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At least 217 records · Page 12

Hypergames and Cyber-Physical Security for Control Systems

The identification of the Stuxnet worm in 2010 provided a highly publicized example of a cyber attack that physically damaged an industrial control system. This raised public awareness about the possibility of similar attacks against other industrial targets—including critical infrastructure. Here, we use hypergames to analyze how strategic perturbations of sensor readings and calibrated parameters can be used to manipulate a system that employs optimal control. Hypergames form an extension of game theory that enables us to model strategic interactions where the players may have significantly different perceptions of the game(s) they are playing. Past work with hypergames has focused on relatively simple interactions consisting of a small set of discrete choices for each player. Here, we apply single-stage hypergames to larger systems with continuous variables. We find that manipulating constraints can be a more effective attacker strategy than manipulating objective function parameters. Moreover, the attacker need not change the underlying system to carry out a successful attack—it may be sufficient to deceive the defender controlling the system. It is possible to scale our approach up to even larger systems, but this will depend on the characteristics of the system in question, and we identify several characteristics that will make those systems amenable to hypergame analysis.

97 MATHEMATICS AND COMPUTING↗

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗

Proteomic Determinants of Variation in Cholesterol Efflux: Observations from the Dallas Heart Study

High-density lipoproteins (HDLs) are promising targets for predicting and treating atherosclerotic cardiovascular disease (ASCVD), as they mediate removal of excess cholesterol from lipid-laden macrophages that accumulate in the vasculature. This functional property of HDLs, termed cholesterol efflux capacity (CEC), is inversely associated with ASCVD. HDLs are compositionally diverse, associating with >250 different proteins, but their relative contribution to CEC remains poorly understood. Our goal was to identify and define key HDL-associated proteins that modulate CEC in humans. The proteomic signature of plasma HDL was quantified in 36 individuals in the multi-ethnic population-based Dallas Heart Study (DHS) cohort that exhibited persistent extremely high (>=90th%) or extremely low CEC (<=10th%) over 15 years. Levels of apolipoprotein (Apo)A-I associated ApoC-II, ApoC-III, and ApoA-IV were differentially correlated with CEC in high (r = 0.49, 0.41, and —0.21 respectively) and low (r = —0.46, —0.41, and 0.66 respectively) CEC groups (p for heterogeneity (pHet) = 0.03, 0.04, and 0.003 respectively). Further, we observed that levels of ApoA-I with ApoC-III, complement C3 (CO3), ApoE, and plasminogen (PLMG) were inversely associated with CEC in individuals within the low CEC group (r = —0.11 to —0.25 for subspecies with these proteins vs. r = 0.58 to 0.65 for subspecies lacking these proteins; p < 0.05 for heterogeneity). These findings suggest that enrichment of specific proteins on HDLs and, thus, different subspecies of HDLs, differentially modulate the removal of cholesterol from the vasculature.

60 APPLIED LIFE SCIENCES↗

Rethinking the soil core microbiome

The concept of a core microbiome emerged from host-associated research to describe microbial members or functions conserved across clearly defined spatial, temporal, and biological boundaries. In soil- and plant-associated microbiome research, however, the term has increasingly shifted toward analytically defined subsets selected using study-specific thresholds or criteria. Synthesizing recent literature and cross-site analyses of bioenergy crop field soils, we show that the original biological meaning of the core microbiome has been blurred by dataset-specific analytical criteria. Taxa designated as ‘core’ were highly sensitive to methodological choices and often reflected explanatory value rather than conserved biological membership. Moreover, many studies that identify taxonomic ‘core’ members interpret their significance in functional terms, suggesting that functional conservation may be the biological interest. Taxonomic conservation may not be the most biologically meaningful target in highly heterogeneous soil and rhizosphere systems, where functional conservation may persist despite taxonomic turnover. Accordingly, ‘core microbiome’ should be reserved for microbial components explicitly demonstrated to be conserved across defined spatial, temporal, and environmental dimensions and linked to conserved ecological functions, while taxa selected for explanatory value are better described as ‘explanatory subsets of taxa’. Greater terminological precision will improve cross-study comparability and strengthen ecological inference in plant–soil microbiome research.

bioenergy crops↗

Use Attainability Analysis Aquatic Life Uses for Perennial Reach of Sandia Canyon [Slides]

