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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRS2023

Exploring Synoptic Display Concepts for Hybrid-Electric Airliner Flightdecks

The advent of hybrid-electric aircraft concepts with distributed propulsion has resulted in relatively complicated powertrain designs. Determining the optimal approach to display information about the hybrid system in the flightdeck has taken on great importance. At NASA Glenn Research Center, the concept SUbsonic Single Aft eNgine (SUSAN) hybrid airliner has been modeled inside a flight simulator where different flight deck displays can be evaluated. This work focused on ways to display information that reduce clutter and information overload without degrading the crew’s ability to operate the aircraft and respond properly to emergencies. The displays’ layout and symbologies were created to be intuitive to the pilots. This paper documents iterations of synoptic displays and discusses the rationale and tradeoffs behind each variation.

Pilot Displays↗
NASA NTRSDate not supplied

Hybrid Suitcase Demonstrator

The need for a small, transportable hybrid motor used to demonstrate and test various hybrid propulsion concepts was identified. Demonstrations would be for public audiences to explain basic hybrid rocket concepts. Tests would be for scientific purposes and hands-on training of engineers. Funded by Technical Excellence and NESC, the system as a whole has served as a learning tool for the NASA project cycle. Coming out of the Preliminary Design Review (PDR), the project was split into two separate configurations, one for demonstrations and one for tests; each would have its own oxidizer feed system. The Demonstration system will be a simple, low pressure system with the ability to recycle for multiple motor firings. The grain design will be a fixed configuration for all firings and utilize a clear material such that the combustion process is visible. The Test Readiness Review has been held and final actions are being pursued before the initial firing.

hybrid rocket↗
NASA NTRSDate not supplied

Flight Deck Design of a Hybrid Turbine/Electric Passenger Aircraft

NASA is exploring the development of a 180-passenger subsonic single engine aft turbine aircraft, The aft turbine provides electric power in a hybrid design to wing mounted electric engines, creating a highly efficient, high-bypass-ratio fan equivalent. The SUbsonic Single Aft eNgine (SUSAN) aircraft is being developed as a sustainable subsonic regional aircraft that seeks to reduce emission levels by 50% in the next few decades. Pilot-in-the-loop studies were conducted at the NASA Langley Research Center in Hampton, Virginia, to explore the flight deck design for the hybrid electric aircraft. Following modern trends in commercial aircraft flight decks with full time augmented controls and a quiet and dark philosophy, single throttle and simplified engine displays were developed for the SUSAN aircraft. The aircraft includes a single aft mounted turbine engine and 16 wing mounted electric fans. The final design was developed from feedback received during an earlier pilot-in-the-loop study where one, two, and three throttles were tested in standard airline operations, including various failures of the turbine and electric engines. Current flight deck designs normally provide control inceptors for each propulsion engine and an engine display for all primary aircraft engine parameters. With full time augmentation expected, a single throttle control with autothrottle always engaged, even during failures, is desired. Augmentation of flight controls using distributed thrust also requires full time control of the electric engines using automation. Additionally, electric engine thrust is augmented during climb based on battery state of charge. Thrust augmentation changes faster than human reaction time and therefore requires full-time automation. A pilot-in-the-loop study was conducted at the NASA Langley Research Center in Hampton, Virginia, to test the final design of the single throttle with simplified engine displays. Fourteen airline pilots evaluated the single throttle and engine display concept. Electric engine failures included one, four symmetric, and eight non-symmetric electric engine failures. The turbine engine was evaluated for complete and partial failure during critical phases of flight to include takeoff as well as enroute. Unexpected go-arounds increase workload and require significant throttle manipulation. Go-arounds were included to ensure the single throttle was usable for all phases of flight. Failures during takeoff required a return to the departure field and failures enroute required a diversion except for one and four electric engine failures as these failures did not affect aircraft flyability or range. There are currently no Part 25 aircraft certified with hybrid systems or electric engines with batteries as emergency propulsion. For turbine engine failures in the SUSAN aircraft design, range is limited to 30 minutes at full power. Battery state of charge and battery health displays were developed and tested for usability and to determine how well they supported pilot decisions for alternate airports during emergency diversions. Novel displays using shape and color were developed to provide immediate feedback when state of charge became critical. This paper details the pilot study including pilot feedback supporting the potential for increased automation and a single throttle control. Detailed recommendations are provided for a novel single throttle control and additional pilot controls to support selection of engines during start, shutdown, and engine troubleshooting procedures. This design deviates significantly from current practice of providing throttles for each propulsion engine. Engine display recommendations are provided based on pilot feedback during a guided post-evaluation interview. Battery state of charge and battery health display recommendations were collected from all airline crews. The simplified engine displays design was rated excellent as measured with a usability scale. Quantitative metrics include airspeed tracking, time to complete checklists, time to make diversion decisions and the quality of the diversion decision. Recommendations for future studies are documented with supporting research and current observations about upcoming flight deck certifications.

