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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Low-Cost Magnesium Alloy Sheet Component Development and Demonstration Project

The overarching objective of this USAMP project was to develop and demonstrate door panels made from magnesium (Mg) sheet with a cost penalty over conventional steel stampings of no more than $5.50/kg saved. The technical approach integrated experiments with advanced computational tools based on Integrated Computational Materials Engineering (ICME) methods to develop new alloy chemistries and their thermomechanical processing that promise improved formability and lower forming temperatures. A penultimate task before finally forming the stampings was to incorporate actual microstructure into models that would enable formability simulations. This approach would, for the first time, account for individual magnesium grains moving in an anisotropic fashion unlike that for aluminum or steel that have isotropic properties upon which the current simulation tools are based. In separate activities, new coatings and lubricants to facilitate forming and improved corrosion protection and joining strategies, were developed to ensure that the door could be produced with stated product requirements. A technical cost model, which included parts production, assembly, and paint for a door specifically designed for Mg sheet, showed the cost penalty to be between 4.26 USD to 6.60 USD/kg saved, which enveloped the project’s cost targets. The cost of the coated Mg sheet was identified as the key driver for the cost penalty. The mass of the Mg-intensive door was 7.9 kg, which was 54% less than the baseline steel door.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluation of Machine Learning Models for Automated Data Analysis in In-Service Nuclear Power Plant Inspections

The commercial nuclear power industry is facing a potential shortage of certified nondestructive evaluation (NDE) analysts to meet future in-service inspection demands. Automated data analysis (ADA) currently supports human inspectors in tasks such as eddy current evaluations for steam generator examinations. Machine learning (ML) systems are nearing the capability to pass performance demonstration tests for ultrasonic testing (UT) inspections of reactor pressure vessel upper head penetrations in nuclear power plants (NPPs). Current research and development is focused on assisted analysis (AA) of ADA versus fully automated examinations. This presentation will cover assessment of ML flaw detection on dissimilar metal weld (DMW) piping joints.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Inhibiting Effect of Additives on Pressure Solution of Calcite

The task of protection of cultural heritage requires a better understanding of combined effects of mechanical and chemical factors involved in environmental deterioration of monuments. The present paper deals with extending some known physicochemical methods proposed for inhibiting the decay of unstressed materials to their study during water-assisted deformation. The tests have been carried out on natural limestone samples and calcite powders in CaCO{sub 3} saturated aqueous solutions under static loads causing measurable pressure solution creep. In the solutions containing 1-hydroxyethylidene-1,1-diphosphonic acid, nitrilotriacetic acid, or ethylenediaminetetraacetic acid, the creep rate decreases considerably with increasing concentration of additives. The extent of creep deceleration has been found to be proportional to the independently estimated calcite surface area occupied by adsorbed species. This fact enables us to discriminate the adsorption-induced effect from other variables controlling the pressure solution rate and may be used in screening of compounds able to minimize the environmental impact on marble and limestone objects undergoing mechanical stresses.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Interpreting nanovoids in atom probe tomography data for accurate local compositional measurements

Quantifying chemical compositions around nanovoids is a fundamental task for research and development of various materials. Atom probe tomography (APT) and scanning transmission electron microscopy (STEM) are currently the most suitable tools because of their ability to probe materials at the nanoscale. Both techniques have limitations, particularly APT, because of insufficient understanding of void imaging. Here, we employ a correlative APT and STEM approach to investigate the APT imaging process and reveal that voids can lead to either an increase or a decrease in local atomic densities in the APT reconstruction. Simulated APT experiments demonstrate the local density variations near voids are controlled by the unique ring structures as voids open and the different evaporation fields of the surrounding atoms. We provide a general approach for quantifying chemical segregations near voids within an APT dataset, in which the composition can be directly determined with a higher accuracy than STEM-based techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Results of stainless steel canister corrosion studies and environmental sample investigations