The following information will be presented during the February 23, 2022 Accord Pueblos Technical Meeting. The purpose of the study is to determine if natural thermal conditions are preventing the attainment of Coldwater Aquatic Life Use in the perennial reach of the upper Sandia Canyon Assessment Unit (Sandia AU). The New Mexico Water Quality Standards allow for a change in the designated use if a Use Attainability Analysis (UAA) demonstrates that the use is not attainable due to one or more of six factors listed in 40 CFR 131.10(g), including naturally occurring pollutant concentrations. The Sandia AU is located in a perennial reach of upper Sandia Canyon between Sigma Canyon and NPDES Outfall 001. The classified Segment 20.6.4.126 NMAC comprises perennial waters within Los Alamos National Laboratory boundaries and includes the Sandia AU. Persistent surface flows originate from NPDES permitted effluent releases. These releases have occurred since the early 1950’s and continue today. The UAA examines several lines of evidence. NMED’s Air-Water Temperature Correlation (AWTC) model is used for identifying appropriate stream classifications and attainable aquatic life use subcategories. The model correlates between July average air temperatures (ATEMP) and maximum weekly average stream temperatures. Air temperatures are obtained from PRISM and LANL Meteorological Towers and used to derive ATEMP. Thermographs were placed in the Sandia AU to obtain measured stream temperatures during summer months when stream temperatures are the highest. Measured data is used with AWTC modeled data to determine if the Sandia AU is meeting its natural air temperature driven thermal condition.

54 ENVIRONMENTAL SCIENCES↗

Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade

Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for (i) standardized validation protocols, (ii) curated benchmark datasets with complete metadata, and (iii) open repositories for glasses. A systematic was forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with machine learning based approaches, for instance, by integrating swap Monte Carlo with machine-learning (ML) potentials, leveraging foundation models through transfer learning, and finetuning ML potentials with experimental data. Progress depends on the community committing to validated models, reproducible protocols, and sustained data sharing.

Krishnan, N. M. Anoop↗

Genotypic and Phenotypic Characterization of Incompatibility Group FIB Positive Salmonella enterica Serovar Typhimurium Isolates from Food Animal Sources

Salmonella enterica is one of the most common bacterial foodborne pathogens in the United States, causing illnesses that range from self-limiting gastroenteritis to more severe, life threatening invasive disease. Many Salmonella strains contain plasmids that carry virulence, antimicrobial resistance, and/or transfer genes which allow them to adapt to diverse environments, and these can include incompatibility group (Inc) FIB plasmids. This study was undertaken to evaluate the genomic and phenotypic characteristics of IncFIB-positive Salmonella enterica serovar Typhimurium isolates from food animal sources, to identify their plasmid content, assess antimicrobial resistance and virulence properties, and compare their genotypic isolates with more recently isolated S. Typhimurium isolates from food animal sources. Methods: We identified 71 S. Typhimurium isolates that carried IncFIB plasmids. These isolates were subjected to whole genome sequencing and evaluated for bacteriocin production, antimicrobial susceptibility, the ability to transfer resistance plasmids, and a subset was evaluated for their ability to invade and persist in intestinal human epithelial cells. Results: Approximately 30% of isolates (n = 21) displayed bacteriocin inhibition of Escherichia coli strain J53. Bioinformatic analyses using PlasmidFinder software confirmed that all isolates contained IncFIB plasmids along with multiple other plasmid replicon types. Comparative analyses showed that all strains carried multiple antimicrobial resistance genes and virulence factors including iron acquisition genes, such as iucABCD (75%), iutA (94%), sitABCD (76%) and sitAB (100%). In 17 cases (71%), IncFIB plasmids, along with other plasmid replicon types, were able to conjugally transfer antimicrobial resistance and virulence genes to the susceptible recipient strain. For ten strains, persistence cell counts (27%) were noted to be significantly higher than invasion bacterial cell counts. When the genome sequences of the study isolates collected from 1998–2003 were compared to those published from subsequent years (2005–2018), overlapping genotypes were found, indicating the perseverance of IncFIB positive strains in food animal populations. This study confirms that IncFIB plasmids can play a potential role in disseminating antimicrobial resistance and virulence genes amongst bacteria from several food animal species.