autothrottle↗
NASA NTRSDate not supplied

Flight Deck Design of a Hybrid Turbine/Electric Passenger Aircraft

NASA is exploring the development of a 180-passenger subsonic single engine aft turbine aircraft, The aft turbine provides electric power in a hybrid design to wing mounted electric engines, creating a highly efficient, high-bypass-ratio fan equivalent. The SUbsonic Single Aft eNgine (SUSAN) aircraft is being developed as a sustainable subsonic regional aircraft that seeks to reduce emission levels by 50% in the next few decades. Pilot-in-the-loop studies were conducted at the NASA Langley Research Center in Hampton, Virginia, to explore the flight deck design for the hybrid electric aircraft. Following modern trends in commercial aircraft flight decks with full time augmented controls and a quiet and dark philosophy, single throttle and simplified engine displays were developed for the SUSAN aircraft. The aircraft includes a single aft mounted turbine engine and 16 wing mounted electric fans. The final design was developed from feedback received during an earlier pilot-in-the-loop study where one, two, and three throttles were tested in standard airline operations, including various failures of the turbine and electric engines. Current flight deck designs normally provide control inceptors for each propulsion engine and an engine display for all primary aircraft engine parameters. With full time augmentation expected, a single throttle control with autothrottle always engaged, even during failures, is desired. Augmentation of flight controls using distributed thrust also requires full time control of the electric engines using automation. Additionally, electric engine thrust is augmented during climb based on battery state of charge. Thrust augmentation changes faster than human reaction time and therefore requires full-time automation. A pilot-in-the-loop study was conducted at the NASA Langley Research Center in Hampton, Virginia, to test the final design of the single throttle with simplified engine displays. Fourteen airline pilots evaluated the single throttle and engine display concept. Electric engine failures included one, four symmetric, and eight non-symmetric electric engine failures. The turbine engine was evaluated for complete and partial failure during critical phases of flight to include takeoff as well as enroute. Unexpected go-arounds increase workload and require significant throttle manipulation. Go-arounds were included to ensure the single throttle was usable for all phases of flight. Failures during takeoff required a return to the departure field and failures enroute required a diversion except for one and four electric engine failures as these failures did not affect aircraft flyability or range. There are currently no Part 25 aircraft certified with hybrid systems or electric engines with batteries as emergency propulsion. For turbine engine failures in the SUSAN aircraft design, range is limited to 30 minutes at full power. Battery state of charge and battery health displays were developed and tested for usability and to determine how well they supported pilot decisions for alternate airports during emergency diversions. Novel displays using shape and color were developed to provide immediate feedback when state of charge became critical. This paper details the pilot study including pilot feedback supporting the potential for increased automation and a single throttle control. Detailed recommendations are provided for a novel single throttle control and additional pilot controls to support selection of engines during start, shutdown, and engine troubleshooting procedures. This design deviates significantly from current practice of providing throttles for each propulsion engine. Engine display recommendations are provided based on pilot feedback during a guided post-evaluation interview. Battery state of charge and battery health display recommendations were collected from all airline crews. The simplified engine displays design was rated excellent as measured with a usability scale. Quantitative metrics include airspeed tracking, time to complete checklists, time to make diversion decisions and the quality of the diversion decision. Recommendations for future studies are documented with supporting research and current observations about upcoming flight deck certifications.

