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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Metal–Organic Frameworks for C6 Alkane Separation

The separation of alkane isomers is an important yet challenging process in the petrochemical industry. Being a crucial step to produce premium gasoline components as well as optimum ethylene feed, the current industrial separation by distillation is extremely energy intensive. Adsorptive separation based on zeolite is limited by insufficient adsorption capacity. Metal-organic frameworks (MOFs) hold enormous promise as alternative adsorbents due to their diverse structural tunability and exceptional porosity. Precise control of their pore geometry/dimensions has led to superior performance. In this minireview, we highlight the recent progresses in developing MOFs for the separation of C6 alkane isomers. Representative MOFs are reviewed based on their separation mechanisms. Emphasis is put on the material design rationale for achieving optimal separation capability. Lastly, we briefly discuss the existing challenges, possible solutions, and future directions of this important field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗
DOE OSTI2021

Theory and phase-field simulations of electrical control of spin cycloids in a multiferroic

Cycloidal spin orders are common in multiferroics. One of the prototypical examples is BiFeO 3 (BFO) which shows a large polarization and a cycloidal antiferromagnetic (AFM) order at room temperature. Here, we employ Landau theory and phase-field simulations to analyze the coupled switching dynamics of polarization and cycloidal AFM orders in BFO. We identify 14 types of transitional spin structures between two cycloids and nine electric-field-induced spin switching paths. We demonstrate the electric-field-induced rotation of wave vectors of the cycloidal spins and discover two types of cycloidal spin switching dynamics: fast local spin flips and slow rotation of wave vectors. Also, we construct roadmaps to achieve the switching between any two spin cycloids through multistep applications of electric fields. Furthermore, this paper provides a theoretical framework for the phenomenological description of spin cycloids and a fundamental understanding of the switching mechanisms to achieve electrical control of magnetic orders.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning

We report a distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The Always/Never Safety Framework for Satellite Rendezvous and Proximity Operations and On-Orbit Servicing

Space rendezvous and proximity operations are increasing in numbers, enabling inspections, diagnostics, and maintenance of on-orbit systems. Because collision, loss of control, and unintended damage can impact the system under examination -- and at the extreme, cause system break-up and space debris -- the safety practices for rendezvous and proximity operations can have significant implications for national security. This study examines the applicability of the Always/Never surety framework, which was developed for United States nuclear weapons, as a model safety basis for unmanned space proximity operations. This unclassified framework has understandable safety approaches and principles and focuses on a system being always safenever unsafe. The authors consider that the adapting the framework might present a means for standardization across government and commerce, encouraging a consistent approach and a set of clarifying safety principles and applications for rendezvous and proximity operations. The framework also offers a consistent taxonomy, presents safety and reliability requirements organized by four environment categories, defines accident or abnormal conditions, contributes a strategy for identifying hostile and tactical environments, and enables decision-making for determining if conditions are safe for proximity space operations.

42 ENGINEERING↗
DOE OSTI2026

Localized Heterogeneous Nucleation for Vapor‐Assisted Sequential Deposition of Metal Halide Perovskites

Vapor-assisted hybrid two-step deposition, which combines thermally evaporated inorganic layers with solution-processed organic halides to form halide perovskites, has emerged as a scalable and industry-compatible route for textured tandem photovoltaics. However, this process is often hindered by reaction-limited phase formation, particularly when compact, non-porous, and highly crystalline inorganic layers formed by thermal evaporation restrict subsequent conversion, resulting in incomplete reaction and pronounced depth-dependent heterogeneity. In this study, we introduce a strategy to regulate the inorganic precursor layer by incorporating localized heterogeneous nucleation sites. Sparsely distributed hydrophilic metal oxide species serve as effective nucleation centers during vapor deposition, enabling effective control over film morphology and crystal orientation from the early stages of growth. This tailored inorganic framework facilitates the subsequent incorporation of organic halides, alleviating reaction limitations and suppressing residual unreacted precursors. Consequently, the perovskite films exhibit improved stoichiometric uniformity and enhanced optoelectronic quality, enabling wide-bandgap perovskite solar cells with markedly improved performance and operational stability. This work provides important mechanistic insight into crystal growth engineering of vapor-deposited perovskite thin films.

nucleation↗
DOE OSTI2023

Keeping it simple: simplified frameworks for long-lived particles at neutrino facilities

