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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 OSTI2021

Analysis of Spectral Lines in Large Databases of Synthetic Spectra for Massive Stars

In this paper, we describe a program that identifies in the optical spectrum the main parameters of a spectral line, namely the initial and final wavelengths, and the line depth. Moreover, using numerical calculations, it identifies and removes adjacent lines. Next, the program calculates the equivalent width and the FWHM. The software was tested in a sample of 300 lines in two databases of synthetic spectra generated by the CMFGEN and PoWR codes, and 300 lines in observed spectra from the IACOB database, showing a Gaussian distribution of relative errors, from which it is inferred that 80% of the measured lines have errors less than 17% and only 5% of the lines have errors greater than 26%. The program was also run on the entire database of 45,000 CMFGEN and 202 POWR synthetic spectra, generating a library of H i, He i, and He ii lines necessary to feed the FITspec code for the derivation of stellar parameters: effective temperature, surface gravity, and luminosity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗
DOE OSTI2022

Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. Here, we interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

Yang, Jason↗
DOE OSTI2024

Photonuclear Physics in SCALE

Radiation shielding modeling involves a wide range of applications, such as power reactors, critical experiments, portable sources, linear accelerators, and fusion systems. Particle energy ranges vary from thermal energies to fast energy spectra. MAVRIC, as the radiation shielding sequence of the SCALE code system [1] developed and maintained by Oak Ridge National Laboratory, is capable of modeling both neutron and photon interactions in matter and is widely used by the US Nuclear Regulatory Commission, Department of Energy, the National Nuclear Security Administration, and radiation shielding practitioners all around the world. Although most applications don’t involve highly energetic photons that can cause photonuclear reactions and create coupled chains of high energy, ionized electrons and bremsstrahlung radiation within a material medium, when present, these reactions can have a significant impact upon the downstream particle fluxes from incident photons above 20 MeV. For example, linear accelerators used for medical isotope production are examples of applications in which high-energetic photons are generated and used for driving or controlling other components of a complex facility. Radiation shielding analyses of such a facility would require accounting for any and all significant sources of radiation. Unfortunately, MAVRIC included no data and methods to model either photonuclear reactions or the bremsstrahlung photons until this work, which enabled accounting for both. Photonuclear reaction cross sections were imported from the Evaluated Nuclear Data File libraries, and corresponding methods were implemented in the SCALE code system; see Section 2 for how the nuclear data are imported and used. Bremsstrahlung photons were modeled by assuming thick-target approximation whereby all generated electrons and positrons due to photon interactions are assumed to be absorbed within the same material in which they were born, yielding a quick particle transport simulation method by using fast lookup tables for generating subsequent gammas; see Section 4 for details. A suite of verification models was used to test implemented methods and associated data. Tests yielded satisfactory results for the new features. Enhancements will be released as part of the next major SCALE release (v7.0), which is anticipated to be deployed in 2025, with beta releases available in the interim.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

TChem v2.0 - A Software Toolkit for the Analysis of Complex Kinetic Models

TChem is an open source software library for solving complex computational chemistry problems and analyzing detailed chemical kinetic models. The software provides support for: complex kinetic models for gas-phase and surface chemistry; thermodynamic properties based on NASA polynomials; species production/consumption rates; stable time integrator for solving stiff time ordinary differential equations; and, reactor models such as homogenous gas-phase ignition (with analytical Jacobian matrices), continuously stirred tank reactor, plug-flow reactor. This toolkit builds upon earlier versions that were written in C and featured tools for gas-phase chemistry only. The current version of the software was completely refactored in C++, uses an object-oriented programming model, and adopts Kokkos as its portability layer to make it ready for the next generation computing architectures i.e., multi/many core computing platforms with GPU accelerators. We have expanded the range of kinetic models to include surface chemistry and have added examples pertaining to Continuously Stirred Tank Reactors (CSTR) and Plug Flow Reactor (PFR) models to complement the homogenous ignition examples present in the earlier versions. To exploit the massive parallelism available from modern computing platforms, the current software interface is designed to evaluate samples in parallel, which enables large scale parametric studies, e.g. for sensitivity analysis and model calibration.

