Adaptive PPO With Multi-Armed Bandit Clipping and Meta-Control for Robust Power Grid Operation Under Adversarial Attacks
Not Available
Massaoudi, Mohamed [Department of Electrical and C↗
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Temperature Evolution of the Activation Barriers Leads to Meyer−Neldel Rules for Structural Relaxation and Transport in Polymers
Understanding activation barriers controlling structural relaxation in glass-forming liquids, molecular transport, and ionic conductivity in amorphous polymers is a grand challenge of fundamental scientific and materials engineering interest across disciplines. Over decades, intriguing but puzzling empirical correlations between the elementary time scale of activated barrier crossing and the apparent Arrhenius activation energy, the so-called Meyer−Neldel (MN) rules, have been discovered in diverse liquids and glasses. Here, in this study, we formulate and successfully apply a new experimental analysis and an explicitly dynamical theoretical framework which provides an understanding of the origin, validity, and failure of such correlations, that bridge and unify the three fields of structural relaxation, molecular transport, and ionic conductivity in liquids and quenched glasses. Distinct quasi-universal laws are predicted in equilibrated liquids and nonequilibrium glasses, consistent with experiments. Our analysis reveals that even if the relaxation appears Arrhenius over a limited temperature range, the physical activation barrier is generally temperature-dependent in polymeric systems even below glass transition temperature. In addition, we show that the approximate validity of classical MN rules hinges on a linear temperature dependence of this barrier and the temperature range probed in experiments. Our findings are relevant for controlling the activation barrier in functional soft polymeric materials relevant to molecular separations, barrier coatings, and charge transport, and also provide new constraints on the theoretical understanding of the mechanism underlying slow activated dynamics in glass-forming condensed matter.
Meyer-Neldel rules↗
Impact of Salt Chemistry and Interlayer Architecture on Solid Electrolyte Interphase Composition and Performance
Reversible lithium metal anodes require interphases that are chemically stable, mechanically robust, and compatible with scalable processing. Here, we use slot-die-coated, salt-rich PEO interlayers to design SEI nanolayers on Cu current collectors and isolate how salt anion chemistry controls interphase composition and lithium plating/stripping behavior in architectures relevant to Li plating and anode-free cells. Mesoscale modeling shows that dendrite initiation at SEI defects is governed by a tradeoff among interphase thickness, ionic conductivity, and stiffness through defect-driven current focusing, motivating controlled formation from thin precursor layers. Using UHV-transferred XPS and ToF-SIMS depth profiling after controlled Li deposition, we find that fluorinated sulfonylimide salts (LiTFSI, LiFSI) form mixed inorganic/organic interphases containing LiF and PEO-derived alkoxides, with LiFSI producing a more LiF-dominant Li-facing surface than LiTFSI under identical conditions. In contrast, LiNO 3 -containing interlayers yield a comparatively thick nitrate-derived interphase that reduces upon Li contact to an N-rich inorganic layer (Li–N/N 3− , LiN x O y ). In Cu| |Li cells, these designed interlayers reduce interfacial resistance and improve Coulombic efficiency (CE) and critical current density (CCD) relative to uncoated controls, with LiFSI- and LiNO 3 -based interlayers providing the highest CE/CCD. Overall, the results demonstrate a manufacturing-relevant approach to engineer SEI nanolayers by salt chemistry, providing pathways to improve reversible lithium metal anodes while reducing reliance on LiTFSI.
anion chemistry↗
Porous Iron Electrodes Reduce Energy Consumption During Electrocoagulation of a Virus Surrogate: Insights into Performance Enhancements Using Three-Dimensional Neutron Computed Tomography
Electrocoagulation has attracted significant attention as an alternative to conventional chemical coagulation because it is capable of removing a wide range of contaminants and has several potential advantages. In contrast to most electrocoagulation research that has been performed with nonporous electrodes, in this study, we demonstrate energy-efficient iron electrocoagulation using porous electrodes. In batch operation, investigation of the external pore structures through optical microscopy suggested that a low porosity electrode with sparse connection between pores may lead to mechanical failure of the pore network during electrolysis, whereas a high porosity electrode is vulnerable to pore clogging. Electrodes with intermediate porosity, instead, only suffered a moderate surface deposition, leading to electrical energy savings of 21% and 36% in terms of electrocoagulant delivery and unit log virus reduction, respectively. Neutron computed tomography revealed the critical role of electrode porosity in utilizing the electrode’s internal surface for electrodissolution and effective delivery of electrocoagulant to the bulk. Energy savings of up to 88% in short-term operation were obtained with porous electrodes in a continuous flow-through system. Further investigation on the impact of current density and porosity in long-term operation is desired as well as the capital cost of porous electrodes.
