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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structure and magnetism of AlCoCrCuFeNi high-entropy alloy

We report on the investigation of magnetic and structural properties of AlCoCrCuFeNi, which is known to crystallize in a dual phase solid solution: the face-centred cubic (FCC) or the body-centred cubic (BCC). The results of neutron (NPD) and synchrotron powder diffraction (SXRD) allow to partially resolve magnetic information coming from BCC and FCC phases, which is impossible in the bulk magnetic measurements. Electron diffraction (PED) revealed that AlCoCrCuFeNi forms dendritic microstructure with the Cu-rich FCC phase and the Ni-rich BCC phase. Lattice parameters obtained from PED method are in good agreement with parameters obtained after refinement on the basis of powder X-ray diffraction measurements. The local crystal and electronic structure around Co was studied using Co K X-ray Absorption Spectroscopy (XAS). The magnetic measurements show that AlCoCrCuFeNi reveal a ferromagnetic transition at about 330K and displays magnetic hysteresis loop at the room temperature. Results from NPD suggest that the magnetic moment is mostly located in the BCC subsystem. The alloy shows soft magnetic properties. Saturated magnetizations (M s ), remanence ratio (M r /M s ) and coercivity (H c ) of the cast are estimated to be 45.10emu/g, 5.1% and 56 Oe at 300K, respectively. Finally, the BCC-FCC phase transformation up to 673K (400°C) was investigated using temperature dependent NPD, where a possible second BCC phase was identified.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Power System Network Reduction for Power Hardware-in-the-Loop Simulation: Preprint

This paper proposes a single-port equivalent and a two-port equivalent to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port remain unchanged over the reduction, including voltage magnitude and phase, active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Defect and structural evolution under high-energy ion irradiation informs battery materials design for extreme environments

Understanding defect evolution and structural transformations constitutes a prominent research frontier for ultimately controlling the electrochemical properties of advanced battery materials. Herein, for the first time, we utilize in situ high-energy Kr ion irradiation with transmission electron microscopy to monitor how defects and microstructures evolve in Na- and Li-layered cathodes with 3d transition metals. Our experimental and theoretical analyses reveal that Li-layered cathodes are more resistant to radiation-induced structural transformations, such as amorphization than Na-layered cathodes. The underlying mechanism is the facile formation of Li-transition metal antisite defects in Li-layered cathodes. The quantitative mathematical analysis of the dynamic bright-field imaging shows that defect clusters preferentially align along the Na/Li ion diffusion channels ( a-b planes), which is likely governed by the formation of dislocation loops. Our study provides critical insights into designing battery materials for extreme irradiation environments and understanding fundamental defect dynamics in layered oxides.

25 ENERGY STORAGE↗
DOE OSTI2021

Power System Network Reduction for Power Hardware-in-the-Loop Simulation

This paper proposes single-port equivalent and two-port equivalent network reduction methods to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port(s) remain unchanged over the reduction, including voltage magnitude and phase and active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.

model reduction↗
DOE OSTI2022

A Novel Power-Hardware-in-the-Loop Interface Method for Grid-forming Inverter Systems: Preprint

Power Hardware-in-the-Loop (PHIL) simulation of grid-forming (GFM) inverter systems facilitates the testing of drastic scenarios like on-grid to off-grid transition, islanded microgrid operation without stiff grid etc. To the authors best knowledge, most of studies in literature are focused on PHIL simulation for grid-following inverter systems and only few studies are focused on GFM inverters and those are challenging and problematic especially for high-power applications. In this article, a novel PHIL simulation platform is proposed that enables interfacing of high-power GFM inverter systems. It proposes the concept of a virtual GFM inverter as a part of the proposed PHIL interface for GFM inverter. This addition of virtual GFM inverter in the PHIL interface expands the conventional Ideal Transformer Model (ITM) method and enables it to overcome the issues of instability of existing ITM methods. In the validation stage, a PHIL experiment is conducted on a 3-phase 480 V, 125 kVA GFM inverter system with proposed interfacing method. The results corroborates the fact that the proposed PHIL simulation method performs well and stable for GFM inverter system.

grid-forming inverters↗
DOE OSTI2026

Ion-Specific Precipitation of Extractants Enables Rare-Earth Separation and Wastewater Remediation from Solvent Extraction of Critical Elements

