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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗
DOE OSTI2023

Band Edge Excitons and Amplified Spontaneous Emission of Mercury Chalcogenide Nanoplatelets

Colloidal nanoplatelets of HgSe and HgTe prepared indirectly through cation exchange reactions can transfer many of the advantageous properties of atomically precise, 2D cadmium chalcogenides to the near-infrared (NIR) spectral window. In this work, HgSe and HgTe nanoplatelets are studied to understand their fundamental photophysical properties, particularly those areas of similarity and difference from cadmium-based NPLs, and to examine their potential as optical gain media. Similar to cadmium chalcogenide NPLs, low-temperature photoluminescence of HgTe NPLs displays two-color emission that depends on temperature, sample, fluence, excitation frequency, and irradiation time. Both HgTe and HgSe show nanosecond emission dynamics at temperatures as low as 2.5 K, with no indication that bright-dark excitonic splitting governs the low-temperature photoluminescence. Collectively, experimental data is most consistent with emission from a negative trion state at low temperature. Although the mercury chalcogenide nanoplatelets are shown to have broadened optical resonances compared to the cadmium chalcogenides from which they are derived, they retain slow Auger recombination and can display low-threshold amplified spontaneous emission in the NIR spectral window. Optical pumping thresholds for HgTe NPLs are observed as low as 4.4 µJ cm -2 and highlight the potential 2D nanoplatelets as gain medium in the near-infrared.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ECEI characterization of pedestal fluctuations in quiescent H-mode plasmas in DIII-D

Here, electron cyclotron emission imaging (ECEI) is employed to characterize the magneto hydraulics dynamics (MHD) fluctuations at the quiescent H-mode pedestals in DIII-D. Pedestal MHD fluctuations cause ECE radiation temperature fluctuations $\delta {T_{e{\text{,rad}}}}$ in both the pedestal and scrape-off-layer (SOL). A synthetic ECE platform is utilized for detailed interpretation of the ECEI signals in the SOL and pedestal regions. It is observed that the ECE radiation $\delta {T_{e{\text{,rad}}}}$, which is located in the SOL region according to the cold and optically thick plasma resonance assumption, is extremely sensitive to MHD radial displacements near the separatrix, exhibiting radiation inversion to $\delta {T_{e{\text{,rad}}}}$ at the pedestal. Here, the radiation inversion refers to the opposite phase between the radiation fluctuation at the pedestal and the radiation fluctuation at the SOL. Consequently, the quasi-coherent MHD (QCM), which displays a radiation inversion, is found to be consistent with an MHD radial structure that has a strong displacement near the separatrix. In contrast, the edge harmonic oscillation (EHO), which displays weak or no inversion, is found to be consistent with an MHD radial displacement structure peaking at the pedestal top. The ECEI data, interpreted with synthetic ECE, are in qualitative agreement with beam emission spectroscopy measurements on DIII-D for the relative radial extent and localization of the EHO and QCM. The high sensitivity of ECE radiation to separatrix displacements can be used to detect turbulence or MHD fluctuations near the separatrix, which may affect the transport across the separatrix and the wetted area in the divertor. The $\delta {T_{e{\text{,rad}}}}$ inversion measured with an ECE or ECEI system potentially provides important information on the magnetic field $\left| {{B_{{\text{sep}}}}} \right|$ at the separatrix, which helps constrain the pedestal equilibrium reconstruction and achieve an unambiguous mapping of the ECE/ECEI system with respect to the separatrix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Superconducting gap symmetry from Bogoliubov quasiparticle interference analysis on Sr 2 RuO 4

Here, the nature of the superconducting order parameter in Sr 2 RuO 4 has generated intense interest in recent years. Since the superconducting gap is very small, high-resolution methods such as scanning tunneling spectroscopy might be the best chance to directly resolve the gap symmetry. Recently, a Bogoliubov quasiparticle interference imaging (BQPI) experiment has suggested that the d x 2 –y 2 gap symmetry is appropriate for Sr 2 RuO 4 . In this work, we use a material-specific theoretical approach based onWannier functions of the surface of Sr 2 RuO 4 to calculate the continuum density of states as detected in scanning tunneling microscopy experiments. We examine several different proposed gap order parameters and calculate the expected BQPI pattern for each case. Comparing to the available experimental data, our results suggest that a s' + id xy gap order parameter is the most probable state, but the measured BQPI patterns still display features unaccounted for by the theory for any of the states currently under discussion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

