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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Surface-Synthesized Graphene Nanoribbons for Room Temperature Switching Devices: Substrate Transfer and ex Situ Characterization

Recent progress in the on-surface synthesis of graphene nanoribbons (GNRs) has given access to atomically precise narrow GNRs with tunable electronic band gaps which makes them excellent candidates for room temperature switching devices such as field-effect transistors (FET). However, in spite of their exceptional properties, significant challenges remain for GNR processing and characterization. This contribution addresses some of the most important challenges, including GNR fabrication scalability, substrate transfer, long-term stability under ambient conditions, and ex situ characterization. We focus on 7- and 9-atom-wide armchair graphene nanoribbons (i.e., 7-AGNR and 9-AGNR) grown on 200 nm Au(111)/mica substrates using a high throughput system. Transfer of both 7- and 9-AGNRs from their Au growth substrate onto various target substrates for additional characterization is accomplished utilizing a polymer-free method that avoids residual contamination. This results in a homogeneous GNR film morphology with very few tears and wrinkles, as examined by atomic force microscopy. Raman spectroscopy indicates no significant degradation of GNR quality upon substrate transfer and reveals that GNRs have remarkable stability under ambient conditions over a 24 month period. The transferred GNRs are analyzed using multiwavelength Raman spectroscopy, which provides detailed insight into the wavelength dependence of the width-specific vibrational modes. Finally, we characterize the optical properties of 7- and 9-AGNRs via ultraviolet–visible (UV–vis) spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Online LIBS–ML Framework for Dynamic Characterization of Heterogeneous Waste-Derived Gasification Feedstocks

LIBS−ML framework for real time feedstock characterization during continuous conveyor transport Heterogeneous waste derived feedstocks (e.g., waste coal, biomass and blends) introduce rapid variability in heating value and ash chemistry that affect gasifier operation, yet conventional laboratory characterization techniques are too slow to support proactive control. To address this gap, this study reports on an online, in situ, dynamic characterization framework that couple’s laser-induced breakdown spectroscopy (LIBS) with leakage safe machine learning (ML) regression to deliver real time, decision quality predictions of gasifier relevant properties. A controlled sample matrix spanning two different waste coals, two different biomasses, and engineered blends under two particle size conditions were constructed and benchmarked using standardized laboratory analyses for proximate/ultimate properties and ash composition. LIBS spectra were acquired dynamically as material flowed on a conveyor belt, using high energy 1064 nm laser ablation and shot averaging to improve repeatability and precision. Supervised regression models (multi layer perceptron (MLP) /artificial neural network (ANN), random forest (RF), and support vector regression (SVR)) and an optimized weighted ensemble were trained on emission line feature sets using nested cross validation with Bayesian hyperparameter tuning and validated against an independent hold out set. The proposed LIBS−ML workflow achieves near laboratory predictive fidelity across parametric targets (including higher heating value (HHV), ash content, fixed carbon, sulfur, major ash forming oxides, and initial deformation temperature (IDT)), with the weighted ensemble providing a robust default predictor under dynamic measurement conditions. These results demonstrate a practical pathway for real time feedstock characterization that can enable feedforward adjustments and more resilient gasifier operation for variable quality waste derived fuels.

Biomass↗
DOE OSTI2020

ACS Sustainable Chemistry & Engineering Virtual Special Issue on Recent Advances in Biomass Characterization and Modeling

Recognizing the importance of biomass characterization and modeling, a virtual special issue (VSI) entitled Recent Advances in Biomass Characterization and Modeling was organized to showcase the recent contributions to this emerging field. This VSI features contributions from leading scientists in the biorefinery field, intending to provide the readers with the latest advances in analytical methodology and modeling to characterize biomass feedstocks and biomass-derived products. Further, the VSI contains perspectives and research articles. Advanced analytical methods that measure the mechanical, chemical, physical, biological, and other properties of biomass and biomass pyrolysis products are described along with their application’s limitations in two perspectives. Biomass is a multicomponent/multiscale, complex, nonconducting, and highly heterogeneous material; thus, it could be problematic for many characterization techniques. Traditional biomass analysis tools that are slow, laborious, and generally require harsh reagents have been replaced or supplemented by simpler and more rapid chromatographic and spectroscopic approaches such as GC-MS, HPLC, FTIR, Raman, and NMR spectroscopies.

