Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “process modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Lignin-Derived Phenolic Compounds and Water Are Effective Cosolvents for Reductive Catalytic Fractionation

Reductive catalytic fractionation (RCF) is an effective lignin-first biorefining method to extract lignin as a stabilized oil from lignocellulosic biomass. To realize RCF at scale, process modeling has shown that minimizing the use of exogenous organic solvents is critical. To this end, here we investigate the ability of lignin-derived monomers to act as either solvents or cosolvents for RCF. We begin by examining the influence of lignin-derived aromatic compounds (4-propylguaiacol, 4-propylphenol, and propylbenzene) on RCF monomer yields and subsequently extend our analysis to mixtures of 4-propylguaiacol and either methanol or water. Here, we demonstrate that 4-propylguaiacol is an effective solvent for lignin extraction and depolymerization during RCF, especially when used in combination with water as a cosolvent. Cosolvent mixtures of 4-propylguaiacol and water enable up to 81% lignin extraction, monomer yields up to 25 wt %, and postreaction phase separation. However, unlike methanol, water as a cosolvent fails to inhibit aromatic ring hydrogenation when conducted over Ru/C as a catalyst, potentially leading to excess hydrogen consumption in a process utilizing this approach. Nonetheless, these results suggest a promising strategy for eliminating external organic solvents from RCF by utilizing mixtures of lignin-derived compounds and water as alternative extraction solvents.

09 BIOMASS FUELS↗
DOE OSTI2024

Analysis of differential scanning calorimetry data for aged plutonium

Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ε and ε to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ε phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ε to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Decoupling of redox processes from soil saturation in Arctic tundra

Permafrost thaw in warming Arctic landscapes alters hydrology and saturation-driven biogeochemical processes. Models assume that aerobic respiration occurs in drained soils while saturated soils support methanogenesis; however, saturated soils maintain redox gradients that host a range of anaerobic metabolisms. We evaluated how redox potential and redox-active solutes vary with soil moisture in the active layer of permafrost-affected acidic and non-acidic tundra hillslopes. Oxidizing conditions persisted in highly permeable organic horizons of both unsaturated tussock tundra and saturated wet sedge meadows. Redox potential decreased with depth in all soils as increasing soil bulk density restricted groundwater flow and oxygen diffusion. High concentrations of dissolved iron, phosphate, and organic carbon coincided with redox boundaries below the soil surface in acidic tundra, indicating active iron redox cycling and potential release of adsorbed phosphate during iron (oxyhydr)oxide dissolution. In non-acidic tundra, weatherable minerals affected nutrient dynamics more than redox-driven iron cycling, especially in low-lying, saturated areas where thaw reached mineral soils. The role of thaw depth and the ability of saturated soils to maintain oxidizing conditions in organic surface layers highlight the importance of soil physical properties and hydrology in predicting biogeochemical processes and greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Factors influencing mode choice of adults with travel-limiting disability

Introduction: Despite the plethora of research devoted to analyzing the impact of disability on travel behavior, not enough studies have investigated the varying impact of social and environmental factors on the mode choice of people with disabilities that restrict their ability to use transportation modes efficiently. This research gap can be addressed by investigating the factors influencing the mode choice behavior of people with travel-limiting disabilities, which can inform the development of accessible and sustainable transportation systems. Additionally, such studies can provide insights into the social and economic barriers faced by this population group, which can help policymakers to promote social inclusion and equity. Method: This study utilized a Random Parameters Logit model to identify the individual, trip, and environmental factors that influence mode selection among people with travel-limiting disabilities. Here, using the 2017 National Household Travel Survey data for New York State, which included information on respondents with travel-limiting disabilities, the analysis focused on a sample of 8,016 people. In addition, climate data from the National Oceanic and Atmospheric Administration were integrated as additional explanatory variables in the modeling process. Results: The results revealed that people with disabilities may be inclined to travel longer distances walking in the absence of suitable accommodation facilities for other transportation modes. Furthermore, people were less inclined to walk during summer and winter, indicating a need to consider weather conditions as a significant determinant of mode choice. Moreover, low-income people with disabilities were more likely to rely on public transport or walking. Conclusion: Based on this study’s findings, transportation agencies could design infrastructure and plan for future expansions that is more inclusive and accessible, thus catering to the mobility needs of people with travel-limiting disabilities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

DPC Cement Filler Development (Progress Report)

