficiently embedding QUBO problems on adiabatic quantum computers

Adiabatic quantum computers like the D-Wave 2000Q can approximately solve the QUBO problem, which is an NP-hard problem, and have been shown to outperform classical computers on several instances. Solving the QUBO problem literally means solving virtually any NP-hard problem like the traveling salesman problem, airline scheduling problem, protein folding problem, genotype imputation problem, thereby enabling significant scientific progress, and potentially saving millions/billions of dollars in logistics, airlines, healthcare and many other industries. Yet, before QUBO problems are solved on quantum computers, they must be embedded (or compiled) onto the hardware of quantum computers, which in itself is a very hard problem. Here, we propose an efficient embedding algorithm, that lets us embed QUBO problems fast, uses less qubits and gets the objective function value close to the global minimum value. We then compare the performance of our embedding algorithm to that of D-Wave’s embedding algorithm, which is the current state of the art, and show that our embedding algorithm convincingly outperforms D-Wave’s embedding algorithm. Our embedding approach works with perfect Chimera graphs, i.e., Chimera graphs with no missing qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Reforming sewage sludge pyrolysis volatile with Fe-embedded char: Minimization of liquid product yield

Highlights: • Fe{sub 2}(SO{sub 4}){sub 3} is added in sewage sludge to produce pyrolysis char (SSC) embedded with Fe. • Fe-embedded SSC is utilized to reform the same source volatile to produce high quality syngas and avoid liquid products. • More gas is produced with raised H{sub 2} content as Fe concentration elevated in the Fe-embedded SSC. • Total liquid product and its oily fraction decreased greatly after reformed with Fe-embedded SSC. - Abstract: Obtaining high quality syngas from sewage sludge (SS) means transferring a low-grade SS into a high-grade fuel or raw materials for chemical products. In this study, Fe is added to SS in form of Fe{sub 2}(SO{sub 4}){sub 3} to produce an effective and self-sufficient catalyst in order to obtain more syngas and minimize liquid products from SS pyrolysis. The Fe-embedded sewage sludge chars (SSCs) were used as catalysts for volatile reforming at 600 °C. It has been found that the gas yield increases from 15.9 to 35.8 wt% of the SS and that of liquids decreases from 31.9 to 10.2 wt% after volatile reforming with Fe-embedded SSC when Fe was added equal to 7 % in the dried SS. In addition, the content of nitrogen-containing compounds in the oily products decreased. After reforming with Fe-embedded SSC, the molar fractions of syngas combustible components, including H{sub 2}, CH{sub 4} and CO, increase, and the higher heating value of the syngas increased to 17.0 MJ/Nm{sup 3} from the original 12.5 MJ/Nm{sup 3} obtained from SS pyrolysis at 550 °C. Moreover, the volatile reforming seems to reduce the level of some important syngas pollutants, like H{sub 2}S, HCl and HCN, even though it was also observed an increase of the contents of SO{sub 2}, NH{sub 3}, NO{sub 2,} HCNO and N{sub 2}O.

09 BIOMASS FUELS↗
DOE OSTI2024

Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.

Ti, Changpeng↗
DOE OSTI2019

Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing

Deep learning (DL) has been used for many natural language processing (NLP) tasks due to its superior performance as compared to traditional machine learning approaches. In DL models for NLP, words are represented using word embeddings, which capture both semantic and syntactic information in text. However, 90-95% of the DL trainable parameters are associated with the word embeddings, resulting in a large storage or memory footprint. Therefore, reducing the number of word embedding parameters is critical, especially with the increase of vocabulary size. In this work, we propose a novel approximate word embeddings approach for convolutional neural networks (CNNs) used for text classification tasks. The proposed approach significantly reduces the number of model trainable parameters without noticeably sacrificing in computing performance accuracy. Compared to other techniques, our proposed word embeddings technique does not require modifications to the DL model architecture. We evaluate the performance of the the proposed word embeddings on three classification tasks using two datasets, composed of Yelp and Amazon reviews. The results show that the proposed method can reduce the number of word embeddings parameters by 98% and 99% for the Yelp and Amazon datasets respectively, with no drop in computing accuracy.