Modern-day accelerator neutrino facilities are excellent venues for searches for new-physics particles. Many distinct new-physics models predict overlapping signatures and phenomenology in these experiments. In this work, we advocate for the adoption of simplified frameworks when studying these types of new-physics signatures, which are characterized by a small number of primary variables, including particle masses, lifetimes, and production and decay modes/rates that most directly control signal event rates and kinematics. In particular, taking the example of long-lived particles that decay inside a neutrino detector as a test case, we study formulate and study simplified frameworks in the context of light scalars/fermions produced in kaon decays which then decay into final states containing an electron-positron pair. We show that using these simplified frameworks can allow for individual experimental analyses to be applicable to a wide variety of specific model scenarios. As a side benefit, we demonstrate that using this approach can allow for the T2K collaboration, by reinterpreting its search for Heavy Neutral Leptons, to be capable of setting world-leading limits on the Higgs-Portal Scalar model. Furthermore, we argue the simplified framework interpretation can serve as a bridge to model identification in the hopeful detection of a new-physics signal. As an illustration, we perform a first determination of the likelihood that, in the presence of a new-physics signal in a detector like the DUNE ND-GAr, multiple different new-physics hypotheses (such as the Higgs-Portal Scalar and Heavy Neutral Lepton ones) can be disentangled. We demonstrate that this model discrimination is favorable for some portions of detectable new-physics parameter space but for others, it is more challenging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Understanding and Tuning the Electronic Properties of Prussian Blue Analogues

Prussian blue analogues (PBAs) have attracted increasing interest owing to their potential applications in various fields such as energy storage and conversion, neuromorphic computing, and magnetic switching. With a general formula of A x M N [M C (CN) 6 ], they feature an open framework that provides abundant channels for diffusion of alkali metal ions A and allows flexible compositional control of transition metal ions M N and M C . The oxidation states of transition metal ions can be tuned by adjusting the amount (x) of alkali ions A. Here, we carried out density functional theory calculations combined with experimental measurements to investigate the effects of transition metal ions, alkali ions, and oxidation states on the electronic properties of PBAs. Our calculations found that the band gaps of PBAs can be tuned from close to 0 eV to more than 4 eV. Experimentally, we introduced the synthesis/characterization of five previously unreported PBAs (M N = Ru, Os; M C = Fe, Ru, and Os) to complete the nine stable M N :M C transition metal combinations in group VIII of the periodic table. The optically measured intervalence charge transfer excitation energies of group VIII PBAs are consistent with calculated band gaps. They demonstrate wide band gap tunability by adjusting transition metals and oxidation states, enabling semiconductor-to-metal transitions for memristor applications and enhancing electronic conductivity for battery applications. In conclusion, this work provides a computational/experimental database of electronic properties versus structural compositions for PBAs.

Electrical conductivity↗
DOE OSTI2026

Hydrogen-Bonded Organic Framework Enables Phase-Pure Layered Tin Perovskite Nanowires for Room-Temperature Lasing

Room-temperature lasing is a key milestone in the development of miniaturized optoelectronic and photonic devices. We present a simple approach to synthesize phase-pure quasi-2D layered tin perovskite nanowires with varying quantum well thicknesses (n = 1 to 4). By incorporating a new organic spacer capable of forming a hydrogen-bonded organic framework, this method promoted anisotropic crystal growth and enhanced lattice rigidity. Furthermore, introducing molecular intercalants enabled controlled crystallization into well-defined nanowires that function as Fabry−Pérot cavities. Cavities made from n = 2 to 4 perovskites support efficient and robust nearinfrared, room-temperature optically pumped lasing with the threshold as low as 75.8 μJ/cm 2 , cavity quality factor over 3000, and negligible degradation over 106 pulses. A cleaved coupled nanolaser was fabricated as a proof-of-concept device for photonic applications.

Cavities↗
DOE OSTI2025

Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
DOE OSTI2026

Balancing moisture and oxygen can match the crystallization dynamics of inert halide perovskite processing

Understanding crystallization in ambient environments is essential for scaling the fabrication of halide perovskite solar cells. Antisolvent-free perovskite deposition offers improved compatibility with high-throughput processing but introduces distinct crystallization dynamics relative to the more ubiquitous use of antisolvents in lab-scale perovskite fabrication. These dynamics are driven by interactions between solutes, solvent and the deposition environment. Using in situ wide-angle X-ray scattering during spin-coating and annealing, we demonstrate how relative humidity (RH) and oxygen, can be tuned to drive polytype evolution during ambient crystallization of formamidinium lead iodide to match that of inert synthesis and achieve comparable film and device quality. In an inert (N 2 ) environment, we find that perovskite films follow a well-established 2H → 3C phase transformation with a short period of coexistence of the 4H and 6H phase during heating. During crystallization in dry air (RH 0%), the added presence of oxygen leads to the dominance of 4H intermediate for an extended duration, establishing a 2H → 4H → 3C pathway. Introducing low humidity (RH 10%) suppresses the 4H phase to a short-lived intermediate above 100 °C, facilitating a more direct transition to the desired 3C phase and almost replicating the crystallization behavior observed under inert conditions. Interestingly, films crystallized under RH 10% show a lower onset temperature for the perovskite 3C phase than under N 2 . At higher humidity (RH 40%), the strong interaction of oxygen and moisture with iodoplumbates appears to stabilize higher order polytypes (4H and 6H). Devices fabricated under RH 10% achieve higher efficiency and enhanced stability compared to those produced under inert atmosphere. These findings provide mechanistic insight into crystallization pathways in different environments and provide a framework to transfer processes from inert to ambient conditions. The results highlight the critical role of controlled humidity in tuning antisolvent-free perovskite crystallization for scalable processing.