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

Design and Characterization of a Transcriptional Repression Toolkit for Plants

Regulation of gene expression is essential for all life. Tools to manipulate the gene expression level have therefore proven to be very valuable in efforts to engineer biological systems. However, there are few well-characterized genetic parts that reduce gene expression in plants, commonly known as transcriptional repressors. We characterized the repression activity of a library consisting of repression motifs from approximately 25% of the members of the largest known family of repressors. Combining sequence information with our trans-regulatory function data, we next generated a library of synthetic transcriptional repression motifs with function predicted in advance. After characterizing our synthetic library, we demonstrated not only that many of our synthetic constructs were functional as repressors but also that our advance predictions of repression strength were better than random guesses. Finally, we assessed the functionality of known transcriptional repression motifs from a wide range of eukaryotes. Our study represents the largest plant repressor motif library experimentally characterized to date, providing unique opportunities for tuning transcription in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2023

Characterization of just one atom using synchrotron X-rays

Since the discovery of X-rays by Roentgen in 1895, its use has been ubiquitous, from medical and environmental applications to materials sciences. X-ray characterization requires a large number of atoms and reducing the material quantity is a long-standing goal. Here we show that X-rays can be used to characterize the elemental and chemical state of just one atom. Using a specialized tip as a detector, X-ray-excited currents generated from an iron and a terbium atom coordinated to organic ligands are detected. The fingerprints of a single atom, the L 2,3 and M 4,5 absorption edge signals for iron and terbium, respectively, are clearly observed in the X-ray absorption spectra. The chemical states of these atoms are characterized by means of near-edge X-ray absorption signals, in which X-ray-excited resonance tunnelling (X-ERT) is dominant for the iron atom. The X-ray signal can be sensed only when the tip is located directly above the atom in extreme proximity, which confirms atomically localized detection in the tunnelling regime. Finally, our work connects synchrotron X-rays with a quantum tunnelling process and opens future X-rays experiments for simultaneous characterizations of elemental and chemical properties of materials at the ultimate single-atom limit.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Extreme ultraviolet microscope characterization using photomask surface roughness

We demonstrate a method for characterizing the field-dependent aberrations of a full-field synchrotron-based extreme ultraviolet microscope. The statistical uniformity of the inherent, atomic-scale roughness of readily-available photomask blanks enables a self-calibrating computational procedure using images acquired under standard operation. We characterize the aberrations across a 30-um field-of-view, demonstrating a minimum aberration magnitude of smaller than λ/21 rms averaged over the center 5-um area, with a measurement accuracy better than λ/180 rms. The measured field variation of aberrations is consistent with system geometry and agrees with prior characterizations of the same system. In certain cases, it may be possible to additionally recover the illumination wavefront from the same images. Our method is general and is easily applied to coherent imaging systems with steerable illumination without requiring invasive hardware or custom test objects; hence, it provides substantial benefits when characterizing microscopes and high-resolution imaging systems in situ.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis

Reliable radiographic methods for characterizing nuclear waste packages non-destructively (without the need to open containers) have the potential to significantly contribute to safe handling and future disposal options, particularly for legacy waste of unknown content. Due to required shielding of waste containers and the need to characterize materials consisting of light elements, X-ray methods are not suitable. Here, energy-resolved MeV neutron radiography is demonstrated as a first-of-its-kind application for non-destructive and remote examination of mock up nuclear waste packages from a safe position using time-of-flight techniques enabled by a novel event-mode imaging detector system. Energy-resolved neutron transmission spectra were measured spatially, permitting the detection of analogue materials to actual nuclear waste such as water, melamine, and ion exchange resin within a 2.54 cm wall thickness steel pipe. The results demonstrate the capability to locate the materials through this wall thickness by radiography and tomographic reconstruction, revealing detailed 3D distributions and structural anomalies. The method effectively detects residual water in ion exchange resin, highlighting its sensitivity to moisture content, a crucial parameter for nuclear waste characterization. Monte Carlo simulations are in agreement with the experimental findings, providing a pathway to simulate waste forms more difficult to tackle experimentally. This work paves the way to apply sub-nanosecond intense MeV neutron sources, such as laser-driven neutron sources under development, to nuclear waste characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Status and prospect of in situ and operando characterization of solid-state batteries

