Building envelope modeling calibration using aerial thermography
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Abstract Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype–environment associations in the hermit thrush ( Catharus guttatus ), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype–environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype–environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors.
Ion interactions strongly determine the solvation environments of multivalent electrolytes even at concentrations below that required for practical battery-based energy storage. This statement is particularly true of electrolytes utilizing ethereal solvents due to their low dielectric constants. These solvents are among the most commonly used for multivalent batteries based on reactive metals (Mg, Ca) due to their reductive stability. Recent developments in multivalent electrolyte design have produced a variety of new salts for Mg2+ and Ca2+ that test the limits of weak coordination strength and oxidative stability. Such electrolytes have great potential for enabling full-cell cycling of batteries based on these working ions. However, the ion interactions in these electrolytes exhibit significant and non-intuitive concentration relationships. In this work, we investigate a promising exemplar, calcium tetrakis(hexafluoroisopropoxy)borate (Ca(BHFIP)(2)), in the ethereal solvents 1,2-dimethoxyethane (DME) and tetrahydrofuran (THF) across a concentration range of several orders of magnitude. Surprisingly, we find that effective salt dissociation is lower at relatively dilute concentrations (e.g. 0.01 M) than at higher concentrations (e.g. 0.2 M). Combined experimental and computational dielectric and X-ray spectroscopic analyses of the changes occurring in the Ca2+ solvation environment across these concentration regimes reveals a progressive transition from well-defined solvent-separated ion pairs to de-correlated free ions. This transition in ion correlation results in improvements in both conductivity and calcium cycling stability with increased salt concentration. Comparison with previous findings involving more strongly associating salts highlights the generality of this phenomenon, leading to important insight into controlling ion interactions in ether-based multivalent battery electrolytes.
The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.
Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool-a tetranucleotide-informed metagenome stability diagram-that is publicly available in the integrated microbial genomes and microbiomes (IMG/M) platform for metagenome ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. IMPORTANCE: Microbes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.
ABSTRACT Recent studies have revealed diverse RNA viral communities in soils. Yet, how environmental factors influence soil RNA viruses remains largely unknown. Here, we recovered RNA viral communities from bulk metatranscriptomes sequenced from grassland soils managed for 5 years under multiple environmental conditions including water content, plant presence, cultivar type, and soil depth. More than half of the unique RNA viral contigs (64.6%) were assigned with putative hosts. About 74.7% of these classified RNA viral contigs are known as eukaryotic RNA viruses suggesting eukaryotic RNA viruses may outnumber prokaryotic RNA viruses by nearly three times in this grassland. Of the identified eukaryotic RNA viruses and the associated eukaryotic species, the most dominant taxa were Mitoviridae with an average relative abundance of 72.4%, and their natural hosts, Fungi with an average relative abundance of 56.6%. Network analysis and structural equation modeling support that soil water content, plant presence, and type of cultivar individually demonstrate a significant positive impact on eukaryotic RNA viral richness directly as well as indirectly on eukaryotic RNA viral abundance via influencing the co-existing eukaryotic members. A significant negative influence of soil depth on soil eukaryotic richness and abundance indirectly impacts soil eukaryotic RNA viral communities. These results provide new insights into the collective influence of multiple environmental and community factors that shape soil RNA viral communities and offer a structured perspective of how RNA virus diversity and ecology respond to environmental changes. IMPORTANCE Climate change has been reshaping the soil environment as well as the residing microbiome. This study provides field-relevant information on how environmental and community factors collectively shape soil RNA communities and contribute to ecological understanding of RNA viral survival under various environmental conditions and virus-host interactions in soil. This knowledge is critical for predicting the viral responses to climate change and the potential emergence of biothreats.