HVDC Surface Flashover in Compressed Air for Various Dielectrics

Here, this study measures the voltage at which flashover occurs in compressed air for a variety of dielectric materials and lengths in a uniform field for DC voltages up to 100 kV. Statistical time lag is recorded and characterized, displaying a roughly exponential dependence on breakdown voltage. Of the materials tested, acrylic is observed to be the most resistant to flashover. These data are intended to facilitate the design of compressed-air insulated high voltage systems as an alternative to SF 6 insulated systems.

42 ENGINEERING↗
DOE OSTI2021

Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch

As renewable energy generation deployment increases, the operation of electrical grids becomes more complex. Economic dispatch is part of a grid operator's regular decision process where the amount of energy to generate is determined based on the number of available generators and the actual level of energy demand. Renewable generators are inherently stochastic due to the chaotic nature of weather patterns, and thus, real-time decisions of economic dispatch become increasingly complex. Modeling efforts to assist in these decisions in the highest fidelity typically take hours to days to solve on leadership-class computers; too long for the 5-minute operational time-frame demanded of operators. Alternatively, multi-fidelity approximations can be used to predict generation levels quickly and with sufficient accuracy to be used for real-time operations. We have developed a visualization tool to demonstrate the utility of multi-fidelity approximations by displaying contextual results of economic dispatch approximations, comparisons across fidelity levels of generation levels and possible failures to meet demand, and meta-data on the modeling setup.

interactive visualization↗
DOE OSTI2024

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

09 BIOMASS FUELS↗
DOE OSTI2022

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗
DOE OSTI2022

Pectin methyltransferase QUASIMODO2 functions in the formation of seed coat mucilage in Arabidopsis

Pectin, cellulose, and hemicelluloses are major components of primary cell walls in plants. In addition to cell adhesion and expansion, pectin plays a central role in seed mucilage. Seed mucilage contains abundant pectic rhamnogalacturonan-I (RG-I) and lower amounts of homogalacturonan (HG), cellulose, and hemicelluloses. Previously, accumulated evidence has addressed the role of pectin RG-I in mucilage production and adherence. However, less is known about the function of pectin HG in seed coat mucilage formation. In this study, we analyzed a novel mutant, designated things fall apart2 (tfa2), which contains a mutation in HG methyltransferase QUASIMODO2 (QUA2). Etiolated tfa2 seedlings display short hypocotyls and adhesion defects similar to qua2 and tumorous shoot development2 (tsd2) alleles, and show seed mucilage defects. Here, the diminished uronic acid content and methylesterification degree of HG in mutant seed mucilage indicate the role of HG in the formation of seed mucilage. Cellulosic rays in mutant mucilage are collapsed. The epidermal cells of seed coat in tfa2 and tsd2 display deformed columellae and reduced radial wall thickness. Under polyethylene glycol treatment, seeds from these three mutant alleles exhibit reduced germination rates. Together, these data emphasize the requirement of pectic HG biosynthesis for the synthesis of seed mucilage, and the functions of different pectin domains together with cellulose in regulating its formation, expansion, and release.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Enhanced ordering in length-polydisperse carbon nanotube solutions at high concentrations as revealed by small angle X-ray scattering

Carbon nanotubes (CNTs) are stiff, all-carbon macromolecules with diameters as small as one nanometer and few microns long. Solutions of CNTs in chlorosulfonic acid (CSA) follow the phase behavior of rigid rod polymers interacting via a repulsive potential and display a liquid crystalline phase at sufficiently high concentration. Here, we show that small-angle X-ray scattering and polarized light microscopy data can be combined to characterize quantitatively the morphology of liquid crystalline phases formed in CNT solutions at concentrations from 3 to 6.5% by volume. We find that upon increasing their concentration, CNTs self-assemble into a liquid crystalline phase with a pleated texture and with a large inter-particle spacing that could be indicative of a transition to higher-order liquid crystalline phases. We explain how thermal undulations of CNTs can enhance their electrostatic repulsion and increase their effective diameter by an order of magnitude. By calculating the critical concentration, where the mean amplitude of undulation of an unconstrained rod becomes comparable to the rod spacing, we find that thermal undulations start to affect steric forces at concentrations as low as the isotropic cloud point in CNT solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions

Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade-offs between simple climate-independent econometric-based models and complex climate-sensitive data-driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. Here, the climate-independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate-sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. -27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate-independent model. The climate-sensitive workflow increased model accuracy and characterized climate-demand interactions, demonstrating a novel tool to enhance water system management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predation probabilities and functional responses: How piscivorous waterbirds respond to pulses in fish abundance

Abstract How predators respond to changes in prey abundance (i.e., functional responses) is foundational to consumer–resource interactions, predator–prey dynamics, and the stability of predator–prey systems. Predation by piscivorous waterbirds on out‐migrating juvenile steelhead trout ( Oncorhynchus mykiss ) is considered a factor affecting the recovery of multiple Endangered Species Act‐listed steelhead populations in the Columbia River basin. Waterbird functional responses, however, may vary by predator species and location, with important implications to predator management strategies. We used a 13‐year dataset on waterbird abundance across seven breeding colonies (three Caspian tern [ Hydroprogne caspia ], two double‐crested cormorant [ Nannopterum auritum ], and two California and ring‐billed gull [ Larus californicus and Larus delawarensis ] colonies) and steelhead tag‐recovery data (>645,000 tagged and >32,000 recovered steelhead) to quantify weekly predation probabilities and functional responses across waterbird species, colonies, and years. Weekly predation probabilities were highly variable, ranging from 0.01 to 0.30 at tern colonies, 0.01 to 0.20 at cormorant colonies, and 0.03 to 0.13 at gull colonies. Per capita predation probabilities were an order of magnitude higher at inland tern and cormorant colonies relative to estuary colonies of the same species. Terns displayed Type II functional responses across colonies and years, where predation probabilities peaked at low steelhead abundances and declined as steelhead abundance increased (i.e., predator swamping). Cormorants nesting at the large estuary colony (several thousand birds) displayed a Type III functional response, but cormorants nesting at the smaller inland colony (several hundred birds) displayed a Type II response. Consumption probabilities of steelhead by gulls remained consistent across a large range of steelhead availability, suggesting a Type I or a Type III functional response, but a lack of colony abundance data prevented quantifying functional responses. The level of tern predation combined with Type II functional responses indicate possible population‐level impacts that could destabilize small or declining prey populations. Conversely, the apparent Type III functional responses of gulls and estuary nesting cormorants are indicative of prey switching behaviors targeted at periods of high steelhead abundance. Our results illustrate the complexity of predator–prey interactions and the importance of quantifying predator‐ and location‐specific functional responses when predicting the efficacy of management strategies to enhance prey populations.

Hostetter, Nathan J.↗
DOE OSTI2024

Evaluation of Physical Microphysical Property Retrieval Algorithms During the 2020 IMPACTS Field Campaign

The NASA Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W-band) and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. This study evaluates two mass-dimensional relationships (Brown and Francis (1995, BF95); Heymsfield (2014, H14) and two lidar-radar microphysical retrieval algorithms (Cloudsat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar-radar data community)) to estimate aircraft-retrieved volume extinction coefficient (σ), ice water content (IWC), and effective radius (r e ) during the 2020 IMPACTS deployment. BF95 and H14 have a close 1:1 correlation (R 2 = 0.98) with in-situ observations of σ. However, only BF95 displays a linear, consistent, and almost temperature-independent low bias for IWC and r e , which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate σ, IWC, and r e . For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (-15°C to -10°C) and near freezing (≥-5°C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Simple Analysis of Primary and Secondary Bile Salt Hydrolysis in Mouse and Human Gut Microbiome Samples by Using Fluorogenic Substrates

