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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Understanding the impacts of inorganic species in woody biomass for preprocessing and pyrolysis–A review

Woody biomass represents an abundant resource for sustainable biofuels, biochemicals, and bioproducts. Technologies for converting woody biomass have been established for decades, and research consistently highlights the critical role of inorganic species and ash plays in feedstock handling and conversion processes, including equipment plugging, corrosion, and catalyst deactivation. A thorough understanding of the variability, transport behavior, and downstream impact of inorganic species in woody biomass is essential for defining feedstock quality specifications and developing effective management strategies for conversion processes. This review compiles critical information in five main sections: 1) inorganic species concentration in woody biomass, based on anatomical fractions and their sources of variability; 2) technique features for quantifying inorganic elemental chemical analysis; 3) impacts of inorganic species on biomass preprocessing; 4) impacts of inorganic species on pyrolysis, and 5) mitigation strategies. Additionally, this review explores future challenges and opportunities in addressing the impacts of inorganic species on biomass quality. These insights aim to support the sustainable development of the biomass-to-bioenergy pipeline and ensure high-quality lignocellulosic feedstocks for efficient downstream conversions. The findings offer valuable guidance to policy makers, industry stakeholders, and researchers in developing effective strategies for managing inorganic species in woody biomass and fostering the sustainable processes for lignocellulosic biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2024

Use of Ionic Liquids for Hemp Fiber Degumming

Degumming or retting to remove pectin, lignin, and hemicellulose is a critical step in the production of high-quality hemp fibers. Current technologies, including traditional water or dew retting, as well as chemical, enzymatic, and physical degumming processes, have inherent environmental risks and produce fibers of varying qualities. Herein we explore the use of an ionic liquid (IL) capable of dissolving pectin, lignin, and hemicellulose, but not cellulose, in combination with different oxidating agents to degum hemp. In this study, we observed that treating raw hemp fibers with the IL choline acetate ([Cho][OAc]), alone or in combination with aqueous H 2 O 2 , led to a reduction in the gummy materials and lignin content of the resulting samples of up to 63 and 78%, respectively. More importantly, the high crystallinity and low reduction in tensile strength of the resulting fibers make these novel and relatively simple IL-based processes promising alternatives toward the sustainable preparation of hemp-based materials suitable for use in the textile industry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Cool GTL for the Conversion of Biogas to Jet Fuel

Cool GTL is a new process under development for conversion of biogas from digestors or CO2 +H2 to high quality drop in jet fuel. Cool GTL is designed to be a streamlined simplified low cost process which can be cost effective for small scale biogas applications. Cool GTL has the advantage that it uses a high activity bi-reforming catalyst, in a streamlined electric reformer design, to make the synthesis gas for a high conversion slurry fischer Tropsch process followed by a trailing wax cracking reactor. In this way high quality jet fuel can be directly produced. Recent experimental test data will be presented showing product quality. The life cycle analysis LCA will also be presented as well.

09 BIOMASS FUELS↗
DOE OSTI2024

Towards in-situ certification of additively manufactured parts: the vital roles of physics-based and data-driven models

Certifying additively manufactured (AM) parts in-situ at the completion of a build is an enticing prospect, as it can help reduce the high costs associated with post-build testing and evaluation. However, achieving this goal presents significant challenges that may keep it aspirational for the foreseeable future. Nonetheless, incremental progress can pave the way forward. A critical aspect of in-situ certification involves continuous quality checking due to the random nature of the AM process and the difficulties in detecting defects or anomalies once layers are built over. While real-time in-situ monitoring strategies assisted by machine learning (ML) play a pivotal role in auditing part quality, they must ideally be supported by real-time (or near real-time) adaptive process control enabled by ML-assisted decision-making. By analyzing in-situ monitoring data in real-time (or near real- time) to dynamically adjust manufacturing parameters, such intervention can ensure AM parts are built to meet stringent certification standards. This can be achieved virtually by using high-fidelity performance models for the physical testing and evaluation tasks. In this short editorial, we discuss the key contributions made by data-driven and physics-based models in providing intelligence to the monitoring and process control tasks underpinning in-situ certification and in the simulation of the build’s performance under test and service conditions. While our focus lies in metal AM, the concepts discussed here are also relevant to other AM processes.