autothrottle↗
DOE OSTI2021

Opportunities and Challenges for Nuclear-Renewable Hybrid Energy Systems

Nuclear-renewable hybrid energy systems (also known as integrated nuclear-renewable systems) are conceptual systems that have two or more energy inputs and produce two or more products. They maximize profitability, and usually equipment utilization, by adjusting between the products depending upon the availability of resources and market value. For example a nuclear-renewable hybrid energy system that produces electricity and hydrogen would maximize sales of electricity when its price is high and shift to hydrogen production when the electricity price is low. This presentation summarizes the results of several economic analyses of nuclear-renewable hybrid energy systems and they key overall conclusions from those analyses.

ENERGY PLANNING, POLICY, AND ECONOMY,ENVIRONMENTAL↗
DOE OSTI2021

Case Study: Hybrid Carbon Conversion Using Low-Carbon Energy Sources in Coal-Producing States

The demand for more carbon efficient power sources and a decrease in natural gas prices has decreased the desire for coal power. This decrease in demand has led to massive job losses in coal mining regions over the past decade. The purpose of this project is to develop a hybrid energy system utilizing both a coal power plant and advanced reactor, which is competitive with natural gas by improving on profitability and decreasing carbon emissions. This report details the problem with a summary of the impact on the coal industry and the availability of renewable energy sources in the Appalachian region. Because of the geography of the region, variable renewable energy sources are not available without significant size and siting restrictions. However, biomass in the form of wood waste is abundant and can be used as a carbon neutral energy source. Combining biomass and coal processing, in addition to thermal power plants, can increase system profits and efficiency by providing peaking power and conversion opportunities for secondary markets. The electric load is based on publicly available demand data from Appalachian Power, which services the western Virginia and southern West Virginia in the Appalachian region. The demand information is combined by service, normalized, and scaled to an average demand of 1000 kW, which will be the basis for sizing the hybrid energy system. A traditional screening curve analysis for a coal plant and advanced reactor shows that the least cost design varies significantly based on the assumed discount rate and capital recovery period. An optimization program to size the design in TEAL based on the load curve gives 10 optimal designs, all with a negative resulting net present value (NPV) and a coal plant capacity of less than 15%. Including profits from selling captured carbon at a flat rate results in a positive NPV; however, the coal capacity factor only increases to about 40%. There are limitations with this optimization as well since the price of CO2 is likely to decrease as more is sold to the conversion market. The suggested design will combine coal power, an advanced reactor, and coal and biomass coprocessing to produce a variety of products that can be sold to the conversion market while increasing system efficiency. The analysis of conversion pathways for coal and biomass reveals that multiple options will need to be included in the analysis to produce the optimal system design. Three systems will be optimized and compared to determine the best design based on the figures of merit of total NPV and cost of carbon avoided. The first system will include a coal power plant and an advanced reactor that will sell electricity to the grid to meet demand and sell captured carbon to the conversion market. The second system adds a high-temperature steam electrolysis plant, which will utilize electricity during times of low demand to produce hydrogen and sell it to the conversion market. The third system adds biomass and coal processing with options for hydrocarbon oils, syngas to be produced for the conversion market, and electricity generation to power components within the system or provide peaking power. This analysis will be based on a new approach that combines traditional screening curve methods with a dispatch algorithm that optimizes the system based on the opportunity cost of different production options. The resulting optimization algorithm should provide results with less processing time than HERON’s decision tree method. The results from this analysis will determine an optimal design and reinforce the benefits of coal power when used in a hybrid energy system. The initial results show that the addition of a secondary market for carbon sales could result in a positive NPV and increases the capacity factor of the coal plant as compared to a design with only sales to the electricity market. The addition of more markets and additional coal consumption from biomass coprocessing could increase NPV further, replace carbon in other markets through the sale of biomass-derived hydrocarbons, and demonstrate the value of coal power technology.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Building the Case for Hybrid Energy Developments: Webinar Recording Day 2

INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Building the Case for Hybrid Energy Developments: Webinar Recording Day 2

INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Deterministic-Monte Carlo Hybrid Methods for Eigenvalue Sensitivity Coefficient Calculations