Electrification of the transportation sector relies on radical re-imagining of energy storage technologies to provide affordable, high energy density, durable and safe systems. Next generation energy storage systems will need to leverage high energy density anodes and high voltage cathodes to achieve the required performance metrics (longer vehicle range, long life, production costs, safety). Solid-state batteries (SSBs) are promising materials technology for achieving these metrics by enabling these electrode systems due to the underlying material properties of the solid electrolyte (viz. mechanical strength, electrochemical stability, ionic conductivity). Electro-chemo-mechanical degradation in SSBs detrimentally impact the Coulombic efficiencies, capacity retention, durability and safety in SSBs restricting their practical implementation. Solid|solid interfaces in SSBs are hot-spots of dynamics that contribute to the degradation of SSBs. Characterizing and understanding the processes at the solid|solid interfaces in SSBs is crucial towards designing of resilient, durable, high energy density SSBs. This work provides a comprehensive and critical summary of the SSB characterization with a focus on in situ and operando studies. Additionally, perspectives on experimental design, emerging characterization techniques and data analysis methods are provided. Furthermore, this work provides a thorough analysis of current status of SSB characterization as well as highlights important avenues for future work.

25 ENERGY STORAGE↗
DOE OSTI2025

Thermal decomposition of neptunyl ammonium nitrate: mechanistic insights and structural characterization of the Np 2 O 5 intermediate phase

Neptunium (Np) possesses a rich and unique chemistry that often diverges from other actinide elements yet remains relatively underexplored compared with the other light actinides. A resurgence of interest in Np has been spurred by the application of 237 Np for plutonium-238 ( 238 Pu) production for use in radioisotope thermoelectric generators (RTGs), necessitating evaluation of Np chemical reactions and materials. The work presented here studied the thermal decomposition of neptunyl ammonium nitrate (NH 4 Np VI O 2 (NO 3 ) 3 ) for synthesis of neptunium dioxide (NpO 2 ), which is the target material used for production of 238 Pu. Additionally, structural characterization of the intermediate solid Np pentoxide (Np 2 O 5 ) was performed. Advanced solid-state characterization techniques, including simultaneous thermal analysis (STA), powder X-ray diffraction (pXRD), Raman spectroscopy, and density functional theory (DFT) modeling have been combined to study the reaction pathways. Analysis revealed that NH 4 Np VI O 2 (NO 3 ) 3 thermally decomposes to a proposed neptunyl nitrate intermediate, followed by Np 2 O 5 and finally NpO 2 , all within the temperature range of 150 °C–600 °C. Further characterization of the pentoxide intermediate provided the first Raman spectra of pure-phase Np 2 O 5 and associated DFT modeling confirmed Raman peak assignments for this phase. These findings provide mechanistic information to advance production of the critical radioisotope 238 Pu and advance the state of knowledge on Np materials chemistry using modern characterization techniques.

Lawson, Kathryn M. [Oak Ridge National Laboratory ↗
DOE OSTI2024

Characterization of the upgraded single-line-of-sight time-resolved x-ray imager using short-pulse visible and UV lasers