The fillers research and development (R&D) program, mostly experimental, is part of a broader R&D program that includes new process modeling and performance assessment of criticality effects and the overall importance of criticality to repository performance (consequence screening). A literature research and consultation effort with experts by Hardin and Brady (2018) identified several potentially effective and workable filler materials including cements (primarily phosphate based), moltenmetal alloys, and low-temperature glasses. Filler attributes were defined, and the preliminary lists were compared qualitatively. Further comparative analysis will be done (e.g., cost estimates) after experimental screening has narrowed the list of alternatives. The following cement filler compositions were selected for experimental development work and accelerated testing in FY20: Aluminum phosphate cements (APCs); more specifically aluminum oxide / aluminum phosphate (Al 2 O 3 / AlPO 4 ) cements in which Al 2 O 3 serves as the filler material bound by an AlPO 4 binder formed by the reaction of Al 2 O 3 with H 3 PO 4 ; Calcium phosphate cements (CPCs); more specifically composed of pure or nearly pure hydroxyapatite or HAP (Ca 5 (PO 4 ) 3 (OH)); Wollastonite phosphate cements (WPC), specifically wollastonite and aluminum or calcium aluminum phosphates in which CaSiO 3 serves as the filler material and the phosphate serves as the binder. The FY20 effort focused on the optimization of compositions and subsequent processing of these three materials to achieve dense and well-consolidated monolithic samples with 30 to 40% porosity and permeabilities of 1 millidarcy. At the close of this progress report the aluminum phosphate cements (APCs) and the wollastonite phosphate cements (WPCs) appear to show the most promise for continued development. Less progress has been made with the calcium phosphate cements (CPCs); their slurry viscosities are high (and difficult to measure) and they exhibit relatively short cure times of 2 to 3 hours with concomitant and excessive volatile (e.g. CO 2 ) generation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗
DOE OSTI2024

The ATLAS experiment software on ARM

With an increased dataset obtained during the Run 3 of the LHC at CERN and the even larger expected increase of the dataset by more than one order of magnitude for the HL-LHC, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM architecture is becoming a competitive and energy efficient alternative. Some surveys indicate its increased presence in HPCs and commercial clouds, and some WLCG sites have expressed their interest. Chip makers are also developing their next generation solutions on ARM architectures, sometimes combining ARM and GPU processors in the same chip. Consequently it is important that the ATLAS software embraces the change and is able to successfully exploit this architecture. We report on the successful porting to ARM of the Athena software framework, which is used by ATLAS for both online and offline computing operations. Furthermore we report on the successful validation of simulation workflows running on ARM resources. For this we have set up an ATLAS Grid site using ARM compatible middleware and containers on Amazon Web Services (AWS) ARM resources. The ARM version of Athena is fully integrated in the regular software build system and distributed in the same way as other software releases. In addition, the workflows have been integrated into the HEPscore benchmark suite which is the planned WLCG wide replacement of the HepSpec06 benchmark used for Grid site pledges. In the overall porting process we have used resources on AWS, Google Cloud Platform (GCP) and CERN. A performance comparison of different architectures and resources will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

02 PETROLEUM↗
DOE OSTI2021

An automated multi-component gas adsorption system (MC GAS)

The knowledge gap on adsorption of complex mixtures in the literature relative to single component data represents a persistent obstacle to developing accurate process models for adsorption separations. The collection of mixed gas adsorption data is an imminent need for improved understanding of the behavior of adsorbent systems in these diverse adsorption applications. Current approaches to understanding mixture adsorption using predictive theories based on pure component adsorption experiments often fail to capture the behavior of more complex, non-ideal systems. In this work, we present an automated volumetric instrument for the measurement of mixed gas adsorption isotherms. This instrument was validated by comparison to other in-house instruments and data available in the literature, and the binary adsorption measurements were found to be thermodynamically consistent. The automation of this instrument allows for rapid collection of high-quality mixture adsorption data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Multi-track reconstruction algorithm in the Mu2e experiment

The Mu2e experiment, under construction at Fermilab, will search for the neutrino-less coherent $\mu^-N\rightarrow e^-N$ conversion in the field of a $^{27}$Al nucleus. Such a process violates lepton flavor conservation. About $60\%$ of muons stopped by an $^{27}$Al nucleus will undergo nuclear capture, while about $40\%$ will decay in orbit. To quantify the conversion probability, we define $R_{\mu e}$, which is given by the ratio between the $\mu^-\rightarrow e^-$ conversion rate and the nuclear capture rate [1]: \begin{equation} R_{\mu e}= \frac{\Gamma\left(\mu^- + N\left(Z,A\right)\rightarrow e^- + N\left(Z,A\right)\right)}{\Gamma\left(\mu^- + N\left(Z,A\right)\rightarrow \nu^-_\mu + N\left(Z-1,A\right)\right)}\,. \end{equation} The upper limit on $R_{\mu e}$ is $7\cdot 10^{-13}$ at $90\%$ CL, set by the SINDRUM II experiment~\cite{SINDRUM II:limit}. The goal of the Mu2e experiment is to reach a sensitivity on $R_{\mu e}$ of $8\cdot 10^{-17}$ at $90\%$ CL. This represents a four-order of magnitude improvement over the current experimental limit. Mu2e will take its first data in 2027. The signature for the muon conversion is a monochromatic electron of $104.97$~\si{\mega\eV}/c, an energy slightly below the muon rest mass. While the main experiment goal is to reconstruct the conversion electron, i.e., an event with a single track, there are motivations to develop an efficient tracking algorithm for reconstructing more simultaneous tracks. This could better constrain the background generated by $p\bar{p}$-annihilation in the Al target and to search for other Beyond the Standard Model processes. In this paper, we present an algorithm designed to reconstruct multi-particle events.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Development and Advanced Testing of DPC Cement Fillers