The TSUNAMI suite within the SCALE code package includes several methods for generating sensitivity data, including multigroup (MG) and continuous-energy (CE) capabilities. For generating sensitivities with CE data, three methods are available in SCALE 6.3.0: (1) the iterated fission probability (IFP) method with the KENO Monte Carlo transport solver, (2) IFP with the Shift Monte Carlo transport solver, and (3) the Contributon-Linked eigenvalue sensitivity/Uncertainty estimation via Tracklength importance Characterization (CLUTCH) with the KENO Monte Carlo transport solver. Currently, it is difficult to generate accurate sensitivities with large reflectors when using the CLUTCH method, specifically with fissionable and hydrogenous materials. To address this issue, the work presented herein examines a methodology to calculate the adjoint flux externally with the 3D deterministic SN transport code DENOVO in SCALE; the result is then read directly into the CLUTCH-TSUNAMI sequence. This hybridization method replaces the Monte Carlo F*(r) calculation in CLUTCH while still utilizing the forward calculation. The critical benchmark HEU-MET-FAST-028-001 is used to generate sensitivities based on the inability of CLUTCH to generate accurate sensitivities. Results from the hybrid method appear to generate sensitivity values that are in excellent agreement with direct perturbations. Although further testing is needed, the method provides promising results for the development and utility of a hybrid method for use in TSUNAMI.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Assessing the Economic Value of Underground Thermal Storage for Hybrid Geothermal Power

Solutions are needed to address resource adequacy in the electric power system for highly decarbonized systems. The storage duration, the length of time a storage device can provide continuous output at its rated capacity, must be sufficient to receive full credit toward resource adequacy. Longer peaks and high fractions of variable renewable generation have increased the required duration to potentially seasonal durations. Underground Thermal Energy Storage (UTES) can be adapted to a hybrid storage power plant or heating and cooling applications to satisfy the need for long-duration storage. In this study, we use the Renewable Energy Deployment System (ReEDS) capacity expansion model to evaluate the increase in value for an enhanced geothermal system (EGS) resources by adding UTES. In modeled scenarios, using geothermal without storage as a baseline we compare the increase in economic value for plants with a range of storage characteristics. The added value of a hybrid storage plant changes depending on assumptions including the length of storage duration, efficiency, and ability to charge storage from the grid during periods of low energy prices. Relating proposed characteristics for geothermal UTES hybrids to the modeled economic value provides insight into economically viable costs for developing UTES as well as what combination of technology characteristics and future energy and policy assumptions drive significant value increases.

capacity expansion model↗
NASA NTRSDate not supplied

Failure Behavior and Control-Based Mitigation for a Parallel Hybrid Propulsion System

NASA is pursuing research to advance Electrified Aircraft Propulsion (EAP) technologies that address fuel burn and emission reduction goals. EAP brings the potential for improved performance over the state of the art. However, for these systems to be practical and certifiable, they need to possess adequate robustness to adverse conditions including a variety of system failures that are not applicable to conventional turbofans today. Numerous EAP concepts interface gas turbine engines with an electrical power system that includes electric machines and sometimes electrical energy storage. The expansion of the powertrain increases the probability of encountering a failure and introduces new failure modes. Failures within the electrical power system may also impact the gas turbine engine(s) to which the electrical powertrain is coupled. This effort investigates failures originating in the electrical power system and their impact on the parallel hybrid propulsion system. Reversionary control strategies are also demonstrated to reduce the impact of the failures. Failure mitigation strategies were devised and employed in simulation. Various failure scenarios were simulated including those occurring during steady state operation, transients, and takeoff and landing scenarios. The timing of the failure and delay in failure identification and activation of mitigation strategies are noteworthy variables in the study. While the system remained stable throughout all failure scenarios, delays in failure identification could result in undesirable conditions such as increased operating temperatures and reduced stall margin. The results demonstrate successful mitigation of failures through reversionary control modes and help to generate confidence in the robustness of the conceptual parallel hybrid propulsion system.