59 BASIC BIOLOGICAL SCIENCES↗

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗

Axisymmetric Radiative Transfer Models of Kilonovae

The detailed observations of GW170817 proved for the first time directly that neutron star mergers are a major production site of heavy elements. The observations could be fit by a number of simulations that qualitatively agree, but can quantitatively differ (e.g., in total r-process mass) by an order of magnitude. We categorize kilonova ejecta into several typical morphologies motivated by numerical simulations, and apply a radiative transfer Monte Carlo code to study how the geometric distribution of the ejecta shapes the emitted radiation. We find major impacts on both spectra and light curves. The peak bolometric luminosity can vary by two orders of magnitude and the timing of its peak by a factor of five. These findings provide the crucial implication that the ejecta masses inferred from observations around the peak brightness are uncertain by at least an order of magnitude. Mixed two-component models with lanthanide-rich ejecta are particularly sensitive to geometric distribution. A subset of mixed models shows very strong viewing angle dependence due to lanthanide "curtaining," which persists even if the relative mass of lanthanide-rich component is small. The angular dependence is weak in the rest of our models, but different geometric combinations of the two components lead to a highly diverse set of light curves. We identify geometry-dependent P Cygni features in late spectra that directly map out strong lines in the simulated opacity of neodymium, which can help to constrain the ejecta geometry and to directly probe the r-process abundances.

79 ASTRONOMY AND ASTROPHYSICS↗

Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

Report for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

Spin Stripes and Superconductivity in Bilayer Nickelates

The bilayer nickelate La3Ni2O7 has recently emerged as a high-temperature superconductor with unusual spin stripe order in its ambient pressure phase. We propose a microscopic Hamiltonian that faithfully reflects the crystalline symmetry of this system, with the primary aim of addressing its unconventional magnetism at ambient pressure. Using state-of-the-art density matrix renormalization group calculations, we show that (π/2,π/2) spin stripe order arises in our model at sizable Hund's coupling JH from a hidden quasi-one-dimensionality and persists over a range of electron concentrations. In the more symmetric high-pressure regime, our model exhibits enhanced interlayer pairing tendencies when the interlayer antiferromagnetic coupling J⊥ becomes sufficiently large. Our results provide a microscopic origin of the diagonal spin stripes and identify Hund's coupling JH and interlayer coupling J⊥ as key ingredients governing magnetic order and pairing tendencies in La3Ni2O7.

Wang, Hao-Xin [The Chinese University of Hong Kong↗

Enabling bifacial thin film devices by developing a back surface field using Cu x AlO y

Bifacial solar cells have the potential to increase the energy yield per unit area over traditional monofacial devices without significant added cost, driving $/kWh costs lower and accelerating the adoption of solar photovoltaics. However, the performance of bifacial thin film solar cells significantly lags that achieved by crystalline silicon cells. Here we incorporate wide bandgap Cu x AlO y as a back buffer layer for CdTe devices and achieve a backside illuminated device with high current density and high fill factor. Moreover, these values remain nearly constant even as the absorber layer thickness changes, indicating that a fully-depleted device is not required for efficient charge collection. We show that this response is indicative of a back surface field, albeit with a persistent high back surface recombination velocity. By managing electron reflection, we achieved a backside illumination conversion efficiency of 7.1% and bifaciality of 0.55 for a 3.3 µm CdTe device and 8.0% and 0.62 for a 2 µm device. Future improvements can be made by identifying and incorporating a passivation material that reduces the back surface recombination velocity.

36 MATERIALS SCIENCE↗

Dynamically downscaled seasonal heat wave projections in the CONUS

Heat waves are a well-documented hazard that are projected to increase in intensity, duration, and frequency with climate change. Regions of the US experience widely varying temperatures; for example, 35 °C is extremely hot for spring in the Northeast but not for summer in the Southeast. It is important to evaluate projections within a regional context and at a high enough resolution to understand the risks to populations. We identify heat waves across the Conterminous US (CONUS) under SSP5–8.5 from 2020 to 2059 with an ensemble of dynamically downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. We demonstrate that there are regional differences caused by seasonal and local drivers of persistent hot temperatures. Summer heat waves are increasing in intensity and duration faster than winter heat waves because of the atmospheric conditions that promote these events. Our analysis emphasizes the value of fine-resolution modeling for projecting future climate risks.