This progress report describes work being done at Sandia National Laboratories (SNL) to assess the localized corrosion performance of container/cask materials used in the interim storage of used nuclear fuel. The work involves both characterization of the potential physical and chemical environment on the surface of the storage canisters and how it might evolve through time, and testing to evaluate performance of the canister materials under anticipated storage conditions. To evaluate the potential environment on the surface of the canisters, SNL is working with the Electric Power Research Institute (EPRI) to collect and analyze dust samples from the surface of in-service SNF storage canisters. In FY 13, SNL analyzed samples from the Calvert Cliffs Independent Spent Fuel Storage Installation (ISFSI); here, results are presented for samples collected from two additional near-marine ISFSI sites, Hope Creek NJ, and Diablo Canyon CA. The Hope Creek site is located on the shores of the Delaware River within the tidal zone; the water is brackish and wave action is normally minor. The Diablo Canyon site is located on a rocky Pacific Ocean shoreline with breaking waves. Two types of samples were collected: SaltSmart™ samples, which leach the soluble salts from a known surface area of the canister, and dry pad samples, which collected a surface salt and dust using a swipe method with a mildly abrasive ScotchBrite™ pad. The dry samples were used to characterize the mineralogy and texture of the soluble and insoluble components in the dust via microanalytical techniques, including mapping X-ray Fluorescence spectroscopy and Scanning Electron Microscopy. For both Hope Creek and Diablo Canyon canisters, dust loadings were much higher on the flat upper surfaces of the canisters than on the vertical sides. Maximum dust sizes collected at both sites were slightly larger than 20 μm, but Phragmites grass seeds ~1 mm in size, were observed on the tops of the Hope Creek canisters. At both sites, the surface dust could be divided into fractions generated by manufacturing processes and by natural processes. The fraction from manufacturing processes consisted of variably-oxidized angular and spherical particles of stainless steel and iron, generated by machining and welding/cutting processes, respectively. Dust from natural sources consisted largely of detrital quartz and aluminosilicates (feldspars and clays) at both sites. At Hope Creek, soluble salts were dominated by sulfates and nitrates, mostly of calcium. Chloride was a trace component and the only chloride mineral observed by SEM was NaCl. Chloride surface loads measured by the Saltsmart™ sensors were very low, less than 60 mg m –2 on the canister top, and less than 10 mg m –2 on the canister sides. At Diablo Canyon, sea-salt aggregates of NaCl and Mg-SO 4 , with minor K and Ca, were abundant in the dust, in some cases dominating the observed dust assemblage. Measured Saltsmart™ chloride surface loads were very low (<5 mg m –2 ); however, high canister surface temperatures damaged the Saltsmart™ sensors, and, in view of the SEM observations of abundant sea-salts on the package surfaces, the measured surface loads may not be valid. Moreover, the more heavily-loaded canister tops at Diablo Canyon were not sampled with the Saltsmart™ sensors. The observed low surface loads do not preclude chloride-induced stress corrosion cracking (CISCC) at either site, because (1) the measured data may not be valid for the Diablo Canyon canisters; (2) the surface coverage was not complete (for instance, the 45º offset between the outlet and inlet vents means that near-inlet areas, likely to have heavier dust and salt loads, were not sampled); and (3) CISCC has been experimentally been observed at salt loads as low as 5-8 mg/m 2 . Experimental efforts at SNL to assess corrosion of interim storage canister materials include three tasks in FY14. First, a full-diameter canister mockup, made using materials and techniques identical to those used to make interim storage canisters, was designed and ordered from Ranor Inc., a cask vendor for Areva/TN. The mockup will be delivered prior to the end of FY14, and will be used for evaluating weld residual stresses and degrees of sensitization for typical interim storage canister welds. Following weld characterization, the mockup will be sectioned and provided to participating organizations for corrosion testing purposes. A test plan is being developed for these efforts. In a second task, experimental work was carried out to evaluate crevice corrosion of 304SS in the presence of limited reactants, as would be present on a dustcovered storage canister. This work tests the theory that limited salt loads will limit corrosion penetration over time, and is a continuation of work carried out in FY13. Laser confocal microscopy was utilized to assess the volume and depth of corrosion pits formed during the crevice corrosion tests. Results indicate that for the duration of the current experiments (100 days), no stifling of corrosion occurred due to limitations in the amount of reactants present at three different salt loadings. Finally, work has been carried out this year perfecting an instrument for depositing sea-salts onto metal surfaces for atmospheric corrosion testing purposes. The system uses an X-Y plotter system with a commercial airbrush, and deposition is monitored with a quartz crystal microbalance. The system is capable of depositing very even salt loadings, even at very low total deposition rates.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy

The third Sandia Fracture Challenge illustrated the geometric and material uncertainties presented by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Particularly, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This method allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.