42 ENGINEERING↗
Substrate-Directed Underlayer Growth of Bilayer MoS 2 Revealed by Mo Isotope Labeling
Direct control over the vertical formation sequence and stacking registry in van der Waals (vdW) bilayers is essential for device performance and moiré engineering yet difficult to resolve unambiguously with conventional probes. Here, we use Mo isotope labeling in a two-step chemical vapor deposition process to synthesize bilayer MoS 2 and trace its vertical formation on common substrates. By combining site-selective laser thinning, Raman spectroscopy, time-of-flight secondary ion mass spectrometry, and atomic-resolution scanning transimission electron microscopy (STEM), we find a clear substrate dependence: on SiO 2 /Si, the second layer nucleates and grows beneath the first (underlayer), whereas on sapphire, it forms on top (overlayer). Density functional theory indicates that a larger equilibrium interfacial separation and weaker MoS 2 –substrate interactions on amorphous SiO 2 permit confined interfacial diffusion and underlayer nucleation, whereas stronger interactions and smaller separations on sapphire favor overlayer growth. On SiO 2 , confined epitaxy templates commensurate 2H, 3R, and mixed bilayers, as confirmed by second harmonic generation spectroscopy and STEM. During underlayer coalescence, embedded mirror-twin grain boundaries stitch atomically sharp 2H|3R junctions via alternating 4|8 ring motifs. Molecular-dynamics simulations reveal that these alternating 4|8 motifs accommodate interlayer vdW coupling and locally modulate the stacking registry. These results provide mechanistic insight into confined epitaxial growth and establish isotope labeling as a powerful probe of two-dimensional materials synthesis.
MoS2↗
Design of a remote-controlled mount for IOTA’s magnet undulator
The SLAC undulator is essential for studies of synchrotron radiation produced by a single electron in IOTA. Since IOTA is meant for different experiments, the undulator is not always needed, so it must be pushed and pulled back depending upon the nature of the experiment. Moving the undulator manually result in significant time loss about 30 minutes for the controlled access, and about 30 minutes to achieve beam injector in IOTA. Because the operation is done manually is not easy to ensure a good repeatability of the undulator position. That’s why IOTA’s team decided to set-up a completely remote-controlled mount.
42 ENGINEERING↗
Dynamic Nonlinear Droop Control (DNDC): A Novel Primary Control Method for DC Microgrids
Not provided.
Engineering↗
Review and Gap Analysis of Heliostat Components and Controls
This investigation provides a comprehensive literature review pertaining to heliostat components and controls as part of the U.S. Department of Energy (DOE), Heliostat Consortium (Heliocon) program. Here, this work presents a detailed assessment of subcomponents, controls and wireless communications elements that comprise various designs of helisotats within concentrating solar power (CSP) installations. Additionally, this work also provides the results of an industry survey, intended to compliment the literature discussion, to provide a gap analysis of the primary technology and cost areas that need to be addressed to help improved to spur concentrating solar power (CSP) bankability. Although the results of the study determined several key areas for development, three strategic areas identified were: (1) the use of advanced composite materials to replace a need for expensive steel within the structure and mirror substrate, (2) employment of closed-loop controls for automated calibration, reduction of commissioning time and O&M hours, reduction of drive requirements, as well as overall cost reduction, and (3) the need for more Heliostat-centric codes and standards to facilitate engineering confidence in the development of new features, cost reductions, or other design iterations to be seamlessly introduced without optical performance problems.