The single-line-of-sight time-resolved x-ray imager (SLOS-TRXI), a fast-gated x-ray imager used for capturing x-ray self-emission in inertial confinement fusion experiments on OMEGA, has been upgraded and characterized. SLOS-TRXI combines an electron-dilation imager and a hybrid complementary metal–oxide–semiconductor (hCMOS) sensor to capture multiple gated frames on a single line of sight with a temporal resolution of ~40 ps and a spatial resolution of 10 µm. The original hCMOS sensor with four frames was replaced with a newer-generation hCMOS sensor having eight frames. Gate characterizations of both the sensor and the entire SLOS-TRXI diagnostic were performed using ~10-ps FWHM visible (2ω) and UV (4ω) short-pulse lasers, respectively. A stepped echelon was used to generate a train of five UV laser pulses having an interpulse separation of 30 ± 3 ps. Characterization results of the hCMOS gating (2.28 ± 0.02-ns FWHM on average) and a temporal resolution of the upgraded SLOS-TRXI (34 ± 4-ps FWHM on average) are presented. A temporal magnification for the electron-dilation imager between 40 and 60 was inferred from the characterization results. Furthermore, the spatial resolution of the upgraded SLOS-TRXI remains the same in light of this work.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Quantum process identification: a method for characterizing non-markovian quantum dynamics

Established methods for characterizing quantum information processes do not capture non-Markovian (history-dependent) behaviors that occur in real systems. These methods model a quantum process as a fixed map on the state space of a predefined system of interest. Such a map averages over the system's environment, which may retain some effect of its past interactions with the system and thus have a history-dependent influence on the system. Although the theory of non-Markovian quantum dynamics is currently an active area of research, a systematic characterization method based on a general representation of non-Markovian dynamics has been lacking. In this article we present a systematic method for experimentally characterizing the dynamics of open quantum systems. Our method, which we call quantum process identification (QPI), is based on a general theoretical framework which relates the (non-Markovian) evolution of a system over an extended period of time to a time-local (Markovian) process involving the system and an effective environment. In practical terms, QPI uses time-resolved tomographic measurements of a quantum system to construct a dynamical model with as many dynamical variables as are necessary to reproduce the evolution of the system. Through numerical simulations, we demonstrate that QPI can be used to characterize qubit operations with non-Markovian errors arising from realistic dynamics including control drift, coherent leakage, and coherent interaction with material impurities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

High-throughput fabrication and semi-automated characterization of oxide thin film transistors

High throughput experimental methods are known to accelerate the rate of research, development, and deployment of electronic materials. For example, thin films with lateral gradients in composition, thickness, or other parameters have been used alongside spatiallyresolved characterization to assess how various physical factors affect material properties under varying measurement conditions. Similarly, multi-layer electronic devices that contain such graded thin films as one or more of their layers can also be characterized spatially in order to optimize the performance. In this work, we apply these high throughput experimental methods to thin film transistors (TFTs), demonstrating combinatorial device fabrication and semiautomated characterization using sputtered oxide TFTs as a case study. Here, we show that both extrinsic and intrinsic types of device gradients can be generated in a TFT library, such as channel thickness and length, channel cation compositions, and oxygen atmosphere during deposition. We also present a semi-automated method to measure the 44 devices fabricated on a 50x50mm substrate that can help to identify properly functioning TFTs in the library and finish the measurement in a short time. Finally, we propose a fully automated characterization system for similar TFT libraries, which can be coupled with high throughput data analysis. These results demonstrate that high throughput methods can accelerate the investigation of TFTs and other electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗
DOE OSTI2020

Optical high-order harmonic generation as a structural characterization tool

Structural characterization is essential to material engineering, but few tools can detect structural properties in the time domain. High harmonic generation (HHG) emerges as a new frontier that touches the heart of condensed-matter physics from the symmetry to quantum geometrical nature of electrons, but its capability in structural characterization has not been materialized. Here, we establish a crucial connection between the symmetry of a material and the helicity of light. We employ monolayer MoS 2 as an example. In this work, we show that a linearly polarized laser pulse used in experiments is not ideal for structural characterization because it only generates in-plane anisotropy. It is the circularly polarized laser field that is capable of producing four distinctive HHG signals from the four phases of MoS 2 . This finally links the laser helicity to the crystal structure. The results are generic and are not affected by the Berry curvature, the interband or intraband contribution. Our study unleashes the power of HHG as a structural characterization tool for technologically important materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Characterization of Nanoscale Pores in Tight Gas Sandstones Using Complex Techniques: A Case Study of a Linxing Tight Gas Sandstone Reservoir