Alawad, Mohammed↗
DOE OSTI2019

Embedment of sensors in ceramic structures

Ceramic materials such as silicon carbide (SiC) are attractive for many nuclear applications due to their ability to withstand high temperatures, which inherently improve the thermal efficiency of the power conversion cycle and can in some cases provide high-temperature process heat. SiC is particularly attractive because of its high-temperature strength retention, stability under neutron irradiation, and low neutron absorption. The TCR program has recently demonstrated the ability to fabricate monolithic SiC components with complex geometries using a binder jet additive manufacturing technique followed by densification using chemical vapor infiltration (CVI). This technology presents an opportunity to embed sensors within ceramic components at critical locations that allow for enhanced process monitoring and control of advanced nuclear energy systems. The challenges with embedding sensors in ceramic components are to identify appropriate materials that can survive the CVI process, remain embedded after cooling to room temperature, and be suitable for operation in a high-temperature nuclear environment. This report summarizes the sensors that are being considered for embedding within ceramics, the sensor embedding process, material compatibility studies, and characterization of the embedded components. Material selection is first guided by computational thermodynamics and is confirmed by embedding coupons and examining material interactions using electron microscopy. This work shows that a wide range of refractory metals, as well as certain fiber optic sensors, can be successfully embedded in 3D-printed SiC. Future work will involve testing of functional sensors including thermocouples and optical fiber-based, spatially distributed temperature/strain sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Demonstration of Embedded Sensors in Ceramic Structures

The Transformational Challenge Reactor (TCR) program seeks to demonstrate an advanced 3D printed nuclear reactor core. A binder jet additive manufacturing process is used to fabricate the complex SiC ceramic components of the TCR core. After printing, the components are densified via chemical vapor infiltration (CVI). This process allows complex cavities to be created in which sensors can be placed at strategic locations. Inserting the sensors during the CVI process allows the sensors to be embedded within the component as it is infiltrated. The primary challenge for embedding the sensors is identifying the sensors and sensor sheath materials that can survive the temperatures (>1,000°C) and chemical exposure to H 2 and HCl during CVI. The embedded sensors that are being investigated for use during TCR operation include thermocouples, self-powered neutron detectors, and spatially distributed fiber-optic temperature sensors. This report describes the CVI materials compatibility tests and initial trials for embedding functional sensors. Mo is identified as the most suitable sensor sheath material based on its availability in small diameter tubing, relatively low neutron absorption cross section, and compatibility with H 2 , HCl, and the surrounding SiC matrix at the process temperatures. Amorphous SiO2 fiber-optic samples with Au and Cu metal coatings were successfully embedded in SiC along with bare fibers, although the metal coatings showed some evidence of localized melting during CVI. Initial experiments attempting to embed functional sensors showed that the sensors can be embedded in 3D printed SiC parts, but there are still challenges that must be overcome to ensure that the sensors and sensor sheaths do not fail during CVI. In particular, Nb-sheathed Mo-Nb thermocouples failed at temperatures far lower than expected despite thermodynamic calculations and materials compatibility tests that show the Nb sheath to be compatible with the CVI environment. One potential explanation for the large expansion of the Nb sheath could be related to hydride formation in the lower temperature region of the sheath. Future work will focus on Mo-sheathed thermocouples instead of Nb. The fiberoptic sensors tested during the instrumented experiment broke due to the lack of mechanical protection after their polymer-based coatings were vaporized. Future instrumented experiments will use metal-coated optical fibers for strain sensing to provide additional mechanical protection. However, custom coatings with higher melting points than commercially available Au and Cu might be required. Regardless, fiber-optic sensors are still candidates for spatially distributed temperature measurements since they can be inserted into an embedded metal sheath after CVI.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Squeezed Nanocrystals: Equilibrium Configuration of Metal Clusters Embedded Beneath the Surface of a Layered Material

Shapes of functional metallic nanocrystals, typically synthesized either free in solution or supported on surfaces, are key for controlling properties. Here, we consider a novel new class of metallic nanocrystals, copper clusters embedded near the surface of graphite, which can be considered a model system for metals embedded beneath surfaces of layered materials, or beneath supported membranes. We develop a continuum elasticity (CE) model for the equilibrium shape of these islands, and compare its predictions with experimental data. The CE model incorporates appropriate surface energy, adhesion energies, and strain energy. The agreement between the CE model and the data is—with one exception—excellent, both qualitatively and quantitatively, and is achieved with a single adjustable parameter. The model predicts that the embedded island shape is invariant with size, manifest both by constant side slope and by constant aspect ratio. This prediction is rationalized by dimensional analysis of the relevant energetic contributions. The aspect ratio of an embedded Cu cluster is much larger than that of a supported but non-embedded Cu cluster, due to resistance of the graphene membrane to deformation. Experimental data diverge from the model predictions only in the case of the aspect ratio of small islands, below a critical height of ~10 nm. The divergence may be due to bending strain, which is treated only approximately in the model. Strong support for the CE model and its interpretation is provided by additional data for embedded Fe clusters. Most of these observations and insights should be generally applicable to systems where a metal cluster is embedded beneath a layered material or supported membrane, provided that shape equilibration is possible.