The fillers research and development (R&D) program, mostly experimental, is part of a broader R&D program that includes new process modeling and performance assessment of criticality effects and the overall importance of criticality to repository performance (consequence screening). A literature research and consultation effort with experts by Hardin and Brady (2018) identified several potentially effective and workable filler materials including cements (primarily phosphate based), moltenmetal alloys, and low-temperature glasses. Filler attributes were defined, and the preliminary lists were compared qualitatively. Further comparative analysis will be done (e.g., cost estimates) after experimental screening has narrowed the list of alternatives. The research presented here is focused Sandia’s efforts for the development of phosphate-based cement fillers. Molten metal filler research is an ongoing activity at Oak Ridge National Laboratories and is not discussed herein. After the completion of the FY20 research effort the following cement filler compositions were selected for further experimental development work and advanced testing in FY21: 1. Aluminum phosphate cements (APCs); more specifically aluminum oxide / aluminum phosphate (Al 2 O 3 / AlPO 4 ) cements in which Al 2 O 3 serves as the filler material bound by an AlPO 4 binder formed by the reaction of Al 2 O 3 with various phosphate sources; 2. Wollastonite phosphate cements (WPCs), specifically wollastonite and aluminum or calcium aluminum phosphates in which CaSiO 3 serves as the filler material bound by a calcium phosphate that serves as the binder; and 3. Calcium aluminate phosphate cements (CAPCs) specifically grossite (CaAl 4 O 7 ) and hibonite (CaAl 11 O 18 ) fillers bound by an aluminum phosphate that serves as the binder. This effort focused on the optimization and subsequent processing of these three cements to achieve dense and well-consolidated monolithic samples. Upon completion of the FY21 effort the aluminum phosphate cements (APCs) and the calcium aluminate phosphate cements (CAPCs) show the most promise for advanced testing and scale up. We will begin the work in FY22 focused on testing the performance of these two cements in small scale DPCs as well as advanced materials testing to evaluate cement performance under expected radiation doses and representative post-closure geochemical environments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Forecasting high-dimensional spatio-temporal systems from sparse measurements

This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Search for leptoquarks decaying into the bτ final state in pp collisions at $\sqrt{\textrm{s}}$ = 13 TeV with the ATLAS detector

A search for leptoquarks decaying into the $bτ$ final state is performed using Run 2 proton-proton collision data from the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb —1 at $\sqrt{\textrm{s}}$ = 13 TeV recorded by the ATLAS detector. The benchmark models considered in this search are vector leptoquarks with electric charge of 2/3e and scalar leptoquarks with an electric charge of 4/3e. No significant excess above the Standard Model prediction is observed, and 95% confidence level upper limits are set on the cross-section times branching fraction of leptoquarks decaying into bτ. For the vector leptoquark production two models are considered: the Yang-Mills and Minimal coupling models. In the Yang-Mills (Minimal coupling) scenario, vector leptoquarks with a mass below 1.58 (1.35) TeV are excluded for a gauge coupling of 1.0 and below 2.05 (1.99) TeV for a gauge coupling of 2.5. In the case of scalar leptoquarks, masses below 1.28 (1.53) TeV are excluded for a Yukawa coupling of 1.0 (2.5). Finally, an interpretation of the results with minimal model dependence is performed for each of the signal region categories, and limits on the visible cross-section for beyond the Standard Model processes are provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Search for an excited lepton that decays via a contact interaction to a lepton and two jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV