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

A-SST Initial Specification

The U.S. Army Research Office (ARO), in partnership with IARPA, are investigating innovative, efficient, and scalable computer architectures that are capable of executing next-generation large scale data-analytic applications. These applications are increasingly sparse, unstructured, non-local, and heterogeneous. Under the Advanced Graphic Intelligence Logical computing Environment (AGILE) program, Performer teams will be asked to design computer architectures to meet the future needs of the DoD and the Intelligence Community (IC). This design effort will require flexible, scalable, and detailed simulation to assess the performance, efficiency, and validity of their designs. To support AGILE, Sandia National Labs will be providing the AGILE-enhanced Structural Simulation Toolkit (A-SST). This toolkit is a computer architecture simulation framework designed to support fast, parallel, and multi-scale simulation of novel architectures. This document describes the A-SST framework, some of its library of simulation models, and how it may be used by AGILE Performers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗
DOE OSTI2021

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗
DOE OSTI2023

Whole genome sequencing of Mycobacterium bovis directly from clinical tissue samples without culture

Advancement in next generation sequencing offers the possibility of routine use of whole genome sequencing (WGS) for Mycobacterium bovis (M. bovis) genomes in clinical reference laboratories. To date, the M. bovis genome could only be sequenced if the mycobacteria were cultured from tissue. This requirement for culture has been due to the overwhelmingly large amount of host DNA present when DNA is prepared directly from a granuloma. To overcome this formidable hurdle, we evaluated the usefulness of an RNA-based targeted enrichment method to sequence M. bovis DNA directly from tissue samples without culture. Initial spiking experiments for method development were established by spiking DNA extracted from tissue samples with serially diluted M. bovis BCG DNA at the following concentration range: 0.1 ng/μl to 0.1 pg/μl (10 –1 to 10 –4 ). Library preparation, hybridization and enrichment was performed using SureSelect custom capture library RNA baits and the SureSelect XT HS2 target enrichment system for Illumina paired-end sequencing. The method validation was then assessed using direct WGS of M. bovis DNA extracted from tissue samples from naturally (n = 6) and experimentally (n = 6) infected animals with variable Ct values. Direct WGS of spiked DNA samples achieved 99.1% mean genome coverage (mean depth of coverage: 108×) and 98.8% mean genome coverage (mean depth of coverage: 26.4×) for tissue samples spiked with BCG DNA at 10 –1 (mean Ct value: 20.3) and 10 –2 (mean Ct value: 23.4), respectively. The M. bovis genome from the experimentally and naturally infected tissue samples was successfully sequenced with a mean genome coverage of 99.56% and depth of genome coverage ranging from 9.2× to 72.1×. The spoligoyping and M. bovis group assignment derived from sequencing DNA directly from the infected tissue samples matched that of the cultured isolates from the same sample. Our results show that direct sequencing of M. bovis DNA from tissue samples has the potential to provide accurate sequencing of M. bovis genomes significantly faster than WGS from cultures in research and diagnostic settings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Status of HEU-Pb in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook

The Department of Energy invests tens of millions of dollars each year to develop the next generation of nuclear engineering modeling & simulation (M&S) tools. These tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers become more powerful, we are able to enhance resolution in our calculations. This improved resolution is taking us to a point where the limitations of simulation capability are in the quality of data, including our ability to quantify the uncertainty and sensitivity of the data. In order to model systems of interest with increasing accuracy, the industry must improve key nuclear data measurements. Thus, M&S tools need evaluated and quality-assured experimental data for validation purposes. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles and evaluates experiment data in a handbook that can be used by criticality safety engineers and others to validate computer codes and cross-section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. Figure 1 organizes all the benchmark evaluations that have been performed by the isotope of interest, in this case Pb, and the average neutron energy the system. Compared to other isotopes of interest for nuclear applications, there are few benchmark evaluations for Pb systems. The lack of integral measurements to determine errors in Pb cross-section data has caused the latest nuclear cross-section libraries to over/underestimate k eff compared to experimental results. Therefore, this evaluation fills an important knowledge gap in benchmark evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Design Principles for the Synthesis of Self-Pillared ZSM-5 Zeolite Nanosheets