The coupling of catalysts and atmospheric-pressure plasma has the potential to improve the efficiency of certain catalytic reactions. Understanding the changes that the catalyst surface undergoes during exposure to plasma is key to improving plasma–catalytic performance. In this work, long term exposure of Pt–Al 2 O 3 powder catalyst to an Ar/N 2 /O 2 non-equilibrium atmospheric-pressure plasma-jet was investigated. Products produced by the interaction were analyzed downstream with Fourier-transform infrared spectroscopy while surface species were analyzed operandi with diffuse reflectance infrared Fourier transform spectroscopy. During exposure, the catalyst temperature was ramped cyclically between 100 °C and 350 °C to understand how substrate temperature affects the plasma–catalyst interaction. Long-lasting changes were revealed to take place on the catalyst surface during plasma exposure. At low temperatures, Pt–O and Pt–NO accumulate on the surface which react at elevated temperatures to form NO 2 . NO 2 initially appears to spill on to the Al 2 O 3 support as nitrites and nitrates instead of desorbing. Stable surface conditions are only achieved after prolonged plasma exposure, when nitrate sites on the Al 2 O 3 support are filled. By changing the catalyst temperature at various rates, the impact of total plasma species flux to the surface was analyzed. It was found that decreasing the heating rate increased the hysteresis in the pattern of NO 2 formation during thermal cycling. The variation with temperature demonstrates that plasma exposure results in a buildup of surface NO x and oxygen species which react or desorb at high temperatures. The observed changes are discussed from the generic viewpoint that a non-equilibrium plasma interacting with a catalyst at low temperature introduces metastable steady-state surface conditions. Upon heating above a threshold temperature, the introduced surface modifications can change either due to thermal effects, or, for a plasma environment, by additional interaction with the incident plasma species flux. The surface/material changes take place in a highly predictable fashion and after sufficient time above the threshold temperature reach a steady-state condition that is different from the transient behavior that is observed during initial heating. During cooling the plasma-surface interaction exhibits a different behavior than during heating, and this results in hysteresis of diverse observables. The metastability/hysteresis description appears quite generic and analogous to hysteresis behavior seen for different systems. Furthermore, it is expected to be useful for understanding the consequences of plasma–catalyst surface interactions for various systems.
One of the biggest consequences of large-scale environmental change is the loss of biodiversity. Recent studies predict a loss of 1 million species in the near future. Biodiversity loss and land use change through anthropogenic disturbance are known to affect disease exposure, disease severity, and disease impacts. It is becoming increasingly clear that biodiversity loss will lead to an increase in infectious diseases in certain regions. Changes in biodiversity may contain important signatures for prediction of infectious diseases and outbreaks. Here, we argue that areas of high or rapid biodiversity loss will be critical to monitor under the umbrella of global health security. Global Health Security consists of the actions and activities required to reduce the impact of public health events in populations living in different geographic regions and across international borders. Like infectious diseases, climate change and other causes of environmental changes do not respect international borders and are becoming more widespread in all parts of the world. Here, the relationships among biodiversity, climate and environmental changes, and pathogen prevalence are dynamic, context dependent, and complex. Accounting for this complexity requires the inclusion of the environment sector, presently missing, when evaluating the potential of global health security actions for mitigating disease risks.
Abstract Plant respiration is an important contributor to the proposed positive global carbon‐cycle feedback to climate change. However, as a major component, leaf mitochondrial (‘dark’) respiration ( R d ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of R d follows an optimal behaviour related to the need to maintain long‐term average photosynthetic capacity ( V cmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co‐ordination theory to predict the acclimation of R d to growth temperature via a link to V cmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co‐ordination theory predicts that field‐measured R d and V cmax accessed at growth temperature ( R d,tg and V cmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for R d and V cmax respectively. Data‐fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both R d and V cmax assessed at 25°C ( R d,25 and V cmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for R d acclimation to temperature that is simpler—and potentially more reliable—than the plant functional type‐based leaf respiration schemes currently employed in most ecosystem and land‐surface models.
Confinement of metal centers is a powerful tool to manipulate reactivity and tune selectivity in chemical transformations. While aluminum as a foil is inactive for carbon dioxide reduction and shows high selectivity for the hydrogen evolution reaction, here we show that aluminum confined in a metal-organic framework (MOF), MIL-53(Al), suppresses hydrogen evolution reaction activity and enhances carbon dioxide reduction. This aluminum MOF can produce up to 40% faradaic efficiency for carbon monoxide and formic acid. Here, this study demonstrates that the unique reaction environment created by the MOF enables changes in reaction selectivity and can impart atypical catalytic capabilities to metals.
Fluorescent labeling of proteins is a powerful tool for probing structure-function relationships with many biosensing applications. Structure-based rules for systematically designing fluorescent biosensors require understanding ligand-mediated fluorescent response mechanisms which can be challenging to establish. We installed thiol-reactive derivatives of the naphthalene-based fluorophore Prodan into bacterial periplasmic glucose-binding proteins. Glucose binding elicited paired color exchanges in the excited and ground states of these conjugates. X-ray structures and mutagenesis studies established that glucose-mediated color switching arises from steric interactions that couple protein conformational changes to twisting of the Prodan carbonyl relative to its naphthalene plane. Mutations of residues contacting the carbonyl can optimize color switching by altering fluorophore conformational equilibria in the apo and glucose-bound proteins. A commonly accepted view is that Prodan derivatives report on protein conformations via solvatochromic effects due to changes in the dielectric of their local environment. Here we show that instead Prodan carbonyl twisting controls color switching. These insights enable structure-based biosensor design by coupling ligand-mediated protein conformational changes to internal chromophore twists through specific steric interactions between fluorophore and protein.