: In-situ monitoring↗
DOE OSTI2024

Improved Manufacturability and Throughput of Ultra- Transparent, Super-Insulating Aerogels

AeroShield Materials produces a novel silica aerogel material with exemplary thermal performance and unprecedented optical clarity, offering the potential for super-insulating fenestration and insulated glass to reduce thermal losses in the built environment by billions of dollars every year. One of the most significant challenges facing AeroShield today is the total amount of time that is required to produce large monolithic aerogel samples. This overall process can require as much as 144 hours total, which can significantly hinder scale-up to an economically viable product. The purpose of this Phase 1 research was to continue development of our novel aerogel manufacturing process to reduce material processing time by up to 10x, greatly improving product throughput and reducing cost. AeroShield’s manufacturing process can be divided into 4 major stages, each with their own distinct set of parameters and time requirements. Under this Phase 1 award, AeroShield was able to identify and optimize a number of these parameters, including molding materials, solvent rinse conditions, critical point drying time, and annealing conditions. AeroShield also performed thorough analyses on how these optimized parameters affected important final characteristics of the gels, including optical clarity, thermal conductivity, and dimensional stability. This campaign culminated in the production of laboratory scale aerogel samples using significantly lower process times of both 36 and 18 total hours, which represent Phase 1 Target and Stretch goals. In order to achieve widespread market adoption, monolithic sheets of the aerogel material must be made to industry-standard sizes (8’ x 12’) at a cost that provides 5-7 year or less breakeven energy savings for consumers (<$2 sq/ft). The work performed under this Phase 1 award shows that time and materials required to make aerogel samples, which make up a significant portion of their overall cost, can be greatly reduced without sacrificing quality. AeroShield plans to use these optimized processes to produce larger, product-relevant sized aerogels in order to achieve target final material costs.

Wilke, Kyle↗
DOE OSTI2024

Multiple Pathways of Influence for Tightly and Loosely Structured Organizations: Implications for Systems Resilience

Organizations play a key role in supporting various societal functions, ranging from environmental governance to the manufacturing of goods. Here, the behaviors of organization are impacted by various influences, including information, technology, authority, economic leverage, historical experiences, and external factors, such as regulations. This paper introduces a generalized framework, focused on the relative structure of an organization (tight vs. loose), that can be used to understand how different influence pathways can impact decision-making within differently structured organizations. This generalized framework is then translated into a modeling and simulation platform to support and assess implications of these structural differences in resilience to disinformation (measured by organizational behaviors of timeliness and inclusion of quality information) using a systems dynamics approach Preliminary results indicate that a tightly structured organization may be less timely at processing information but could be more resilient against using poor quality information in organizational decisions compared to a loosely structured organization. Ongoing work is underway to understand the robustness of these findings and to validate current model design activities with empirical insights.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗
DOE OSTI2026

CoURAGE KAZR b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility supports atmospheric and earth system research through a comprehensive network of fixed and mobile observatories. These facilities provide long-term and intensive campaign-based observations of clouds, aerosols, precipitation, radiation, and meteorological state variables. ARM observations are designed to improve the physical understanding and numerical representation of atmospheric processes in earth system models, with particular emphasis on cloud-radiation interactions and precipitation processes. The Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) deploys one of the ARM Mobile Facilities (AMF) to the Mid-Atlantic region surrounding Baltimore, Maryland, for the period 1 December 2024 through 30 November 2025. This deployment focuses on characterizing atmospheric structure, cloud properties, and precipitation processes across strong land-use and surface heterogeneity gradients associated with urban, rural, and coastal (Chesapeake Bay) environments. The CoURAGE deployment complements the Baltimore Social-Environmental Collaborative (BSEC), a DOE Urban Integrated Field Laboratory (UIFL), by providing high-quality atmospheric observations needed to connect urban surface processes, emissions, and meteorology to cloud and precipitation responses. In addition to the central urban site, ancillary observing sites were deployed to rural Maryland northwest of Baltimore and to an island site in Chesapeake Bay. These measurements further complement a long-term atmospheric observatory operated in Beltsville, Maryland, by Howard University in collaboration with the Maryland Department of the Environment. Together, these assets form a four-node regional atmospheric observatory network representing Baltimore and its three primary surrounding environments—urban, rural, and coastal/bay. This coordinated observational strategy enables investigation of spatial gradients in boundary-layer structure, cloud occurrence, precipitation evolution, and aerosol-cloud interactions across complex surface regimes. Within this network, vertically pointing cloud radars play a critical role by providing continuous, high-resolution measurements of cloud and precipitation vertical structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Summer 2024 INL Intern Poster Session Submission - Brian Schumitz