Rubin, Hannah [University of Tennessee, Knoxville ↗

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Merger Signatures are Common, but not Universal, in Massive, Recently Quenched Galaxies at z ~ 0.7

We present visual classifications of merger-induced tidal disturbances in 143 M* ~ 10 11 M ⊙ post-starburst galaxies at z ~ 0.7 identified in the $SQuIGG\vec{L}E$ Sample. This sample spectroscopically selects galaxies from the Sloan Digital Sky Survey that have stopped their primary epoch of star formation within the past ~500 Myr. Visual classifications are performed on Hyper Suprime-Cam imaging. We compare to a control sample of mass- and redshift-matched star-forming and quiescent galaxies from the Large Early Galaxy Census and find that post-starburst galaxies are more likely to be classified as disturbed than either category. This corresponds to a factor of $3.6$$^{+2.9}_{-1.3}$ times the disturbance rate of older quiescent galaxies and $2.1$$^{+1.9}_{-.73}$ times the disturbance rate of star-forming galaxies. Assuming tidal features persist for ≲500 Myr, this suggests merging is coincident with quenching in a significant fraction of these post-starbursts. Galaxies with tidal disturbances are younger on average than undisturbed post-starburst galaxies in our sample, suggesting tidal features from a major merger may have faded over time. This may be exacerbated by the fact that, on average, the undisturbed subset is fainter, rendering low-surface-brightness tidal features harder to identify. However, the presence of 10 young (≲150 Myr since quenching) undisturbed galaxies suggests that major mergers are not the only fast physical mechanism that shut down the primary epoch of star formation in massive galaxies at intermediate redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

Strain in Metal Halide Perovskite Thin Films - Interfacial Mechanical Coupling

Hybrid organic−inorganic metal halide perovskites (MHPs) are promising semiconductors for photovoltaics and optoelectronics, but their commercial viability is limited by instability, particularly strain induced by mismatch in coefficients of thermal expansion (CTE) between the perovskite film and substrate. Here, we investigate strain development and relaxation in MHP thin films using in situ bending and Grazing Incidence Wide-Angle X-ray Scattering (GIWAXS). We quantify the film− substrate interfacial mechanical coupling and identify interfacial slippage beyond a critical strain (∼0.4%), with Br-2PACz exhibiting comparatively stronger interfacial mechanical coupling among common interface modifiers. Time-resolved GIWAXS reveals reversible macrostrain during thermal cycling driven by CTE mismatch. Leveraging this behavior, we introduce a prestrain process that induces persistent compressive strain after cooling, with partial relaxation over time. These results provide insight into interfacial mechanical coupling and strain dynamics, offering a framework for strain engineering in perovskite devices.

metals↗

Functional succession of actively growing soil microorganisms during rewetting is shaped by precipitation history

Rewetting of seasonally dry soils induces a burst of microbial activity and carbon mineralization that changes nutrient availability and leads to succession. Yet the microbial functions that underpin this succession are not well described. Further, it’s unclear how previous precipitation frames microbial capacities after rewetting and how long these effects persist. We used isotopically-labeled water to rewet seasonally dry annual grassland soil that experienced either mean annual or reduced precipitation during the previous two years, and sampled at five subsequent time points. We used quantitative stable isotope probing (qSIP)-informed genome- resolved metagenomics to identify growing microorganisms, predict their capabilities, and analyze how these traits differed over time and between precipitation treatments. Organisms that grew after wetup were more abundant than non-growing organisms prior to the wet-up, suggesting that traits that initiate succession are pre-defined at the end of the prior plant growing season or via survival over the summer. Fast-growing organisms had fewer carbohydrate active enzyme (CAZy) genes per genome than slower-growing organisms, suggesting that although fast-growers were capable of degrading complex C, they may not specialize in this process. Differential abundance of CAZy genes in growing organisms throughout the succession implies that substrate availability varied with time. In contrast, the abundance of peptidases in growing organisms differed between precipitation treatments, but not over time following wet-up. Before wet-up, the soil organisms’ gene inventories were different between the two precipitation treatments. Surprisingly, this legacy effect waned after just one week. Thus, pre-wetup differences in microbial functional capacity converged shortly after rewetting.

Sieradzki, E T↗