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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Diagnostic, Prognostic, and Therapeutic Use of Radiopharmaceuticals in the Context of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) outbreak has devastated the healthcare systems and economies of over 200 countries in just a few months. The etiological agent of COVID-19, SARS-CoV-2, is a highly contagious virus that can be transmitted by asymptomatic and symptomatic carriers alike. While in vitro testing techniques have allowed for population-wide screening, prognostic tools are required to assess the disease severity and therapeutic response, contributing to improve the patient clinical outcomes. Moreover, no specific antiviral against COVID-19 exists at the time of publication, severely limiting treatment against the infection. Hence, there is an urgent clinical need for innovative therapeutic strategies that may contribute to manage the COVID-19 outbreak and prevent future pandemics. In this work, we critically examine recent diagnostic, prognostic, and therapeutic advancements for COVID-19 in the field of radiopharmaceuticals. First, we summarize the gold standard techniques used to diagnose COVID-19, including in vitro assays and imaging techniques, and then discuss how radionuclide-based nuclear imaging provides complementary information for prognosis and treatment management of infected patients. Second, we introduce new emerging types of radiotherapies that employ radioimmunoconjugates, which have shown selective cytotoxic response in oncological studies, and critically analyze how these compounds could be used as therapeutic agents against SARS-CoV-2. Finally, this Perspective further discusses the emerging applications of radionuclides to study the behavior of pulmonary SARS-CoV-2 aerosol particles.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Polyalkenamers as Drop-In Additives for Ring-Opening Metathesis Polymerization: A Promising Upcycling Paradigm

Here we report a distinct strategy to upcycle waste polyalkenamers such as polybutadiene into new, performance-advantaged materials by using them as drop-in additives for ring-opening metathesis polymerization (ROMP). The polyalkenamers serve as competent chain-transfer agents in ROMPs of common classes of cyclic olefin monomers, facilitating good molecular weight control, allowing low Ru catalyst loadings, and enabling efficient incorporation of the polyalkenamer into the synthesized polymeric material. We successfully demonstrate ROMP using model polyalkenamers and translate these learnings to leverage commercial polybutadiene and acrylonitrile butadiene styrene (ABS) as chain transfer agents for ROMP copolymerizations. Critically, our strategy is shown to be highly efficient and operationally simple, quantitatively incorporating the polyalkenamer and inheriting aspects of its thermomechanical performance. Our results highlight a promising pathway for the upcycling of polyalkenamers and provide an alternative to existing deconstruction and functional upcycling strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Applying Multimodal Mass Spectrometry to Image Tumors Undergoing Ferroptosis Following In Vivo Treatment with a Ferroptosis Inducer

Epithelial ovarian cancer (EOC) is the most common form of ovarian cancer. The poor prognosis generally associated with this disease has led to the search for improved therapies such as ferroptosis-inducing agents. Ferroptosis is a form of regulated cell death that is dependent on iron and is characterized by lipid peroxidation. Precise mapping of lipids and iron within tumors exposed to ferroptosis-inducing agents may provide insight into processes of ferroptosis in vivo and ultimately assist in the optimal deployment of ferroptosis inducers in cancer therapy. In this work, we present a method for combining matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) with secondary ion mass spectrometry (SIMS) to analyze changes in spatial lipidomics and metal composition, respectively, in ovarian tumors following exposure to a ferroptosis inducer. Tumors were obtained by injecting human ovarian cancer tumor-initiating cells into mice, followed by treatment with the ferroptosis inducer erastin. SIMS imaging detected iron accumulation in the tumor tissue, and sequential MALDI-MS imaging of the same tissue section displayed two chemically distinct regions of lipids. Additionally, one region was associated with the iron-rich area detected with SIMS, and the other region encompassed the remainder of the tissue section. Bulk lipidomics confirmed the lipid assignments putatively assigned from the MALDI-MS data. Overall, we demonstrate the ability of multimodal MSI to identify the spatial locations of iron and lipids in the same tissue section and associate these regions with clinical pathology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Controlled Synthesis of SnO 2 Nanocrystals with Tunable Band Gaps

Tin(IV) oxide nanocrystals (SnO 2 NCs) have significant potential in various applications, with their performance closely related to their band gap. The band gap is influenced by the size and shape of the NCs, which can be precisely controlled by adjusting reaction conditions. In this study, we present deliberately designed synthesis protocols to produce high-quality SnO 2 NCs with tunable band gaps using different methods. Key factors affecting the synthesis include control of the oxidizing agent, reaction temperature, solvent selection, and reaction time optimization. The resulting NCs were characterized by using TEM, XRD, XPS, and optical spectroscopy. Notably, SnO 2 NCs synthesized by controlling the oxidizing agent (air injection) in a hot organic solution were smaller in size and exhibited abundant oxygen vacancies. In contrast, extending the reaction time or using ethanol as a solvent in hydrothermal systems facilitated larger spherical or rod-like SnO 2 NCs with fewer oxygen vacancies. Further analysis of the band gap and valence band maximum energy revealed that the abundant vacancies in SnO 2 NCs synthesized with the air-controlled hot organic solution method resulted in a narrower band gap and an upshifted valence band. These synthetic strategies illustrate the potential for deliberately designing SnO 2 NCs with optimized electronic structures for various applications.