14 SOLAR ENERGY↗
Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education
Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.
99 GENERAL AND MISCELLANEOUS↗
Patchy nanoparticles by atomic stencilling
Stencilling, in which patterns are created by painting over masks, has ubiquitous applications in art, architecture and manufacturing. Modern, top-down microfabrication methods have succeeded in reducing mask sizes to under 10 nm, enabling ever smaller microdevices as today’s fastest computer chips. Meanwhile, bottom-up masking using chemical bonds or physical interactions has remained largely unexplored, despite its advantages of low cost, solution-processability, scalability and high compatibility with complex, curved and three-dimensional (3D) surfaces. Here we report atomic stencilling to make patchy nanoparticles (NPs), using surface-adsorbed iodide submonolayers to create the mask and ligand-mediated grafted polymers onto unmasked regions as ‘paint’. We use this approach to synthesize more than 20 different types of NP coated with polymer patches in high yield. Polymer scaling theory and molecular dynamics (MD) simulation show that stencilling, along with the interplay of enthalpic and entropic effects of polymers, generates patchy particle morphologies not reported previously. These polymer-patched NPs self-assemble into extended crystals owing to highly uniform patches, including different non-closely packed superlattices. We propose that atomic stencilling opens new avenues in patterning NPs and other substrates at the nanometre length scale, leading to precise control of their chemistry, reactivity and interactions for a wide range of applications, such as targeted delivery, catalysis, microelectronics, integrated metamaterials and tissue engineering.
36 MATERIALS SCIENCE↗
Synthesis and growth of solution-processed chiral perovskites
In materials science, chiral perovskites stand out due to their exceptional optoelectronic properties and the versatility in their structure and composition, positioning them as crucial in the advances of technologies in spintronics and chiroptical systems. This review underlines the critical role of synthesizing and growing these materials, a process integral to leveraging their complex interplay between structural chirality and distinctive optoelectronic properties, including chiral-induced spin selectivity and chiroptical activity. The paper offers a comprehensive summary and discussion of the methods used in the synthesis and growth of chiral perovskites, delving into extensive growth techniques, fundamental mechanisms, and strategic approaches for the engineering of low-dimensional perovskites, alongside the creation of novel chiral ligands. The necessity of developing new synthetic approaches and maintaining precise control during the growth of chiral perovskites is emphasized, aiming to enhance their structural chirality and boost their efficiency in spin and chiroptical selectivity.
36 MATERIALS SCIENCE↗
Effects of chemical disorder and spin-orbit coupling on the electronic-structure and Fermi-surface topology of YbSb-based monopnictides
Here, in this work, we study the influence of disorder on the electronic structure of YbSb—a rare-earth monopnictide featuring a simple rocksalt (B1) crystal structure and a well-defined Fermi-surface topology—by employing first-principles density-functional theory. We focus on chemical disorder introduced through Te and Al doping, selected based on their thermodynamic stability in alloyed configurations, to understand how such perturbations modify the electronic states of YbSb. Our results indicate that Te doping predominantly introduces electronlike states at the 𝑋 and 𝐿 points, while Al doping leads to a suppression of holelike states at Γ, effectively driving the system from a semimetallic state to one characterized by very narrow-gap behavior at Γ. This modulation of the Fermi surface, particularly the reduction of central hole pockets at Γ, plays a central role in altering interpocket scattering—a mechanism critical for tuning quantum transport properties, including superconductivity. This disorder-driven modulation of the Fermi surface, particularly the suppression of central hole pockets at Γ, controls interpocket scattering, which is essential for optimizing quantum transport properties, including superconductivity. Our results show that disorder can be effectively used for engineering band topology, thereby tuning quantum related response through a tailored electronic structure.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Multi-agent voltage control in distribution systems using GAN-DRL-based approach
Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.