Block copolymers (BCPs) offer distinct advantages for vat photopolymerization by enabling mechanically programmable network structures through microphase-separated morphologies that can be kinetically trapped during curing, yielding properties unattainable in homogeneous resins. However, the respective roles of repeat-unit sequence and solvent environment, together with their interplay in directing network formation and mechanical performance, remain unclear. Here, we synthesize a series of CO 2 -based polycarbonate copolymers comprising a crosslinkable glassy poly(vinyl cyclohexene carbonate) (PVCHC, A block) and a non-crosslinkable soft poly(propylene carbonate) (PPC, B block). The polymer sequence is systematically varied (ABA, BAB, and statistical), and solvent choice controls block-selective swelling to jointly control gelation behavior, microphase morphology, and mechanical response through changes in the accessibility and local environment of photocrosslinkable vinyl groups during network formation, as revealed by photorheology and small angle x-ray scattering. By tuning polymer sequence and curing solvent, we transform nominally identical formulations from brittle to highly ductile materials, achieving a three-orders-of-magnitude range in toughness (0.003 to 9.1 MJ m −3 ). These results establish clear structure–processing–property relationships and identify polymer sequence and selective solvation as powerful strategies for programming both printability and performance of block copolymer resins for additive manufacturing.
Additive Manufacturing (AM) is expected to play a large role in the labs-wide goals of accelerating innovation and leading in modern engineering. More specifically, AM is seen as a key enabling technology for increasing the agility of nuclear deterrence and other national security applications involving complex coupled environments. However, the impact of AM on these initiatives has not been as wide-ranging as hoped because – despite its unique qualities – the focus has mostly been on detailed qualification to force AM components into pre-existing performance envelopes. This paradigm fundamentally precludes the novel possibilities afforded by the geometric and material flexibility of AM. In particular, the engineering of small-scale features to undergo buckling and contact can cause large geometric and symmetry changes which provide responsiveness to different environments. Despite almost a decade of observing such behavior, there exists no way to systematically design for AM to exploit it. Our goal for this project was to connect material design to multi-environment component performance by reconceptualizing how to design for AM to exploit the buckling and contact of small-scale features.
Aldol condensation and esterification reactions provide paths to upgrade ethanol and acetaldehyde to higher- value molecules useful as fuels or intermediates for the synthesis of polymers. Transition-metal-substituted BEA zeolites (M-BEA) catalyze these reactions; however, the mechanisms for these processes in M-BEA and the effects of incidental or purposefully included silanol groups are not reported. Here, we combine kinetic and spectroscopic measurements obtained during catalytic reactions of acetaldehyde (CH 3 CHO), ethanol (C 2 H 5 OH), and hydrogen (H 2 ) mixtures over a series of Ti-BEA catalysts that possess a known range of silanol group densities to examine the kinetic relevance of intervening steps and the impact of silanol groups on catalytic rates. Across all Ti-BEA, rates for aldol condensation and esterification increase with the pressure of CH 3 CHO; however, C 2 H 5 OH and H 2 O weakly inhibit the rates of these reactions. The substitution of CD 3 CDO for CH 3 CHO decreases aldol condensation rates slightly (~10%) but leads to greater esterification rates (2- to 5-fold). The kinetic isotope effects together with the measured dependence of rates on reactant pressures suggest that aldol condensation and esterification occur on unoccupied Ti sites and involve multiple kinetically relevant steps. CH 3 CHO deprotonates irreversibly, and the kinetically relevant nucleophilic attack of the enolate to CH 3 CHO* (i.e., adsorbed CH 3 CHO on Ti sites) leads to aldol products, while the nucleophilic attack of the enolate to C 2 H 5 OH* gives esters. Selectivities toward aldol condensation increase with the ratio of CH 3 CHO to C 2 H 5 OH pressure and with increases in the silanol density of the as-synthesized Ti-BEA. During catalysis, in situ infrared spectroscopy demonstrates that these silanol groups react with C 2 H 5 OH to form ethoxysilane groups (i.e., SiOC 2 H 5 ) that modify the polarity of the environment near Ti active sites. As initial silanol densities increase, steady-state turnover rates for aldol condensation and esterification increase by factors of 5 and 2, respectively. The changes in rates and selectivities among Ti-BEA catalysts likely reflect changes in excess free energies of transition states for enolization and nucleophilic attack of the enolate to adsorbed coreactants. The differences in excess stability report on the interactions among reactive intermediates at framework Ti atoms and the ethoxysilane and remaining silanol groups present. Furthermore, the in situ modification of these pore environments confers changes in the stability of reactive species in a manner that contradicts intuition when considering the initial state of the catalyst but can be reconciled after accounting for the formation of persistent alkoxy surface moieties in the pores.