Julien, Scott↗
NASA NTRS2009

Embedded passives emulator FY2003 interim report

The National Aeronautics and Space Administration Electronic Parts and Packaging (NEPP) Program has been evaluating state-of-the-art Embedded Passive Technology for the printed circuit board (PCB) industry for the past several years and has conducted its own testing on embedded passive coupons (resistors and capacitors). This document is a progress report of the work performed through fiscal year 2008 by NASA and the Navy to convert an existing PWB (printed wiring board) design to a design that incorporates embedded passive components. The work is being performed through a memorandum of understanding (MOU) between the NEPP Program (NASA) and the Naval Surface Warfare Center (NSWC) Crane Emerging Critical Interconnect Technology (ECIT) Program. NSWC Crane’s ECIT Program has funding to convert two PCB designs from the military and aerospace industry to a PCB design with embedded passives. ECIT will provide a design team that will take a well characterized design and convert it to an embedded design. They will also provide electrical modeling and layout expertise as well as build the PWB in their facility in Indiana. NEPP, through the Jet Propulsion Laboratory (JPL), will assemble any passives deemed not suitable for embedding and the active components and electrically test the new design to the existing (non-embedded) design

Gerke, David↗
DOE OSTI2019

Embedded Fiber-Optic Sensors for In-Pile Applications

Qualification and commercialization of new nuclear fuels and materials requires a comprehensive set of data regarding behavior under irradiation. There are currently very limited options for in-situ monitoring of material evolution during irradiation due to the extremely harsh environment (i.e., high temperatures and intense radiation) of materials test reactors. This paper describes work being performed at Oak Ridge National Laboratory to embed metal-coated fiber-optic sensors into in-core irradiation experiments to enable measurement of radial dimensional changes and spatially distributed temperature and strain. Some critical issues that must be addressed before embedded fiber optics can be deployed in-core include (1) embedding of metal-coated fibers without failure or prohibitively large signal attenuation, (2) embedding in curved channels to allow for radial dimensional measurements, and (3) demonstrating that embedded fibers can survive the large stresses that result from differential thermal expansion between the glass fiber and the surrounding metal matrix. This work shows how optical fibers have been successfully embedded in aluminum and copper alloys in both straight and curved channels with various bend radii. The embedded fibers have also survived heating to temperatures of 500°C and cooling to room temperature. This paper presents some of the experimental results including measured light attenuation resulting from embedding with and without bends and high-temperature testing.

Petrie, Christian↗
DOE OSTI2024

OPER: Optimality-Guided Embedding Table Parallelization for Large-scale Recommendation Model

With the sharp increasing volume of user data, Deep Learning Recommendation Model (DLRM) becomes an indispensable infrastructure in large technology companies. However, large-scale DLRM on the multi-GPU platform is still inefficient due to unbalanced workload partitioning and intensive inter-GPU communication. To this end, we propose OPER, an OPtimality guided Embedding table placement for large-scale Recommendation model training and inference. OPER explores the potential of mitigating remote memory access latency in DLRM through fine-grained embedding table placement. Specifically, OPER proposes a theoretical modeling that builds up the relationship between EMT placement and the embedding communication latency in both training and inference. OPER proves the NP hardness of finding the optimal embedding table placement and proposes a heuristic algorithm that yields near optimal placement. OPER implements a SHMEM-based embedding table training system and a unified embedding index mapping to support fine-grained embedding table sharding and placement. Comprehensive experiments reveal that OPER achieves on average 3.4× and 5.1× speedup on training and inference respectively over state-of-the-art DLRM frameworks.