This LRS submission is my poster for the INL Intern Poster Session, Summer 2024. Abstract: The Software Engineering and Cybersecurity Lab (SECL) at Montana State University has developed PIQUE, a system for evaluating software quality. PIQUE's adaptability allows for language-specific static-analysis operations, including a model for assessing cloud microservice ecosystems. These ecosystems often rely on Docker for efficient deployment and management of containerized services. Our research focuses on evaluating the network quality within these microservice ecosystems. To automate this process, we're utilizing Snort, an open-source intrusion detection system renowned for its ability to detect and log network traffic. By leveraging Snort's customizable rules, we aim to construct comprehensive testing methods for measuring and quantifying the network quality based on traffic between Docker containers. This research aims to enhance the overall security and reliability of cloud microservice ecosystems by providing automated and robust quality evaluation mechanisms, ultimately contributing to the advancement of software engineering practices in these environments

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Water quality data collected from the Muskegon River (MI, USA) using AquaBOT June-July 2024.

This dataset contains processed output from AquaBOT, which combines 30-second interval measurements of GPS location and YSI water quality parameters (temperature (degrees Celsius), dissolved oxygen (mg/l), specific conductance(microSiemens/cm at 25 degrees Celsius), and turbidity (NTU)), for 3 sections of the Muskegon River in Michigan, USA. The file aquabot2024_MuskegonRiver.csv has information on locations, dates and times, and observations. The data were processed by removing observations recorded before and after AquaBOT was in the water and observations where no data values were recorded. No other QA/QC was done. This research was performed as part of the DOE Research Development and Partnership Pilot award “Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region”, which aims to expand collaborations between researchers at Central Michigan University and U.S. Department of Energy labs and projects focused on enhancing climate resilience in Great Lakes communities and ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LHC EFT WG note: SMEFT predictions, event reweighting, and simulation

This note provides a comprehensive overview of tools for predicting observables in the Standard Model effective field theory (SMEFT) at both tree level and one loop using event generators. We evaluate three primary methodologies–event reweighting, separate simulation of squared matrix elements, and full SMEFT process simulation–focusing on their statistical performance, computational efficiency, and potential biases. Each approach is assessed in terms of its accuracy, highlighting trade-offs between precision and resource demands. Practical insights into their applicability for high-energy physics analyses are offered, with particular attention to processes where SMEFT effects are significant. Additionally, we discuss the role of helicity in reweighting strategies and its impact on the quality of predictions. By comparing the methods across various LHC processes, this note provides guidance for selecting the most effective strategy for various SMEFT studies, ensuring robust predictions while optimizing computational resources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Membrane Electrode Assembly Manufacturing Automation Technology for the Electrochemical Compression of Hydrogen: Cooperative Research and Development (Final Report)

Electrochemical compression has the possibility to outcompete mechanical compression for hydrogen end- use applications. While HyET has a compressor that can output JO kilograms (kg)/day (fully scalable from home-to-industrial application) at up to 700 bar, the energy demand and reliability require top-quality electrochemical hydrogen compression (EHC) membrane electrode assemblies (MEAs), preferably prepared by cost-effective high-capacity manufacturing. High pressure requires a special MEA design, deviating from typical proton exchange membrane fuel cell (PEMFC) MEAs with adapted catalyst layer substrates, asking for a modified coating process. The National Renewable Energy Laboratory (NREL) will help HyET by developing an automated catalyst coating process fit for EHC MEA manufacturing. In addition, inline quality inspection methods will be developed/selected to improve the MBA quality as it is used for EHC stack assembly. In a joint effort, NREL and HyET will even design an automated manufacturing process for the EHC MEA and approach potential United States (US) suppliers of manufacturing equipment.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Thermomechanical Degradation of Sintered Copper Under High-Temperature Thermal Shock