Analysis of microchemistry in fish fin spines offers a non-lethal approach to address key questions relating to spatial patterns and habitat use of fish. Reconstruction of life history is based on chemical changes within calcified structures with an underlying assumption of elemental incorporation relative to environmental concentration. However, information is limited on the relative influence of water and diet concentrations of elements to corresponding concentrations within fin spines. Strontium (Sr) is commonly used in trace element studies because of its predictable changes within watersheds, with recent applications showing utility of 87 Sr/ 86 Sr for greater spatial resolution. Therefore, this study evaluated the influence of water and diet 87 Sr/ 86 Sr on pectoral fin spines of Atlantic Sturgeon A. oxyrinchus oxyrinchus in controlled laboratory experiments. In the first experiment, fish were exposed to a control, water spiked with 0.10 mg/L of 87 Sr or diet spiked with 0.25 µg/g or 0.50 µg/g of 87 Sr in replicated tanks for 12 weeks. In a second experiment, to determine the time duration required to occupy a different environment for fin spine chemistry to change, fish were maintained in water- or diet-spiked treatments for 1, 2, 3, or 6 weeks. Additionally, to determine if temperature has an influence on 87 Sr/ 86 Sr in fin spines, fish were held at 15 and 25°C for 12 weeks. Fin spines were analyzed using laser ablation multi-collector inductively coupled plasma mass spectrometry (LA-MC-ICP-MS), and water and diet samples were analyzed using solution based ICP-MS. Results from Experiment 1 indicate water is the primary contributor and diet a secondary contributor of 87 Sr/ 86 Sr in pectoral fin spines of Atlantic Sturgeon. Results from Experiment 2 indicate fin spine 87 Sr/ 86 Sr can be influenced by water after 1 week and diet after 12 weeks. Additionally, although sample size was limited (n=3), water temperature did not have an effect on 87 Sr/ 86 Sr fin spine chemistry. In conclusion, these results provide guidance for interpretation of 87 Sr/ 86 Sr in pectoral fin spines of sturgeon and indicate usefulness of the technique for retrospectively estimating movement and habitat use.
Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.
Electroactive polymer thin films undergo repeated reversible structural change during operation in electrochemical applications. While synchrotron X-ray scattering is powerful for the characterization of stand-alone and ex situ organic thin films, in situ/operando structural characterization has been underutilized-in large part due to complications arising from supporting electrolyte scattering. This has greatly hampered the development of application relevant structure property relationships. Therefore, a new methodology for in situ/operando X-ray characterization that separates the incident and scattered X-ray beam path from the electrolyte is developed. As a proof of concept, the operando structural characterization of weakly-scattering, organic mixed conducting thin films in an aqueous electrolyte environment is demonstrated, accessing previously unexplored changes in the $\pi-\pi$ peak and diffuse scatter, while capturing the solvent swollen thin film structure which is inaccessible in previous ex situ studies. These in situ/operando measurements improve the sensitivity to structural changes, capturing minute changes not possible ex situ, and have multimodal potential such as combined Raman measurements that also serve to validate the true in situ/operando conditions of the cell. Finally, new directions enabled by this in situ/operando cell design are examined and state of the art measurements are compared.
This research focused on seasonally dry tropical forests (SDTFs), a biome that exhibits strong variation in ecological, hydrological and physical characteristics. Climate change is expected to strongly impact the atmospheric environment of SDTFs, and long-term trends in precipitation seasonality have already been detected. The extent to which such changes in seasonal moisture availability will impact SDTF carbon dynamics is highly uncertain, and much of this uncertainty hinges on poorly-understood belowground processes. Thus, the central objectives of this research were to qualitatively and quantitatively describe how belowground processes mediate the response of SDTF carbon dynamics to environmental change. We accomplished these aims through field campaigns in four tropical countries to characterize soil biogeochemistry and plant hydraulic traits, ecosystem-scale field experiments that manipulated both nutrients and rainfall, and simulation modeling.