The increasing demand for rare-earth elements (REEs) necessitates sustainable recovery strategies, particularly from secondary sources, such as electronic waste. Solvent extraction is the primary industrial method for REE separation; however, the unintentional dissolution of extractants into wastewater poses serious environmental risks, leading to organic contamination and process inefficiencies. Existing wastewater treatment methods struggle to remove these persistent pollutants, underscoring the need for innovative recovery approaches. Herein, we present a ligand-mediated precipitation strategy that simultaneously recovers REEs and removes dissolved extractants from solvent extraction wastewater. We show that residual extractants in the aqueous phase can selectively bind REEs, inducing their precipitation while leaving transition metals in solution. By integrating FTIR spectroscopy, EDS, XPS, EXAFS, and SAXS, we elucidate the mechanism of ion-specific precipitation and the local coordination environment of metal ions in the precipitate. Importantly, we demonstrate that the precipitated extractants can be efficiently recovered and reused, providing a closed-loop solution that enhances sustainability. Applying this method to leachates from samarium–cobalt (Sm–Co) and neodymium–iron–boron (NdFeB) mixed magnets, we achieve highly selective REE precipitation under mild conditions, demonstrating a scalable and cost-effective pathway for REE recovery, wastewater purification, and extractant recycling. In conclusion, by integrating element-specific ligand-mediated precipitation with extractant reuse, this work offers a transformative approach to REE separation that reduces the environmental impact while improving resource efficiency.

E-waste↗
DOE OSTI2023

Chemical looping conversion of CH4/CO2 to syngas on 5wt.%Ni@Ce0.6Zr0.4O2 catalyst: Impact of dynamic accumulation of surface carbon and oxygen vacancies

Syngas, a combination of carbon monoxide (CO) and hydrogen (H2), is a precursor to many chemicals and fuels, contributing to the billion-dollar global hydrocarbon industry. Chemical looping reforming (CLR) of the greenhouse gases methane (CH4) and carbon dioxide (CO2) allows for energy-efficient production of syngas. 5wt.% nickel (Ni) on ceria-zirconia (5wt%Ni@Ce0.6Zr0.4O2) mixed metal oxide catalyst was investigated here to explore pathways for enhanced syngas production on sustainable earth-abundant transition metal supported catalysts. The role of reduction-oxidation (redox) state of the catalyst, and carbon formation on the catalyst surface during chemical looping is explored to drive superior reaction kinetics, conversions, selectivity, and syngas ratios (H2/CO). The bulk and surface structure of the catalyst, along with carbon deposition features were characterized by electron microscopy, X-Ray diffraction, and ex situ Raman spectroscopy. The dynamic evolution of catalysts under CLR reaction conditions and the intrinsic reaction mechanisms were probed with in situ Raman spectroscopy, in situ Fourier transform spectroscopy, dynamic oxygen storage capacity (DOSC), and the Temporal Analysis of Products (TAP) reactor studies. Intrinsic kinetics of syngas production via CLR was correlated to the redox state of catalyst and the participation of nickel-catalyzed multiwalled carbon nanotube (CNT) growth, allowing enhanced CLR reaction performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Network Reduction for Power Hardware-in-the-Loop (PHIL) Simulation

This paper proposes single-port equivalent and two-port equivalent network reduction methods to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port(s) remain unchanged over the reduction, including voltage magnitude and phase and active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Novel Power-Hardware-in-the-Loop Interface Method for Grid-Forming Inverter Systems

Power-hardware-in-the-loop (PHIL) simulations of grid-forming (GFM) inverter systems facilitate the testing of drastic scenarios, such as on-grid to off-grid transitions and islanded microgrid operations without a stiff grid. To the authors’ best knowledge, most studies in the literature focus on PHIL simulations for grid-following inverter systems. Only a few studies focus on GFM inverters, and those are challenging and problematic, especially for high-power applications. This article proposes a novel PHIL simulation platform that enables interfacing high-power GFM inverter systems. The paper proposes the concept of a virtual GFM inverter as a part of the proposed PHIL interface. This addition of a virtual GFM inverter in the PHIL interface expands the conventional ideal transformer model (ITM) method and enables it to overcome the issues of instability of existing ITM methods. In the validation stage, a PHIL experiment is conducted on a three-phase, 480-V, 125-kVA GFM inverter system with the proposed interfacing method. The results corroborate that the proposed PHIL simulation method performs well and is stable for GFM inverter systems.

droop control↗
DOE OSTI2021

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sodium tungstate-promoted CaMnO 3 as an effective, phase-transition redox catalyst for redox oxidative cracking of cyclohexane