The need for reliable bonded interface materials is critical to realize the performance benefits of wide-bandgap devices in power electronics modules, especially in operating temperatures greater than 150 degrees C. In this paper, we investigate the thermomechanical performance of sintered copper (Cu) as a large-area attachment, bonded between Cu baseplates and active-metal-bonded substrates, under accelerated thermal shock (-40 degrees C to 200 degrees C) conditions. In the fabrication phase of the samples, we used different stencil patterns and found that the grid and stripe patterns resulted in a better outgassing of the residual organics during the sintering process, thereby ensuring a substantially improved bond quality compared to a full-area print. The paste consisted of Cu microflakes, and we performed sintering using a Budatec SP300 sintering press at 275 degrees C with 15 MPa of bonding pressure for 5 minutes in a nitrogen atmosphere. Under accelerated experiments, we monitored the degradation of the sintered Cu bond in the samples through C-mode scanning acoustic microscope (C-SAM) images. To quantify the defect percentage in C-SAM images, we investigated image denoising techniques to exclude the pattern prints. Finally, we cross-sectioned a sample and obtained scanning electron microscope images, which revealed adhesive fracture as the dominant failure mechanism.

ADVANCED PROPULSION SYSTEMS,INORGANIC, ORGANIC, PH↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions

The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed.

Agriculture↗
DOE OSTI2025

Results of an interlaboratory study on the working curve in vat photopolymerization II: Towards a standardized method

The working curve measurement in photopolymer additive manufacturing is a ubiquitous measure of the cure depth of a printing resin as a function of radiant exposure of light. The fit parameters from this measurement (the depth of light penetration D p and the critical exposure E c ) are used to evaluate and report a resin’s printability, optimize processing parameters, and inform print and resin quality control. Despite its widespread use, the working curve lacks a standard measurement method. Here, following up on our paper “Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization” from last year, an interlaboratory study on the working curve was performed using calibrated, reproducible, bandpass filtered light sources. With these light sources, the variability between labs in measured working curves was dramatically reduced from the initial interlaboratory study. Aggregate data from this experiment produced reliable D p and E c measurements at 385 nm of 39.2 ± 3.7 µm and 12.3 ± 3.0 mJ cm −2 , respectively. At 405 nm the values of D p and E c are 69.3 ± 3.8 µm and 17.9 ± 2.3 mJ cm −2 , respectively. The results are agnostic to the thickness measurement tool utilized by participants, ensuring broad applicability across laboratories. We also tested the generalizability of the proposed method of using a filtered light source by filtering a commercial 405 nm light source and obtaining a working curve in agreement with the aggregate data from the interlaboratory study. This interlaboratory study provides a basis for a documentary standard for the working curve, so that the entire photopolymer additive manufacturing industry can share reproducible and interoperable working curve data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A product data network to enable faster, easier, and better planning of building envelopes

The building envelopes contributes significantly to the energy-efficiency of the building. Building performance simulation has made it possible to compare façade technologies regarding energy demand, daylighting, thermal and visual comfort in detail. Planners, such as architects and engineers, need experience to find product data with the right quality and level of detail, and to process the data to fit the calculation and the application. In the available time, planners can compare only a limited number of products, which means that better solutions could go unnoticed. This paper presents a new concept for making product data easily accessible for building façade planning. The concept consists of a network of databases for the efficient exchange and use of optical and calorimetric data of glazing units, shading devices, and combinations of both. The paper presents the research questions, an analysis of the current challenges, six design goals for the product data network and its implementation together with a discussion. Many product data sources can be connected to many planning software applications via the specified application programming interface. When planning software connects to the product data network, the planning of building envelopes can be much faster because planners do not need to spend so much time to search and process product data manually. The planning of building envelopes can also become much easier, especially for planners with limited experience. They do not need to understand all the details about which data fits which calculation if the software company implements this. The planning of building envelopes can become much more reliable when software companies validate their use of the product data network, because the current manual process is prone to errors. The planning of building envelopes can also improve because more products can be compared in the available time, allowing better solutions to be found.

Maurer, Christoph↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