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2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

Recent proliferation of distributed energy sources in distribution or sub-transmission systems necessitates close monitoring of these three-phase power grids which typically operate under unbalanced loading conditions. Unlike the transmission systems where the network equations are commonly based on the positive sequence component models, a detailed three phase model will have to be used in implementing network applications for these systems. In the specific case of the state estimator, where measurement and parameter errors may bias the solution, bad data and parameter error detection algorithms should also be incorporated. Implementing the state estimator and error detection algorithms for three-phase systems impose additional computational burden and modifications to the state estimation code. This paper proposes a practical solution to avoid these issues by using synchronized phasor measurements and modal decoupling. The previously developed parameter error detection algorithm based on the normalized Lagrange multipliers (NLM) test is applied to the measurements independently in each mode in parallel, not only saving CPU time but also avoiding new code development for a three-phase estimator. Different parameter error scenarios are created and tested to verify the effectiveness of the proposed error detection approach.

Khalili, Ramtin↗

PMU-Based Decoupled State Estimation for Unsymmetrical Power Systems

Modal decomposition of measurement equations has already been shown to simplify the formulation and resulting computational complexity of three-phase state estimation of systems where all the transmission lines are three-phase and fully transposed. When there are non-transposed and/or mixed-phase lines, modal decomposition can no longer fully decouple the threephase measurement equations. Here, this paper addresses the above shortcoming by proposing a simple yet practical solution based on the commonly used numerical compensation techniques. Thus, it enables application of the powerful decoupling approach to any type of three-phase networks which may contain non-transposed or mixed-phase lines and are fully observable by PMUs. The proposed procedure modifies the measurement set by deriving additive terms that compensate for the neglected unsymmetrical effects. It will be shown that unbalanced systems including nontransposed and mixed-phase elements, can still be transformed into three decoupled subsystems and solved in parallel by the proposed approach. Performance of the proposed algorithm is validated against several IEEE test cases.

42 ENGINEERING↗

Theory-guided design of duplex-phase multi-principal-element alloys

Density-functional theory (DFT) is used to identify phase-equilibria in multi-principal-element and high-entropy alloys (MPEAs/HEAs), including duplex-phase and eutectic microstructures. Here, a combination of composition-dependent formation energy and electronic-structure-based ordering parameters were used to identify a transition from FCC to BCC favoring mixtures, and these predictions experimentally validated in the Al-Co-Cr-Cu-Fe-Ni system. A sharp crossover in lattice structure and dual-phase stability as a function of composition were predicted via DFT and validated experimentally. The impact of solidification kinetics and thermodynamic stability was explored experimentally using a range of techniques, from slow (castings) to rapid (laser remelting), which showed a decoupling of phase fraction from thermal history, i.e., phase fraction was found to be solidification rate-independent, enabling tuning of a multi-modal cell and grain size ranging from nanoscale through macroscale. Strength and ductility tradeoffs for select processing parameters were investigated via uniaxial tension and small-punch testing on specimens manufactured via powder-based additive manufacturing (directed-energy deposition). This work establishes a pathway for design and optimization of next-generation multiphase superalloys via tailoring of structural and chemical ordering in concentrated solid solutions.

36 MATERIALS SCIENCE↗

A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning

Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a major challenge. We present MODES (Multi-mOdal Disentangled Embedding Space), a representation fusion framework that explicitly separates shared and modality-specific factors of variation, offering a structured latent space for multimodal information that improves both prediction and interpretability. By leveraging pre-trained unimodal foundation models, MODES mitigates the dependency on extensive paired datasets, crucial in data-scarce clinical settings. We introduce a masking strategy that optimizes representation dimensionality by eliminating low-information dimensions, to achieve compact, information-rich representations. Our framework demonstrates superior performance in predicting diagnoses and phenotypes compared to unimodal and conventional fusion models. MODES also enables robust diagnostic inference in missing data scenarios, offering an opportunity toward interpretable and efficient multimodal diagnostics in personalized healthcare.

60 APPLIED LIFE SCIENCES↗

Bi-modal particle size distribution for high energy product hybrid Nd–Fe–B—Sm–Fe–N bonded magnets

In this work, we have demonstrated high energy product bonded magnet by leveraging the variation in sizes between Nd-Fe-B and Sm-Fe-N, as well as their hard magnetic properties. The hybrid anisotropic bonded magnets contain 70 vol% of magnet powder (Dy-free Nd-Fe-B and Sm-Fe-N) and 30 vol% of nylon. The objective of the work was to create bi-modal and bi-compositional bonded magnets in which the fine (3μm) particles of Sm-Fe-N would be used to fill the voids between the bigger Nd-Fe-B (105μm) particles, thus improve packing density. The magnetic hysteresis loop did not show significant signs of decoupled interactions between the magnetic phases. It was also found that the performance of the bonded magnet was most enhanced at 1:4 ratio of Nd-Fe-B and Sm-Fe-N. At that ratio, maximum density of 5 g/cm 3 and the highest (BH) max value of 18.5 MGOe were obtained, although the intrinsic coercivity decreased, relative to the trend seen for other ratios. This work advances the opportunity to expand the use of Sm-Fe-N in bonded magnet applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A fluorescent-protein spin qubit