band gap↗
DOE OSTI2024

Adjustments to Climate Perturbations—Mechanisms, Implications, Observational Constraints

Since the 5th Assessment Report of the Intergovernmental Panel on Climate Change (AR5) an extended concept of the energetic analysis of climate change including forcings, feedbacks and adjustment processes has become widely adopted. Adjustments are defined as processes that occur in response to the introduction of a climate forcing agent, but that are independent of global-mean surface temperature changes. Most considered are the adjustments that impact the Earth energy budget and strengthen or weaken the instantaneous radiative forcing due to the forcing agent. Some adjustment mechanisms also impact other aspects of climate not related to the Earth radiation budget. Since AR5 and a following description by Sherwood et al., much research on adjustments has been performed and is reviewed here. We classify the adjustment mechanisms into six main categories, and discuss methods of quantifying these adjustments in terms of their potentials, shortcomings and practicality. We furthermore describe aspects of adjustments that act beyond the energetic framework, and we propose new ideas to observe adjustments or to make use of observations to constrain their representation in models. Altogether, the problem of adjustments is now on a robust scientific footing, and better quantification and observational constraint is possible. This allows for improvements in understanding and quantifying climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Systematic discovery of pseudomonad genetic factors involved in sensitivity to tailocins

Tailocins are bactericidal protein complexes produced by a wide variety of bacteria that kill closely related strains and may play a role in microbial community structure. Thanks to their high specificity, tailocins have been proposed as precision antibacterial agents for therapeutic applications. Compared to tailed phages, with whom they share an evolutionary and morphological relationship, bacterially produced tailocins kill their host upon production but producing strains display resistance to self-intoxication. Though lipopolysaccharide (LPS) has been shown to act as a receptor for tailocins, the breadth of factors involved in tailocin sensitivity, and the mechanisms behind resistance to self-intoxication, remain unclear. Here, we employed genome-wide screens in four non-model pseudomonads to identify mutants with altered fitness in the presence of tailocins produced by closely related pseudomonads. Our mutant screens identified O-antigen composition and display as most important in defining sensitivity to our tailocins. In addition, the screens suggest LPS thinning as a mechanism by which resistant strains can become more sensitive to tailocins. Furthermore, we validate many of these novel findings, and extend these observations of tailocin sensitivity to 130 genome-sequenced pseudomonads. This work offers insights into tailocin–bacteria interactions, informing the potential use of tailocins in microbiome manipulation and antibacterial therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Learning emergent partial differential equations in a learned emergent space

We propose an approach to learn effective evolution equations for large systems of interacting agents. This is demonstrated on two examples, a well-studied system of coupled normal form oscillators and a biologically motivated example of coupled Hodgkin-Huxley-like neurons. For such types of systems there is no obvious space coordinate in which to learn effective evolution laws in the form of partial differential equations. In our approach, we accomplish this by learning embedding coordinates from the time series data of the system using manifold learning as a first step. In these emergent coordinates, we then show how one can learn effective partial differential equations, using neural networks, that do not only reproduce the dynamics of the oscillator ensemble, but also capture the collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an emergent PDE description of the dynamics in this parametrization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-scale organization in communicating active matter

The emergence of collective motion among interacting, self-propelled agents is a central paradigm in non-equilibrium physics. Examples of such active matter range from swimming bacteria and cytoskeletal motility assays to synthetic self-propelled colloids and swarming microrobots. Remarkably, the aggregation capabilities of many of these systems rely on a theme as fundamental as it is ubiquitous in nature: communication. Despite its eminent importance, the role of communication in the collective organization of active systems is not yet fully understood. Here we report on the multi-scale self-organization of interacting self-propelled agents that locally process information transmitted by chemical signals. We show that this communication capacity dramatically expands their ability to form complex structures, allowing them to self-organize through a series of collective dynamical states at multiple hierarchical levels. Our findings provide insights into the role of self-sustained signal processing for self-organization in biological systems and open routes to applications using chemically driven colloids or microrobots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A giant virus infecting the amoeboflagellate Naegleria