14 SOLAR ENERGY↗
A combined designed CSP and Pfs48/45 infection and transmission blocking vaccine for malaria
Abstract The multiple stages of the malaria parasite life cycle hampers vaccine development. Combining a pre-erythrocytic antigen with a transmission-blocking antigen would target two independent stages of the life cycle for disease control, resulting in a multistage vaccine that can prevent infection and disease transmission simultaneously. Here, we generated a self-assembled ferritin nanoparticle vaccine that simultaneously presents designed immunogens CSPj5c and 17-4 from the infection-blocking circumsporozoite and the transmission-blocking Pfs48/45 antigens. These immunogens were designed, through structure-based approaches, to retain protective epitopes and confer protection upon vaccination. Immunization with CSPj5c-17-4-ferritin nanoparticles conferred protection against challenge with transgenic sporozoites expressingPlasmodium falciparumCSP in mice, and purified IgGs from immunized rabbits elicited potent transmission-reducing activity. Addition of the engineered 17-4 improved the immune responses to CSPj5c and protection from sporozoite challenge. CSPj5c-17-4-ferritin is therefore a promising multistage malaria vaccine with a potential role in malaria control.
Immunology↗
Phylogenomic discovery and engineering of nitrogen fixation into the bioenergy woody crop poplar
Biological nitrogen fixation (BNF) is a key process enabling plants in specific lineages to convert atmospheric dinitrogen (N₂) into bioavailable ammonia through symbioses with diazotrophic microbes. Expanding this capability beyond native nitrogen-fixing clades into non-nodulating crops would reduce synthetic fertilizer use, lowering energy inputs and environmental impacts in agriculture. Supported by DOE Funding Award DE-SC0018247, the NitFix project advanced foundational knowledge required to engineer root-nodule symbioses in new host species. The team generated the most comprehensive phylogenomic analysis to date of all known nodulating lineages, resolving the evolutionary history of nitrogen-fixing symbiosis and identifying core gene suites retained across nodulating taxa. Through multimodal genomics, transcriptomics, and functional analyses in Medicago truncatula and related species, the project mapped regulatory networks underlying nodule organogenesis, bacterial infection, and nitrogen-fixation efficiency. Key discoveries include the identification of conserved signaling modules for rhizobial recognition, transcription factors controlling nodule differentiation, and metabolic pathways integrating fixed nitrogen into plant growth. The project also developed enabling tools—including optimized transformation pipelines, gene-editing workflows, and imaging-based phenotyping—to accelerate engineering efforts in emerging models. Together, these results refine the mechanistic framework of symbiotic nitrogen fixation and highlight transferable components essential for rewiring these traits into non-nodulating crops.
59 BASIC BIOLOGICAL SCIENCES↗
Optimizing qubit control pulses for state preparation
In the burgeoning field of quantum computing, the precise design and optimization of quantum pulses are essential for enhancing qubit operation fidelity. This study focuses on refining the pulse engineering techniques for superconducting qubits, employing a detailed analysis of square and Gaussian pulse envelopes under various approximation schemes. We evaluated the effects of coherent errors induced by naive pulse designs. Furthermore, we identified the sources of these errors in the Hamiltonian model’s approximation level. We mitigated these errors through adjustments to the external driving frequency and pulse durations, thus implementing a pulse scheme with stroboscopic error reduction. Our results demonstrate that these refined pulse strategies improve performance and reduce coherent errors. Moreover, the techniques developed herein are applicable across different quantum architectures, such as ion-trap, atomic, and photonic systems.
Chirp modulation↗
Predictive analytics of selections of russet potatoes
We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.
60 APPLIED LIFE SCIENCES↗
Lyapunov-Based Iterative Learning of Regions of Attraction for Autonomous Systems
This paper proposes a novel algorithm for estimating the region of attraction of equilibrium points for nonlinear discrete-time autonomous systems. The method iteratively expands an initial estimate of the region of attraction by constructing unions of sublevel sets of learned functions parametrized as neural networks. Unlike conventional techniques that rely on a single global Lyapunov function, the proposed approach provides a collection of local Lyapunov-like functions, enabling richer representations and potentially larger region of attraction estimates. These functions are trained using sampled state-space data, and their Lipschitz continuity ensures that desirable properties extend beyond the training samples. The devised strategy is tested via numerical simulations, demonstrating the effectiveness of the proposed approach.
97 MATHEMATICS AND COMPUTING↗