316L stainless steel↗
DOE OSTI2023

The AXEAP2 program for K β X-ray emission spectra analysis using artificial intelligence

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant K β XES of 3 d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 ( AXEAP2 ), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3 d electron spin state, 3 d –3 p electron exchange force and K β emission core-hole lifetime.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Critical parameters in the faculty application process: A data-driven analysis

We report the process of applying for science and engineering faculty positions requires a significant time investment for both the applicant and hiring committees. Applicants face the daunting task of sifting through a seemingly endless amount of information related to crafting their application packages in preparation for a review process that is inherently highly subjective. Furthermore, while scientific supervisors are often the most valuable resource during this stage of career development, many years may have passed since they went through this process themselves, and they may not have experience with recent developments in the application process. Because expectations and job responsibilities of new faculty members are rapidly evolving, certain guidelines are often not clearly specified, and it is therefore becoming difficult for applicants to most effectively prepare their application documents. Without access to adequate information and assistance, early-career researchers in particular can be placed at a significant disadvantage in the hiring process. Several recent articles can help prospective applicants in materials science and engineering land a faculty position,1,2,3 but there is nevertheless a need for clarification on expectations for different aspects of the faculty application materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Determining the Oxygen Stoichiometry of Cobaltite Thin Films

Transition metal oxides (TMO) are promising materials to realize low-power neuromorphic devices. Their physical properties critically depend on their oxygen vacancy concentrations, whose experimental determination remains a challenging task. Here we focus on cobaltites, in particular La 1-x Sr x CoO 3-d (LSCO), and we present a strategy to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra. Using a combination of experiment and theory, we show that the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change of the relative peak positions of the O K-edge XA spectra. Furthermore, we also identify an additional geometrical fingerprint that captures both the changes of the Co-O bond length and Co-O-Co bond angle in the material due to the presence of oxygen vacancies. Finally, we predict the oxygen vacancy concentration of experimental samples and show how the resistivity of the oxide material may be tuned as a function of the defect concentration present in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Durability of Corrosion Protection Coatings for Aluminum

The objectives of this work are to (1) Develop a detailed understanding of the performance characteristics of current chromate conversion coatings utilized in the stockpile, (2) Develop a detailed understanding of the performance characteristics of non-chromate conversion coatings proposed to replace their chromate containing counterparts, (3) Provide an informed assessment of the aforementioned coatings and a recommended course of action for the replacement of chromate containing coatings, and (4) establish subject matter expertise in the area of replacement coatings to provide as a resource to the systems organization. These tasks will be accomplished by performing a battery of traditional corrosion performance tests (in accordance with the relevant MIL specifications governing chemical conversion coatings), combined with more detailed analytical evaluation of the materials. Coatings will be evaluated in the as-received and artificially aged condition. Four different coating systems were evaluated on four different aluminum alloys in FY16. The focus this year was to continue populating the test matrix initiated in previous years, evaluating key coatings in the as received and thermally stressed condition, as compared to the baseline chromate conversion coating (Bonderite (formerly Alodine) 1200S). The non-chromate conversion coatings included Bonderite T 5900, Chemeon TCP-HF, Luster-On Aluminescent, and MacDermid-Enthone Iridite NCP. A second effort that was completed this year was to address a bottleneck that existed for the electrical evaluation of the coatings, assessing their conformance with the MIL specification. An electromechanical test system was assembled and automated, eliminating the need to rely on an already heavily utilized mechanical test system and operator in Materials Mechanics and Tribology.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Correlation of Surface Acoustic Wave (SAW) force myography sensor output with elbow joint torque

Accurate assessment of skeletal muscle forces and net joint torque is essential for preventing fatigue-related injuries, optimizing physical training, and monitoring disease progression in neuromuscular conditions. However, existing joint torque evaluation techniques are hindered by limited portability and high operational costs, confining their use to controlled laboratory or clinical settings. Despite substantial advances in wearable joint torque estimation systems, ongoing challenges such as power constraints, bulky wired setups, and susceptibility to environmental or motion artifacts underscore the urgent need for truly batteryless, wireless solutions deployable in real-world settings. This paper proposes a novel surface acoustic wave (SAW)-based force myography (FMG) system for noninvasive measurement of joint torque, validated against a gold-standard electromechanical dynamometer. The approach uses a single SAW sensor embedded in an armband to detect volumetric biceps brachii changes, with a second-order polynomial mapping sensor output and elbow angle to torque. Seven participants were tested in both isometric (15°–90°) and isokinetic (10°/s and 20°/s) supinated elbow flexion tasks. Under isometric conditions, subject-specific calibration achieved a normalized root-mean-square error (NRMSE) of 13.6% ± 6.0% and R 2 = 0.834 ± 0.180, while a group-level model yielded 14.4% ± 6.8% and 0.808 ± 0.208, respectively. For isokinetic trials, the group model produced an NRMSE of 24.1% ± 6.6% at 10°/s and 24.9% ± 08.9% at 20°/s, highlighting the feasibility of using a single-sensor SAW-FMG setup across different speeds. Because SAW devices support wireless, battery-free operation, the proposed system offers a pathway to portable, real-time monitoring for sports medicine, rehabilitation, and clinical diagnostics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Spider silk MASP1 and MASP2 proteins as carbon fiber precursors