Quantum bits (qubits) are two-level quantum systems that support initialization, readout and coherent control1. Optically addressable spin qubits form the foundation of an emerging generation of nanoscale sensors. The engineering of these qubits has mainly focused on solid-state systems. However, fluorescent proteins, rather than exogenous fluorescent probes, have become the gold standard for in vivo microscopy because of their genetic encodability. Although fluorescent proteins possess a metastable triplet state, they have not been investigated as qubits. Here we realize an optically addressable spin qubit in enhanced yellow fluorescent protein. A near-infrared laser pulse enables triggered readout of the triplet state with up to 20% spin contrast. Using coherent microwave control of the enhanced-yellow-fluorescent-protein spin at liquid-nitrogen temperatures, we measure a (16 ± 2) μs coherence time under Carr–Purcell–Meiboom–Gill decoupling. We express the qubit in mammalian cells, maintaining contrast and coherent control despite the complex intracellular environment. Finally, we demonstrate optically detected magnetic resonance in bacterial cells at room temperature with contrast up to 8%. Our results introduce fluorescent proteins as a powerful qubit platform that paves the way for applications in the life sciences, such as nanoscale field sensing and spin-based imaging modalities.

Feder, Jacob S. [Univ. of Chicago, IL (United Stat↗

Evaluating Contributions of Pitch-Carbon Coating to Improved Stability of Si Anodes Through Voltage-Resolved Multi-Phase Characterization

Silicon nanoparticles have emerged as a promising alternative to graphite to improve the energy density of next-generation lithium-ion battery anodes. Nano-sized Si domains facilitate rapid ion transport and minimize particle-scale mechanical degradation, but also exhibit increased (electro)chemical reactivity with Li-ion electrolyte components due to their high surface area. We have previously demonstrated that surface modification of Si nanoparticles with pitch-carbon is an effective strategy to reduce these parasitic reactions. In the present work, we holistically evaluate the mechanistic contribution of pitch-carbon coating to the observed stability improvement over uncoated Si. We utilize coupled in situ and ex situ methods to probe changes to solid-surface, volatile headspace, and gas-phase chemistry occurring during initial cycling. Measurements taken at targeted potentials associated with electrolyte species reduction enables the decoupling of specific reaction pathways tied to interfacial stability. Further, we demonstrate the non-trivial role of gas reconsumption in dictating the nature of the passivating surface layer evolved on both uncoated and pitch-coated Si. This multi-phase analysis offers insights into the mechanism of effective surface passivation, which may be applied to inform future Si material development.

ENERGY STORAGE↗

Multiscale operando X-ray investigations provide insights into electro-chemo-mechanical behavior of lithium intercalation cathodes

The electrochemical performance and cycle life of lithium-ion batteries (LIBs) depend on the electrochemical, chemical, and mechanical behavior of electrodes and electrolytes. Despite extensive studies conducted previously, challenges exist to decouple these behaviors, capture the evolution of electro-chemo-mechanical behavior in realistic conditions, and correlate atomic-scale stress evolution to micro-scale bulk mechanical degradation. Here, we report multiscale operando techniques to investigate polydisperse battery electrodes by integrating volume-averaged quantitative synchrotron X-ray scattering with high-resolution transmission X-ray microscopy (TXM). The former provides us information spanning a wide spatial range, from Angstrom-level atomic structures to micrometer-level particle scales, while the latter provides time-resolved 2D images of the particles during cycling. The complementarity of the two operando techniques is demonstrated by an over-lithiation test of LiCoO 2 electrodes, where particles crack and eventually pulverize. Additionally, the techniques are applied to study LiCoO 2 cycling stability from 3.0 V to 4.5 V. Operando X-ray scattering result shows nanometer-scale features keep forming in LiCoO 2 electrodes during cycling, resulting in an increased projected area observed by the TXM experiment. The formation of such features is inhibited by a polymer coating on the electrode, leading to vastly improved cycling stability. The polymer coating alleviates LiCoO 2 surface deterioration, reduces side product generation, and inhibits LiCoO 2 particles volume expansion during the cycling test. These operando multimodal X-ray techniques presented herein thus offer a novel, multiscale diagnostic modality for studying existing and emerging battery materials, aiding the development of next-generation LIBs.

25 ENERGY STORAGE↗