Wang, Zheng↗
DOE OSTI2021

Embedded Sensors in Additively Manufactured Silicon Carbide

Silicon carbide (SiC) components are being considered for a wide range of nuclear applications due to their high-temperature strength retention, low neutron absorption, chemical inertness, and dimensional stability under neutron irradiation. However, machining and joining of SiC components have traditionally limited its application to relatively simple geometries. Recent work has demonstrated additive manufacturing of complex, high-purity, crystalline SiC components using a combination of binder jet printing and densification via chemical vapor infiltration (CVI). The process lends itself to embedding of fuel, absorbers, moderators, and sensors at strategic locations within a component. The latter could allow for enhanced in situ performance monitoring of limiting fuel temperatures, self-shielded neutron flux, and potentially spatially distributed strain within complex SiC components if sensors can be successfully embedded during CVI. This work describes (1) methods for embedding sensors; (2) thermodynamic analyses and material compatibility testing for identifying sensors capable of surviving high temperatures and exposure to hydrogen and hydrogen chloride during CVI; and (3) nuclear applications for embedded sensors, including potential failure modes during fabrication and during reactor operation. Molybdenum-sheathed thermocouples were successfully embedded in a complex SiC component, whereas niobium-sheathed high-temperature irradiation-resistant thermocouples started to drift as soon as the reactant gases were introduced and ultimately failed during CVI due to severe constrained expansion, potentially resulting from niobium hydride formation in the low-temperature region of the CVI system. Optical fibers were successfully embedded in SiC, but further work is needed to protect the fragile fiber leads after their protective coatings are removed during CVI.

thermocouples↗
DOE OSTI2019

Absolutely Localized Projection-Based Embedding for Excited States

We present a quantum embedding method that allows for calculation of local excited states embedded in a Kohn–Sham density functional theory (DFT) environment. Projection-based quantum embedding methodologies provide a rigorous framework for performing DFT-in-DFT and wave function in DFT (WF-in-DFT) calculations. The use of absolute localization, where the density of each subsystem is expanded in only the basis functions associated with the atoms of that subsystem, provide improved computationally efficiency for WF-in-DFT calculations by reducing the number of orbitals in the WF calculation. In this work, we extend absolutely localized projection-based quantum embedding to study localized excited states using EOM-CCSD-in-DFT and TDDFT-in-DFT. The embedding results are highly accurate compared to the corresponding canonical EOM-CCSD and TDDFT results on the full system, with TDDFT-in-DFT frequently more accurate than canonical TDDFT. The absolute localization method is shown to eliminate the spurious low-lying excitation energies for charge-transfer states and prevent overdelocalization of excited states. Additionally, we attempt to recover the environment response caused by the electronic excitations in the high-level subsystem using different schemes and compare their accuracy. Finally, we apply this method to the calculation of the excited-state energy of green fluorescent protein and show that we systematically converge to the full system results. Here we demonstrate how this method can be useful in understanding excited states, specifically which chemical moieties polarize to the excitation. Furthermore, this work shows absolutely localized projection-based quantum embedding can treat local electronic excitations accurately and make computationally expensive WF methods applicable to systems beyond current computational limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Metal-to-Ligand Charge-Transfer Spectrum of a Ru-Bipyridine-Sensitized TiO 2 Cluster from Embedded Multiconfigurational Excited-State Theory

Understanding optical properties of the dye molecule in dye-sensitized solar cells (DSSCs) from first-principles quantum mechanics can contribute to improving the efficiency of such devices. While density functional theory (DFT) and time-dependent DFT have been pivotal in simulating optoelectronic properties of photoanodes used in DSSCs at the atomic scale, questions remain regarding DFT’s adequacy and accuracy to furnish critical information needed to understand the various excited-state processes involved. In this work, we simulate the absorption spectra of a dye-sensitized solar cell analogue, comprised of a Ru-bipyridine (Ru-bpy) dye molecule and a small TiO2 cluster via DFT and via an accurate embedded correlated wavefunction (CW) theory. We generated CW spectra for the adsorbed Ru-bpy dye via a recently introduced capped density functional embedding theory or capped-DFET (to generate the embedding potential that accounts for the interaction of the molecule and the TiO 2 cluster). We then combined capped-DFET with the accurate but expensive multiconfigurational complete active space second-order perturbation theory (CASPT2)—embedded CASPT2. Because the CW theory is conducted on only a portion of the total system in the presence of an embedding potential that describes that portion’s interaction with its environment, we efficiently obtain CW-quality predictions that reflect local properties of the entire system. Specifically, for example, with capped-DFET and embedded CW theory, we can simulate accurately a plethora of metal-to-ligand charge-transfer excited properties at a manageable computational cost. Here, we predict detailed electronic spectra within the visible region, featuring the lowest three singlet and triplet excited states, along with predictions of the singlets’ lifetimes. We illustrated these results using a Jablonski diagram that show the relative energy position of the singlet and longer-lived triplet excited states and analyzed and proposed relaxation paths for the excited state corresponding to the most intense but short-lived absorption (interconversion, intersystem crossing, fluorescence, and phosphorescence) that may lead to longer-lived excited states necessary for efficient charge separation required to generate current in solar cells.

14 SOLAR ENERGY↗