Results are presented from a search for events containing an excited lepton (electron or muon) produced in association with an ordinary lepton of the same flavor and decaying to a lepton and two hadronic jets. Both the production and the decay of the excited leptons are assumed to occur via a contact interaction with a characteristic energy scale Λ. The branching fraction for the decay mode under study increases with the mass of the excited lepton and is the most sensitive channel for very heavy excited leptons. The analysis uses a sample of proton-proton collisions collected by the CMS experiment at the LHC at $ \sqrt{s} $ = 13 TeV, corresponding to an integrated luminosity of 77.4 fb$^{−1}$. The four-body invariant mass of the two lepton plus two jet system is used as the primary discriminating variable. No significant excess of events beyond the expectation for standard model processes is observed. Assuming that Λ is equal to the mass of the excited leptons, excited electrons and muons with masses below 5.6 and 5.7 TeV, respectively, are excluded at 95% confidence level. These are the best limits to date.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Electrolyzer and Fuel Cell Recycling for a Circular Hydrogen Economy

Abstract Electrolyzers and fuel cells will be crucial for achieving global clean hydrogen and industrial decarbonization goals. However, the nascent clean hydrogen sector faces uncertainties around material supply chains and technology end‐of‐life management. This work aims to guide the transition to a circular hydrogen economy by using process modeling, techno‐economic analysis, and life cycle assessment to evaluate the material cost, energy use, greenhouse gas (GHG) emissions, toxicity, and water use of five potential recycling strategies for proton exchange membrane electrolyzers (PEMWE) and fuel cells (PEMFC). Hydrometallurgy, acid dissolution, and electrochemical dissolution are shown to offer 2–7 times improvement across all assessed metrics relative to the manufacturing of PEMWE and PEMFC from raw materials. Recycling can also lower the raw material demand, material cost, energy use, and GHG emissions associated with PEMWE and PEMFC deployment in the United States in 2050 by 23%, 19%, 21%, and 16%, respectively. This study provides key insights into the costs, benefits, and complexities of recycling strategies for PEMWE and PEMFC, aiding the development of a circular economy that is synergistic with clean hydrogen deployment.

08 HYDROGEN↗
DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗
DOE OSTI2022

Advance modification of polyacrylonitrile nanofibers for enhanced removal of hexavalent chromium from water

Hexavalent chromium [Cr(VI)] is a known carcinogenic and mutagenic heavy metal. Its level in drinking water is regulated worldwide to protect public health. This work presents a novel chemical method to modify the polyacrylonitrile (PAN) nanofibers for efficient Cr(VI) removal from water. The PAN nanofibers with an average diameter of 165 nm are produced using the electrospinning technique. Through a 2-step chemical modification process, the amidine polyacrylonitrile (APAN) nanofibers are synthesized by the conversion of nitrile groups [—C≡N] in PAN nanofibers into amidines [—C(N=NH)(NH 2 )]. The attenuated total reflectance Fourier transform (ATR-FTIR) spectroscopy revealed the successful conversion of nitrile groups into amidoximes, resulting in amidoximated polyacrylonitrile nanofibers, and subsequent conversion into amidine functional groups, forming the APAN nanofibers. Greater concentration of hydroxylamine hydrochloride, increased reaction time and temperature yielded higher conversion of nitrile groups into amidoximes to a maximum of 37%. The Cr(VI) uptake by APAN nanofibers was found as a multilayer adsorption process modeled by Freundlich isotherm. The maximum Langmuir Cr(VI) adsorption capacity for APAN nanofibers was found as 225 mg g -1 at pH = 3.0 after 4.0 h exposure duration. The regeneration study revealed the excellent reusability of APAN nanofibers after five adsorption/desorption cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Interactions Between Climate and Species Drive Future Forest Carbon and Water Balances

Global change is altering forest carbon and water balances; however, the extent to which tree species shape ecosystem‐scale responses to climate, particularly in biodiverse forests, remains unclear. To address this, we simulated the effects of an envelope of future climate conditions on watershed carbon and water balances and quantified the contributions of tree species based on their xylem anatomy. We accomplished this by incorporating species‐level transpiration calculations into a landscape‐scale ecosystem process model. Our revised model linked the effects of forest succession, species composition, and climate change on water and carbon. Calibration of forest water fluxes using sap flux measurements and catchment water balances captured variability in species transpiration and interannual ET in biodiverse, humid temperate forest catchments in the southern Blue Ridge Mountains, USA. Across wet and dry future climate projections, ET increased, and streamflow and net carbon uptake decreased, particularly under a scenario of increasing drought. Despite accounting for just 30% of current biomass, diffuse‐porous tree species were the main driver of carbon and water flux responses now and in the future, thus intensifying the increase in ET and decline in streamflow. As diffuse‐porous biomass continues to increase, these forests will be increasingly sensitive to drought, amplifying losses of carbon sequestration and freshwater delivery.

54 ENVIRONMENTAL SCIENCES↗