Failure behavior↗
NASA NTRSDate not supplied

Failure Behavior and Control Based Mitigation for a Parallel Hybrid Propulsion System

NASA is pursuing research to advance Electrified Aircraft Propulsion (EAP) technologies that address fuel burn and emission reduction goals. EAP brings the potential for improved performance over the state of the art. However, for these systems to be practical and certifiable, they need to possess adequate robustness to adverse conditions including a variety of system failures that are not applicable to conventional turbofans today. Numerous EAP concepts interface gas turbine engines with an electrical power system that includes electric machines and sometimes electrical energy storage. The expansion of the powertrain increases the probability of encountering a failure and introduces new failure modes. Failures within the electrical power system may also impact the gas turbine engine(s) to which the electrical powertrain is coupled. This effort investigates failures originating in the electrical power system and their impact on the parallel hybrid propulsion system. Reversionary control strategies are also demonstrated to reduce the impact of the failures. Failure mitigation strategies were devised and employed in simulation. Various failure scenarios were simulated including those occurring during steady state operation, transients, and takeoff and landing scenarios. The timing of the failure and delay in failure identification and activation of mitigation strategies are noteworthy variables in the study. While the system remained stable throughout all failure scenarios, delays in failure identification could result in undesirable conditions such as increased operating temperatures and reduced stall margin. The results demonstrate successful mitigation of failures through reversionary control modes and help to generate confidence in the robustness of the conceptual parallel hybrid propulsion system.

Failure behavior↗
DOE OSTI2024

Synthetic Control of Water-Stable Hybrid Perovskitoid Semiconductors

Hybrid metal-halide perovskites and their derived materials have emerged as the next-generation semiconductors with a wide range of applications, including photovoltaics, light-emitting devices, and other optoelectronics. Over the past decade, numerous single-crystalline perovskite derivatives have been synthesized and developed. However, the synthetic methods for these derivatives mainly rely on acidic crystallization conditions. This approach leads to crystals comprising metal halide building blocks, which show problematic stability when directly exposed to water. In this study, a methodology is developed for synthesizing hybrid metal-halide compounds using lead iodide and the zwitterionic bifunctional molecule cysteamine (CYS), to form various perovskitoid structures under a broad pH range. Interestingly, the different pH conditions alter the coordination environment of lead halides, leading to lead-sulfide and lead-nitride covalent bond formation. This modification significantly enhances their stability when in direct contact with water, lasting for months. Photoluminescence measurements and first principal density functional theory (DFT) calculations reveal that the perovskitoids synthesized under basic and acidic pH conditions exhibit a direct bandgap nature, while those synthesized under neutral conditions display an indirect bandgap. This approach opens new avenues for manipulating synthetic methods to develop water-stable hybrid semiconductors suitable for a wide range of applications, such as solid-state light emitters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Regulating Exciton–Phonon Coupling to Achieve a Near‐Unity Photoluminescence Quantum Yield in One‐Dimensional Hybrid Metal Halides

Abstract Low‐dimensional hybrid metal halides are emerging as a highly promising class of single‐component white‐emitting materials for their unique broadband emission from self‐trapped excitons (STEs). Despite substantial progress in the development of these metal halides, many challenges remain to be addressed to obtain a better fundamental understanding of the structure–property relationship and realize the full potentials of this class of materials. Here, via pressure regulation, a near 100% photoluminescence quantum yield (PLQY) of broadband emission is achieved in a corrugated 1D hybrid metal halide C 5 N 2 H 16 Pb 2 Br 6 , which possesses a highly distorted structure with an initial PLQY of 10%. Compression reduces the overlap between STE states and ground state, leading to a suppressed phonon‐assisted non‐radiative decay. The PL evolution is systematically demonstrated to be controlled by the pressure‐regulated exciton–phonon coupling which can be quantified using Huang–Rhys factor S . Detailed studies of the S ‐PLQY relation for a series of 1D hybrid metal halides (C 5 N 2 H 16 Pb 2 Br 6 , C 4 N 2 H 14 PbBr 4 , C 6 N 2 H 16 PbBr 4 , and (C 6 N 2 H 16 ) 3 Pb 2 Br 10 ) reveal a quantitative structure–property relationship that regulating S factor toward 28 leads to the maximum emission.

36 MATERIALS SCIENCE↗