Animals produce bile to maximize absorption of lipophilic nutrients in the gut. The physical properties of bile are largely dictated by amphipathic bile salt molecules, which also act as signaling molecules that bind host receptors and modulate physiological processes. Upon excretion of bile salts into the gut, the gut microbiome can create metabolites that have modified signaling capabilities. The category and magnitude of bile salt metabolism can affect the host either positively or negatively. A key modification is bile salt hydrolysis, which is a prerequisite step for all additional microbial modification. Here, we have synthesized five different fluorogenic bile salts that report on hydrolysis activity using a simple, continuous assay. We profile activity in both murine and human fecal samples. Our data demonstrate that most gut microbiomes have the highest capacity for hydrolysis of host-produced bile salts, but some microbially modified bile salts also display significant turnover.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Iron Availability within the Leaf Vasculature Determines the Magnitude of Iron Deficiency Responses in Source and Sink Tissues in Arabidopsis

Abstract Iron (Fe) uptake and translocation in plants are fine-tuned by complex mechanisms that are not yet fully understood. In Arabidopsis thaliana, local regulation of Fe homeostasis at the root level has been extensively studied and is better understood than the systemic shoot-to-root regulation. While the root system is solely a sink tissue that depends on photosynthates translocated from source tissues, the shoot system is a more complex tissue, where sink and source tissues occur synchronously. In this study, and to gain better insight into the Fe deficiency responses in leaves, we overexpressed Zinc/Iron-regulated transporter-like Protein (ZIP5), an Fe/Zn transporter, in phloem-loading cells (proSUC2::AtZIP5) and determined the timing of Fe deficiency responses in sink (young leaves and roots) and source tissues (leaves). Transgenic lines overexpressing ZIP5 in companion cells displayed increased sensitivity to Fe deficiency in root growth assays. Moreover, young leaves and roots (sink tissues) displayed either delayed or dampened transcriptional responses to Fe deficiency compared to wild-type (WT) plants. We also took advantage of the Arabidopsis mutant nas4x-1 to explore Fe transcriptional responses in the opposite scenario, where Fe is retained in the vasculature but in an unavailable and precipitated form. In contrast to proSUC2::AtZIP5 plants, nas4x-1 young leaves and roots displayed a robust and constitutive Fe deficiency response, while mature leaves showed a delayed and dampened Fe deficiency response compared to WT plants. Altogether, our data provide evidence suggesting that Fe sensing within leaves can also occur locally in a leaf-specific manner.

Cell Biology↗
DOE OSTI2021

Clumped isotope thermometry of modern and fossil snail shells from the Himalayan-Tibetan Plateau: Implications for paleoclimate and paleoelevation reconstructions

Carbonate clumped isotope thermometry has been applied to fossil mollusk shells from Tibet to reconstruct the paleoclimate and paleoelevation of the region. However, inferred paleoelevation and climatic conditions from this proxy are inconsistent with paleontological evidence. Here, we report new clumped isotope data from both modern and fossil (5–4 Ma) freshwater mollusk shells with the results of X-ray diffraction (XRD) analyses of fossil shells from the Himalayan-Tibetan Plateau. Although all of the fossil shells analyzed in this study appeared pristine based on visual inspection, XRD data reveal that more than half of these apparently “pristine” fossil shells contain trace amounts of calcite. Clumped isotope temperatures derived from the fossil shells display a large range of variation (>22 °C). Among the fossil shells analyzed, those containing traces of calcite have yielded temperatures that are on average ~10 °C lower than those with no detectable calcite from the same strata. These observations suggest that clumped isotope alteration can occur in aragonite shells in low-temperature environments and even in shells with no visible signs of alteration that contain only traces of calcite. The temperatures derived from fossil shells with no detectable calcite are on average 4 °C higher than those derived from modern shells, indicating that southwest Tibet was warmer 4–5 Ma than today. After accounting for temperature change due to global cooling, the difference in clumped isotope temperatures between pristine fossil shells and modern shells suggests that the paleoelevation of the southwestern Tibetan Plateau in the Pliocene was similar to its present-day elevation.

Geology↗