The objective of this project is to develop an unconventional non-petroleum based carbon fiber precursor which has the potential to be produced in high yield and quantities. Methods will be developed to produce pilot-scale quantities of fibers from spider silk proteins with mechanical properties at least 75% that of the natural dragline silk fibers in tensile strength and elongations of less than 5%. The precursor fibers will be converted to carbon fibers, with a goal of >250Ksi strength and 1-2% elongation. Cost analysis will be performed and the process optimized. Task 1: Subtask 1. Protein production: We exceeded the go/ no go milestone of 1.0g/L of one of the spider silk protein (MSp2) purified last FY and have now increased from 5L to 500L fermentations. We have made a series of changes to the purification protocol from the initial report last FY. These led to a reduction in the time needed for the purification and reduced the purification costs by nearly 90%. Subtask 2. Fiber spinning: The major focus has been to produce more material to send 24 fiber thread to ONRL. We are still developing the methodology to successfully spin 24 fiber yarns. This involves both the spinning dope solutions as well as the methods to keep the fibers from fusing during the post spin stretch. The second area of focus has been to standardize the spin dopes for making the fibers. We now know that the conductivity (indicative of salt remaining with the protein after purification) is an important factor in successful spinning as is the pH. We now know that we need to be below 600 uS conductivity and that the most effective pH is protein dependent. Subtask 3. Silkworm silk: We have found the transgenic silkworms made using gene replacement at the fibroin light chain instead of heavy chain as we did previously have a higher tensile strength. See figures below showing the curve for the top end of the cocoon fibers. This tensile strength is the same as the average for spider dragline silk. Task 2. Carbonization: The major accomplishment in the latter part of the work is that the ONRL group has successfully heated the spider silk protein fibers all the way up to 1700°C and produced a very competitive carbon fiber based on mechanical properties. Several important factors were discovered during these initial trials: 1) the ramp speed for increasing the temperature is critical; 2) maintaining tension on the fiber during the heating process because as it is heated it tends to expand; and 3) narrow temperature window in which stretching the fiber during heating leads to much better final materials. Task 3. Techno-Economic Methods: The techno-economic analysis was expanded to determine the relative cost of production with the bacterial production system compared to the transgenic alfalfa and goat production systems. The comparisons show two important things. For all systems the key factor in the final price is the amount of spider silk protein produced for whatever measure of volume or weight is used. Second alfalfa can be the cheapest but is subject to the possible regulatory control unless the US develops a more comprehensive approach to GMOs. The silkworm analysis was not completed due to a variety of confounding factors. The primary one was that if the production were shifted overseas then the cost would likely be nearly equivalent to current silk prices of $5-15/kg. However if concerns about the location of production is important then it would need to be done in the US and initial costs would be much higher but if the later scenario is utilized then the cost would be lowered but it was not possible to calculate exact costs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Intramolecular Noncovalent Interactions Facilitate Thermally Activated Delayed Fluorescence (TADF)

In the conventional molecular design of thermally activated delayed fluorescence (TADF) organic emitters, simultaneously achieving a fast rate of reverse intersystem crossing (RISC) from the triplet to the singlet manifold and a fast rate of radiative decay is a challenging task. A number of recent experimental data, however, point to TADF emitters with intramolecular π-π interactions as a potential pathway to overcome the issue. Here, we report a comprehensive investigation of TADF emitters with intramolecular π…π or lone-pair…π non-covalent interactions. We uncover the impact of those intramolecular non-covalent interactions on the TADF properties. In particular, we find that folded geometries in TADF molecules can trigger lone-pair…π interactions, introduce a n → π* character of the relevant transitions, enhance the singlet-triplet spin-orbit coupling, and ultimately greatly facilitate the RISC process. Furthermore, this work provides a robust foundation for the molecular design of a novel class of highly efficient TADF emitters in which intramolecular non-covalent interactions play a critical function.

36 MATERIALS SCIENCE↗