Hossain, Maimur [Univ. of California, San Diego, C↗
DOE OSTI2026

MHD-induced SOL filaments and divertor heat flux striations in NSTX

Magnetohydrodynamic (MHD) activity in NSTX has been found to induce striated heat flux patterns at the divertor. These striated heat fluxes were previously interpreted as strike-point splitting caused by stochastic magnetic lobes near the X-point. Recent analysis indicates that MHD can directly generate filaments in the scrape-off layer (SOL), and the MHD-related filaments are consistent with the rotation and the frequency of the MHD. Magnetic field line tracing from the mid-plane of the MHD-related filaments to the divertor is consistent with the location of striated heat fluxes on the divertor. Additionally, gas puff imaging (GPI) diagnostic data shows the generation of regular blobs following each edge harmonic oscillation (EHO) toroidal mode number n = 1 perturbation, which indicates that the MHD perturbation directly drives the blobs. These blobs are responsible for the divertor heat flux striations. Here, this finding provides a new framework for understanding MHD-induced edge transport and has important implications for heat-load control in NSTX-U and future spherical tokamaks.

MHD↗
DOE OSTI2022

A reduced model for the ITER divertor based on SOLPS solutions for ITER Q = 10 baseline conditions: B. A reduced model based on reversed-direction two point modeling

Edge codes such as SOLPS coupled to neutral codes such as EIRENE have become so comprehensive and sophisticated that they now constitute, in effect, 'code-experiments' that, as for actual experiments, can benefit from interpretation using simple models and conceptual frameworks, i.e. reduced models. The first task is the identification of options for the reduced model control parameters that are best suited for control of the action of the divertor, i.e. for control of target power loading and sputter–erosion, primarily. A strong correlation between the electron temperature at the divertor target, T e,t , and the neutral deuterium D 2 density at the target, n D2,t , flux-tube resolved, has recently been reported for a number of code studies including SOLPS-4.3 modeling of a set of ~50 ITER baseline cases: Q DT = 10, q 95 = 3, P SOL = 100 MW, metallic walls, and Ne seeding (Pitts et al 2019). Part A of the present study reports new results for largely the same ITER cases, confirming the strong correlation reported earlier between local values of T e,t , and (i) n D2,t , and (ii) normalized volumetric losses of power and pressure in the divertor. Strong correlations have now also been found, and are reported here for the first time, between T e,t and all of the divertor target quantities of practical interest. A physical explanation for this surprising result has not as yet been fully identified; nevertheless it has encouraging implications for reduced modeling of the ITER divertor. For such ITER conditions, (i) the global Ne injection rate, Inj Ne (Ne s –1 ), and (ii) the electron temperature at the location on the target where the peak power deposition occurs, T e,t @q ⊥,pk (eV), are found to be promising reduced model control parameters. In this part B, a reduced model for the ITER divertor is developed and described in detail, based on reversed-direction two point modeling, Rev2PM. The input to the reduced model is a value of the variable pair (T e,t @q ⊥,pk , Inj Ne )for a chosen case and the output are values of the various target as well as divertor-entrance quantities of practical interest, e.g. q ⊥,pk , the electron density at the X-point, n e,Xpt , etc. The reduced model was quantitatively characterized using one half of the code cases; it was then used to successfully predict (replicate) the code values of e.g. n e,Xpt for the other half.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency

Neuromorphic computing offers one path forward for AI at the edge. However, accessing and effectively utilizing a neuromorphic hardware platform is non-trivial. In this work, we present a complete pipeline for neuromorphic computing at the edge, including a small, inexpensive, low-power, FPGA-based neuromorphic hardware platform, a training algorithm for designing spiking neural networks for neuromorphic hardware, and a software framework for connecting those components. We demonstrate this pipeline on a real-world application, engine control for a spark-ignition internal combustion engine. We illustrate how we connect engine simulations with neuromorphic hardware simulations and training software to produce hardware-compatible spiking neural networks that perform engine control to improve fuel efficiency. We present initial results on the performance of these spiking neural networks and illustrate that they outperform open-loop engine control. We also give size, weight, and power estimates for a deployed solution of this type.

Schuman, Catherine↗
DOE OSTI2020

Efficient, stable silicon tandem cells enabled by anion-engineered wide-bandgap perovskites

Maximizing the power conversion efficiency (PCE) of perovskite/silicon tandem solar cells that can exceed the Shockley-Queisser single-cell limit requires a high-performing, stable perovskite top cell with a wide bandgap. We developed a stable perovskite solar cell with a bandgap of ~1.7 electron volts that retained more than 80% of its initial PCE of 20.7% after 1000 hours of continuous illumination. Anion engineering of phenethylammonium-based two-dimensional (2D) additives was critical for controlling the structural and electrical properties of the 2D passivation layers based on a lead iodide framework. The high PCE of 26.7% of a monolithic two-terminal wide-bandgap perovskite/silicon tandem solar cell was made possible by the ideal combination of spectral responses of the top and bottom cells.

14 SOLAR ENERGY↗