The design of next-generation materials for emerging energy and environmental applications heavily relies on empirical approaches to direct nonclassical nucleation and crystal growth pathways, polymorphism, intercrystalline transformations, and seed-assisted growth processes. A long-standing obstacle to nanoporous materials design is the complexity of their crystallization, which hinders the development of predictive models and/or physical descriptors that can guide their synthesis. In this study, we use a combination of state-of-the-art synthesis, characterization, and computational design to prepare hierarchical MFI-type zeolites, which we couple with benchmark catalytic testing to assess structure-property-performance relationships. These hierarchical materials are intergrowths of two commercially relevant zeolite frameworks, MFI and MEL, prepared as self-pillared pentasil (SPP) zeolites through seed-assisted, organic-free syntheses for which little theoretical guidance existed. Here, by comparing a large library of zeolite seeds with different pore sizes, dimensions, and structural composite building units, we determined the relative impact of seed and silica source selection, among other synthesis variables. Combined experimental and computational studies are used to test several hypotheses in literature to rationalize the choice of seed structure and establish a more robust selection criteria for seed-assisted synthesis of zeolites. Specifically, we show that a data-driven approach to develop structural descriptors correlates to new, facile routes to rationally design SPP zeolites, addressing knowledge gaps in the fundamental understanding of (non)classical crystal growth mechanisms that are characteristic of nanoporous aluminosilicates.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modernization efforts for the R -Matrix code SAMMY [Abstract]

The R-Matrix code SAMMY is a widely used nuclear data evaluation code focused on the resolved range, which includes corrections for experimental effects. The code is still mostly written in Fortran 77, and uses a memory management system suitable for the time of its initial writing (1984). A modernization effort is under way to bring the code in-line with modern software development practices. A continuous-integration testing framework was added, automating the large existing set of test cases. It is run on every commit. The memory management was updated to current standard practices suitable for modern software analysis tools. The code can be obtained from https://code.ornl.gov/RNSD/SAMMY. The resonance parameters and covariance information are now stored in C++ objects shared by SAMMY and AMPX, the processing code that generates nuclear data libraries for SCALE. This allows for easier maintenance and access to the resonance parameters inside and outside of SAMMY. This feature is already used by accessing and changing parameters in memory in the Bayesian Monte Carlo Evaluation Framework for Cross Sections Nuclear Data and Integral Benchmark Experiments project, Further plans include the switch to the ENDF reading and writing routines in AMPX, as these routines are more robust, easier to maintain, and support more features. Of note here is support for the new GNDS format. Previously it wasn’t easy to share the full covariance matrix for evaluations containing more than one isotope due to limitations on the ENDF format; this is now supported in GNDS. The data are currently available in a binary SAMMY format and can be exported to GNDS to make them more widely available and sharable. The next step will be to use the same resonance processing code at 0K in AMPX and SAMMY as one of the available Reich-Moore R-Matrix formalism. The first step toward this goal is to isolate the reconstruction into a module that takes resonance parameters as its input and does not depend on SAMMY global parameters. This goal has been achieved and it should now be possible to more easily change the resonance formalism and add enhancements as the Phenomenological R-Matrix parameterization of direct, doorway, and compound nuclear reactions discussed elsewhere on this conference. This concerted modernization and enhancement effort provides multiple advantages to the nuclear data community. It will allow parameter optimization using enhanced formalisms, including experimental effects, that better match complex experimental data. Then those evaluated parameters can immediately be passed off to AMPX to be reconstructed with the exact same cross section model and be put into a data library for subsequent testing using SCALE and the Valid Benchmark suite or other suitable benchmark suites.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Invited: Software defined accelerators from learning tools environment

Next generation systems, such as edge devices, will need to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. In this paper we present the Software Defined Accelerators From Learning Tools Environment (SODALITE), an automated open source high-level ML framework-to-verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets. The SODALITE approach will implement optimal designs by seamlessly combining custom components generated through high-level synthesis (HLS) with templated and fully tunable Intellectural Properties (IPs) and macros, integrated in an extendable resource library. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimensions.

High-Level Synthesis, Accelerators, Hardware-Softw↗
DOE OSTI2020

SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators

Next generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. This paper discusses the SODA synthesizer, an automated open source high-level ML framework-to-Verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets based on the LLVM infrastructure. The SODA synthesizers will allow implementing optimal designs by combining templated and fully tunable IPs and macros, and fully custom components generated through high-level synthesis. All these components will be provided through an extendable resource library, characterized with both commercial and open source logic design flows. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimension

Minutoli, Marco↗
DOE OSTI2020

Fast-Spectrum Critical Assemblies with a Pb-HEU Core Surrounded by a Copper Reflector