Pore structures with rich nanopores and permeability in tight gas reservoirs are poorly understood up to date. Advanced techniques are needed to be employed to accurately characterize pore structures, especially tiny pores which include micron and nanopores. In this study, various experimental techniques such as scanning electron microscopy (SEM), nuclear magnetic resonance (NMR) T 2 , nitrogen adsorption method, and NMR cryoporometry (NMRC) are combined to interrogate the complex pore systems of the tight gas reservoir in the Linxing formation, Ordos Basin, China. Results show that tight gas sandstones are primarily comprised of residual interparticle and clay-dominated pores. Clay and quartz are two dominate minerals while pyrite occupies a nontrivial amount as well. The permeability of tight gas sandstones is very low, exhibiting an extremely poor positive correlation with porosity. While pore types and relative pore contents are more influential factors on the permeability, accurate characterization of pore size distribution is critical for the permeability of tight gas sandstones. Therefore, complementary characterization methods are carried out, indicating that neither small pores with radii < 100 nm (around peak 1 in NMR T 2 distribution) nor large pores with radii > 5 μ m (around peak 3 in NMR T 2 distribution) control the permeability by analyzing the connectivity of the pores in various size ranges, but rather pores averaging approximately 350 ± X nm (around peak 2 in NMR T 2 distribution) have sufficient connectivity to host and transmit hydrocarbons. The pore size of tight gas sandstones is dominated by the clay-rich mineral assemblage. The study shows that the NMRC technique can be a very promising method, especially when referred to as a promising “roadmap” on how to interrogate tight formations such as the tight gas sands or even shale especially for the nanopore characterization.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Prediction and characterization of promoters and ribosomal binding sites of Zymomonas mobilis in system biology era

Zymomonas mobilis is a model bacterial ethanologen with many systems biology studies reported. Besides lignocellulosic ethanol production, Z. mobilis has been developed as a platform for biochemical production through metabolic engineering. However, identification and rigorous understanding of the genetic origins of cellular function, especially those based in non-coding region of DNA, such as promoters and ribosomal binding sites (RBSs), are still in its infancy. This knowledge is crucial for the effective application of Z. mobilis to new industrial applications of biotechnology for fuels and chemicals production. In this study, we explored the possibility to systematically predict the strength of promoters based on systems biology datasets. The promoter strength was clustered based on the expression values of downstream genes (or proteins) from systems biology studies including microarray, RNA-Seq and proteomics. Candidate promoters with different strengths were selected for further characterization, which include 19 strong, nine medium, and ten weak ones. A dual reporter-gene system was developed which included appropriate reporter genes. These are the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and EGFP reporter gene driven by candidate promoters for quantification. This dual reporter-gene system was confirmed using the inducible promoter, Ptet, which was used to determine the strength of these predicted promoters with different strengths. In addition, the dual reporter-gene system was applied to determine four synthetic RBSs with different translation initiation rates based on the prediction from bioinformatics server RBS calculator. Our results showed that the correlations between the prediction and experimental results for the promoter and RBS strength are relatively high, with R2 values more than 0.7 and 0.9, respectively. This study not only identified and characterized 38 promoters and four RBSs with different strengths for future metabolic engineering in Z. mobilis, but also established a flow cytometry-based dual reporter-gene system to characterize genetic elements including, but not limited to the promoters and RBSs studied in this work. This study also suggested the feasibility of predicting and selecting candidate genetic elements based on omics datasets and bioinformatics tools. Moreover, the dual reporter-gene system developed in this study can be utilized to characterize other genetic elements of Z. mobilis, which can also be applied to other microorganisms.

09 BIOMASS FUELS↗
DOE OSTI2018

Advancements in Life Cycle Human Exposure and Toxicity Characterization

The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA). The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose–response modeling. The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose–response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose–response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential. Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks.

54 ENVIRONMENTAL SCIENCES↗