Oxidative cracking, which combines catalytic oxidation and cracking reactions, represents a promising approach to reduce the energy and carbon intensities for light olefin production from naphtha. The need to co-feed gaseous oxygen with hydrocarbons, however, leads to significant CO x formation and safety concerns. The cost and energy consumption associated with air separation also affects its economic attractiveness. In this study, we investigated a redox oxidative cracking (ROC) scheme and evaluated perovskites (La 0.8 Sr 0.2 FeO 3 and CaMnO 3 ) and Na 2 WO 4 -promoted perovskite (La 0.8 Sr 0.2 FeO 3 @Na 2 WO 4 and CaMnO 3 @Na 2 WO 4 ) as the redox catalysts for ROC. CaMnO 3 @Na 2 WO 4 redox catalyst shows high activity, selectivity, and stability for light olefin production from cyclohexane. Operated under a redox oxidative cracking (ROC) scheme, CaMnO 3 @Na 2 WO 4 enhances the catalytic cracking of cyclohexane, while showing high selectivity towards hydrogen combustion with its built-in, active lattice oxygen. Over three-fold increase in olefin yield compared to thermal cracking and 35% yield increase compared to conventional O 2 -cofeed oxidative cracking were achieved. Low energy ion scattering (LEIS), X-ray photoelectric spectroscopy (XPS), and differential scanning calorimetry (DSC) indicated a core-shell structure, where a molten Na 2 WO 4 layer covers the CaMnO 3 core. Na 2 WO 4 modifies the oxygen donation behavior of CaMnO 3 and provides a catalytically active surface for cyclohexane activation. In-situ XRD revealed that CaMnO 3 @Na 2 WO 4 exhibited excellent structural stability and regenerability. The transformation of Mn 4+ ↔ Mn 3+ ↔ Mn 2+ in CaMnO 3 , facilitated by reversible phase transition to (Ca/Mn)O solid solution, is responsible for the lattice oxygen donation and uptake during redox cycles. Electrochemical impedance spectroscopy (EIS) measurements further confirmed that the oxygen species were transported through the molten Na 2 WO 4 layer to participate in ROC. These findings offer mechanistic insights to design effective redox catalysts for hydrocarbon valorization using the chemical looping strategy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Direct detection of dark baryons naturally suppressed by ℋ-parity

We identify symmetries in a broad class of vectorlike confining dark sectors that forbid the leading electromagnetic moments that would ordinarily mediate dark baryon scattering with the Standard Model. The absence of these operators implies dark baryon dark matter has much smaller cross sections for elastic scattering off nuclei, leading to suppressed direct detection signals. In the confined description, we identify an “ℋ-parity” symmetry that exists in any dark sector with dark quarks transforming under a vectorlike representation of a new confining SU(𝑁 𝑐 ) gauge theory as well as a vectorlike representation of the electroweak group SU⁡(2) 𝐿 . The parity is independent of 𝑁 𝑐 and 𝑁 𝑓 , though it is essential that the dark quarks are neutral under hypercharge. This parity forbids dark hadron electric and magnetic dipole moments, charge radius, and anapole moment, while permitting dimension-7 operators that include polarizability, electroweak loop-induced interactions, and lower-dimensional electromagnetic transition moments between different neutral dark baryon states. We work out an explicit example, 𝑁 𝑐 = 𝑁 𝑓 = 3, that is the most minimal theory with fermionic dark baryons. In this specific model, we use the nonrelativistic quark model to show the magnetic dipole moment and charge radius vanish, while the transition moments are nonzero, consistent with ℋ-parity. We discuss the implications of a suppressed direct detection signal, emphasizing that this broad class of models provide a well-motivated target for future colliders.

composite models↗
DOE OSTI2020

Chapter 21. Atomic-Level Dislocation Dynamics in Irradiated Metals

Primary damage and microstructure evolution in structural nuclear materials operating under conditions of a high flux of energetic atomic particles and high temperature and stress lead to the formation of a high concentration, non-homogeneous distribution of defect clusters in the form of dislocation loops, voids, gas-filled bubbles and radiation-induced precipitates of nanometer scale. They cause changes in many material properties. Being obstacles to dislocation glide, they strongly affect the mechanical properties, with an increase in yield and flow stresses and a reduction in ductility. Atomic-scale computer simulations can provide details of how these effects are influenced by the obstacle structure, applied stress, strain rate and temperature. Processes such as obstacle cutting, transformation, absorption and drag are observed. Some recent results for body-centered and face-centered cubic metals are described in this review and, where appropriate, comparisons are drawn with predictions based on the elasticity theory of crystal defects. Perspectives on how to use this information at higher scales and in particular in mesoscale, dislocation dynamics simulations are also discussed.