The Department of Energy invests tens of millions of dollars each year to develop the next generation of nuclear engineering modeling & simulation (M&S) tools. These M&S tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers become more powerful, we are able to enhance resolution in our calculations. This improved resolution is taking us to a point where the limitations of simulation capability are in the quality of data, including our ability to quantify the uncertainty and sensitivity of the data. In order to accurately model systems of interest, the industry must improve key nuclear data measurements and our confidence of how well we understand the data. Thus, M&S tools need evaluated and quality-assured experimental data for validation purposes. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles benchmark experiment data in a handbook that can be used by criticality safety engineers to validate computer codes and cross-section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. Figure 1 organizes all the benchmark evaluations that have been performed by the isotope of interest, in this case Pb, and the neutron energy within the system. Compared to other isotopes of interest for nuclear applications, there are few benchmark evaluations for Pb systems. This has caused the latest nuclear cross-section libraries to over/underestimate changes in the neutron population compared to experimental results. Therefore, this evaluation fills an important knowledge gap in benchmark evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Hyperspectral X-ray Imaging: Progress Towards Chemical Analysis in the SEM

The Hyperspectral X-ray Imaging (HXI) project will enable non-destructive chemical-state determination of nano-scale samples in the electron microscope for nuclear safeguards applications. To efficiently measure chemical state through electron-excited X-ray emission requires a combination of wide spectral bandwidth, high resolution, and high count rate capability. We are building a next-generation X-ray detector based on an array of transition edge sensors (TESs) to make these measurements possible and routine in the scanning electron microscope (SEM). Leveraging the large pixel densities afforded by microwave multiplexing readout and continuous, uninterrupted operation of a cryogen-free dilution refrigerator, this instrument will have efficiency to allow chemical species identification of nano-scale samples in hours instead of days to weeks. We describe prototype pixel designs for this HXI instrument, comprising three pixel types that will make up the hybrid TES array. Engineering design of the integrated HXI cryostat and SEM system is in progress, with full detector commissioning expected in Spring 2021 followed closely by full-scale integration with the SEM. Furthermore, we also report on the commissioning of a complementary TES-based X-ray emission spectroscopy platform for bulk samples to build a spectral library for HXI sample identification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Estimating soil thermal inertia profiles from the passive equilibration of a temperature probe: Supporting data and code

This dataset contains measurements of soil thermal properties and organic layer thickness at three field sites on the Seward Peninsula near Nome, Alaska, USA. Code is also provided for estimation of soil thermal properties from temperature data, including a forward heat transfer mode, a library of simulated temperature data, and an inversion scheme to retrieve thermal properties from temperature measurements (Lamb et al., 2025). This dataset and code is published in support of the manuscript “Estimating Soil Thermal Inertia Profiles from the Passive Equilibration of a Temperature Probe” by Lamb et al., 2025, in which an experimental thermal measurements technique is developed. The dataset contains measurements of soil thermal properties and organic layer thickness obtained with an industry standard thermal properties analyzer and direct observations, as well as data generated using the experimental technique to estimate thermal properties and soil structure from temperature observations. All data is reported in .csv format, and all code is written in Python 3.7 using standard packages. This research was performed as a part of the NGEE Arctic project, which aims to advance model predictions of arctic carbon cycle responses to a changing climate over the 21st Century.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Thermally Activated Delayed Photoluminescence: Deterministic Control of Excited-State Decay

Thermally activated photophysical processes are ubiquitous in numerous organic and metal-organic molecules, leading to chromophores with excited state properties that can be considered an equilibrium mixture of the available low-lying states. Relative populations of the equilibrated states are governed by temperature. Such molecules have been devised as high quantum yield emitters in modern organic light-emitting diode technology and for deterministic excited state lifetime control to enhance chemical reactivity in solar energy conversion and photocatalytic schemes. This recent discovery of thermally activated photophysics at CdSe nanocrystal-molecule interfaces enables a new paradigm wherein molecule-quantum dot constructs are used to systematically generate material with predetermined photophysical response and excited state properties. Semiconductor nanomaterials feature size-tunable energy level engineering, which considerably expands the purview of thermally activated photophysics beyond what is possible using only molecules. This Perspective is intended to provide a non-exhaustive overview of the advances that led to the integration of semiconductor quantum dots in thermally activated delayed photoluminescence (TADPL) schemes and to identify important challenges moving into the future. The initial establishment of excited state lifetime extension utilizing triplet-triplet excited-state equilibria is detailed. Next, advances involving the rational design of molecules composed of both metal-containing and organic-based chromophores that produce the desired TADPL are described. Lastly, the recent introduction of semiconductor nanomaterials into hybrid TADPL constructs is discussed, paving the way towards the realization of fine-tuned deterministic control of excited state decay. It is envisioned that libraries of synthetically facile composites will be broadly deployed as photosensitizers and light emitters for numerous synthetic and optoelectronic applications in the near future.

14 SOLAR ENERGY↗