Osetskiy, Yury N.↗
DOE OSTI2026

Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications

Magnetic graphs, originally developed to model quantum systems under magnetic fields, have recently emerged as a powerful framework for analyzing complex directed networks. Existing research has primarily used the spectral properties of the magnetic graph matrix to study global and stationary network features. However, their capacity to model local, nonequilibrium behaviors, often described by matrix powers, remains largely unexplored. We present a combinatorial interpretation of the magnetic graph matrix powers through directed walk profiles—counts of graph walks indexed by the number of edge reversals. Crucially, we establish that walk profiles correspond to a Fourier transform of magnetic matrix powers. The connection allows exact reconstruction of walk profiles from magnetic matrix powers at multiple discrete potentials, and more importantly, an even smaller number of potentials often suffices for accurate approximate reconstruction in real networks. This shows the empirical compressibility of the information captured by the magnetic matrix. This fresh perspective suggests further applications; for example, we illustrate how powers of the magnetic matrix can identify frustrated directed cycles (e.g., feedforward loops) and can be effectively employed for link prediction by encoding local structural details in directed graphs.

complex networks↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2023

Mathematical nuances of Gaussian process-driven autonomous experimentation

Abstract The fields of machine learning (ML) and artificial intelligence (AI) have transformed almost every aspect of science and engineering. The excitement for AI/ML methods is in large part due to their perceived novelty, as compared to traditional methods of statistics, computation, and applied mathematics. But clearly, all methods in ML have their foundations in mathematical theories, such as function approximation, uncertainty quantification, and function optimization. Autonomous experimentation is no exception; it is often formulated as a chain of off-the-shelf tools, organized in a closed loop, without emphasis on the intricacies of each algorithm involved. The uncomfortable truth is that the success of any ML endeavor, and this includes autonomous experimentation, strongly depends on the sophistication of the underlying mathematical methods and software that have to allow for enough flexibility to consider functions that are in agreement with particular physical theories. We have observed that standard off-the-shelf tools, used by many in the applied ML community, often hide the underlying complexities and therefore perform poorly. In this paper, we want to give a perspective on the intricate connections between mathematics and ML, with a focus on Gaussian process-driven autonomous experimentation. Although the Gaussian process is a powerful mathematical concept, it has to be implemented and customized correctly for optimal performance. We present several simple toy problems to explore these nuances and highlight the importance of mathematical and statistical rigor in autonomous experimentation and ML. One key takeaway is that ML is not, as many had hoped, a set of agnostic plug-and-play solvers for everyday scientific problems, but instead needs expertise and mastery to be applied successfully. Graphical abstract

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Kelvin probe force microscopy under ambient conditions

Kelvin probe force microscopy (KPFM) is a technique derived from atomic force microscopy that provides maps of surface potential or work function differences across material systems, with nanometre-scale resolution. KPFM is a useful tool for investigating electrical phenomena such as dipole orientation, interfacial charge transfer, charge accumulation, band bending and doping levels. This Primer aims to provide an overview of typical ambient-condition KPFM measurements, covering their underlying principles, experimental implementations and wide-ranging applications. Key KPFM variants, including amplitude and frequency modulation, heterodyne detection schemes and innovative open loop and pulsed force techniques, are discussed, with practical guidance on optimizing signal acquisition and reducing errors. Specialized approaches, such as time-resolved KPFM and multimodal KPFM, are discussed for their ability to capture dynamic charge processes and chemical information, respectively. Here, we highlight recent advances in KPFM applications, spanning metal alloys, soft matter, ferroelectrics, photovoltaics and 2D materials, showcasing its versatility across research domains. By addressing current limitations and identifying future opportunities, this Primer underscores the transformative potential of KPFM in advancing the understanding of nanoscale electrical phenomena.

Zahmatkeshsaredorahi, Amirhossein [Lehigh Univ., B↗
DOE OSTI2025

Plastic from CO 2 , Water, and Electricity: Tandem Electrochemical CO 2 Reduction and Thermochemical Ethylene-CO Copolymerization

Converting CO 2 into industrially useful products is an appealing strategy for utilization of an abundant chemical resource. Electrochemical CO 2 reduction (eCO 2 R) offers a pathway to convert CO 2 into CO and ethylene, using renewable electricity. Here, these products can be efficiently copolymerized by organometallic catalysts to generate polyketones. However, the conditions for these reactions are very different, presenting the challenge of coupling microenvironments typically encountered for the transformation of CO 2 into highly complex but desirable multicarbon products. Herein, we present a system to produce polyketone plastics entirely derived from CO 2 and water, where both the CO and C 2 H 4 intermediates are produced by eCO 2 R. In this system, a combination of Cu and Ag gas diffusion electrodes is used to generate a gas mixture with nearly equal concentrations of CO and C 2 H 4 , and a recirculatory CO 2 reduction loop is used to reach concentrations of above 11% each, leading to a current-to-polymer efficiency of up to 51% and CO 2 utilization of 14%.

carbon storage↗