Giant viruses (Nucleocytoviricota) are significant lethality agents of various eukaryotic hosts. Although metagenomics indicates their ubiquitous distribution, available giant virus isolates are restricted to a very small number of protist and algal hosts. Here we report on the first viral isolate that replicates in the amoeboflagellate Naegleria. This genus comprises the notorious human pathogen Naegleria fowleri, the causative agent of the rare but fatal primary amoebic meningoencephalitis. We have elucidated the structure and infection cycle of this giant virus, Catovirus naegleriensis (a.k.a. Naegleriavirus, NiV), and show its unique adaptations to its Naegleria host using fluorescence in situ hybridization, electron microscopy, genomics, and proteomics. Naegleriavirus is only the fourth isolate of the highly diverse subfamily Klosneuvirinae, and like its relatives the NiV genome contains a large number of translation genes, but lacks transfer RNAs (tRNAs). NiV has acquired genes from its Naegleria host, which code for heat shock proteins and apoptosis inhibiting factors, presumably for host interactions. Notably, NiV infection was lethal to all Naegleria species tested, including the human pathogen N. fowleri. This study expands our experimental framework for investigating giant viruses and may help to better understand the basic biology of the human pathogen N. fowleri.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Extensive hydrogen incorporation is not necessary for superconductivity in topotactically reduced nickelates

Abstract A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phaseviaa topotactic reaction with a strongly reducing agent such as CaH 2 . Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La 1−x X x NiO 2 / SrTiO 3 (X= Ca and Sr) and Nd 6 Ni 5 O 12 / NdGaO 3 films, along with non-superconducting NdNiO 2 / SrTiO 3 and (Nd,Sr)NiO 2 / SrTiO 3 . We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that extensive hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates.

Science & Technology - Other Topics↗
DOE OSTI2021

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unconventional colloidal aggregation in chiral bacterial baths

Abstract When in equilibrium, thermal forces agitate molecules, which then diffuse, collide and bind to form materials. However, the space of accessible structures in which micron-scale particles can be organized by thermal forces is limited, owing to the slow dynamics and metastable states. Active agents in a passive fluid generate forces and flows, forming a bath with active fluctuations. Two unanswered questions are whether those active agents can drive the assembly of passive components into unconventional states and which material properties they will exhibit. Here we show that passive, sticky beads immersed in a bath of swimmingEscherichia colibacteria aggregate into unconventional clusters and gels that are controlled by the activity of the bath. We observe a slow but persistent rotation of the aggregates that originates in the chirality of theE. coliflagella and directs aggregation into structures that are not accessible thermally. We elucidate the aggregation mechanism with a numerical model of spinning, sticky beads and reproduce quantitatively the experimental results. We show that internal activity controls the phase diagram and the structure of the aggregates. Overall, our results highlight the promising role of active baths in designing the structural and mechanical properties of materials with unconventional phases.

Physics↗
DOE OSTI2020

Metabolic Profiling of Volatile Organic Compounds (VOCs) Emitted by the Pathogens Francisella tularensis and Bacillus anthracis in Liquid Culture

We conducted comprehensive (untargeted) metabolic profiling of volatile organic compounds (VOCs) emitted in culture by bacterial taxa Francisella tularensis (F. tularensis) subspecies novicida and Bacillus anthracis (B. anthracis) Sterne, surrogates for potential bacterial bioterrorism agents, as well as selective measurements of VOCs from their fully virulent counterparts, F. tularensis subspecies tularensis strain SCHU S4 and B. anthracis Ames. F. tularensis and B. anthracis were grown in liquid broth for time periods that covered logarithmic growth, stationary, and decline phases. VOCs emitted over the course of the growth phases were collected from the headspace above the cultures using solid phase microextraction (SPME) and were analyzed using gas chromatography-mass spectrometry (GC-MS). We developed criteria for distinguishing VOCs originating from bacteria versus background VOCs (originating from growth media only controls or sampling devices). Analyses of collected VOCs revealed methyl ketones, alcohols, esters, carboxylic acids, and nitrogen- and sulfur-containing compounds that were present in the bacterial cultures and absent (or present at only low abundance) in control samples indicating that these compounds originated from the bacteria. Distinct VOC profiles where observed for F. tularensis when compared with B. anthracis while the observed profiles of each of the two F. tularensis and B. anthracis strains exhibited some similarities. Furthermore, the relative abundance of VOCs was influenced by bacterial growth phase. These data illustrate the potential for VOC profiles to distinguish pathogens at the genus and species-level and to discriminate bacterial growth phases. The determination of VOC profiles lays the groundwork for non-invasive probes of bacterial metabolism and offers prospects for detection of microbe-specific VOC biomarkers from two potential biowarfare agents.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Adsorptive behavior of poly (vinylidene fluoride) membranes for the recovery of lignin-derived hydrophobic deep eutectic solvents

Abstract Recently, membrane technology has gained significant traction as an energy-efficient alternative to traditional thermal processes for solvent recovery. Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional organic solvents, yet a systematic methodology for selecting compatible membrane materials for their recovery remains underdeveloped. This study established a predictive framework for membrane material selection in hydrophobic DES applications using Hansen Solubility Parameters (HSP) with inverted criteria targeting materials with relative energy difference (RED) values greater than 1.0. Flat sheet membranes were fabricated via the non-solvent induced phase separation (NIPS) technique. Four NIPS fabricated polymer membranes were evaluated: polysulfone, cellulose acetate, polyvinylidene fluoride (PVDF) fabricated with polyethylene glycol (PEG) as a pore-forming agent, and polybenzimidazole (PBI). The HSP approach successfully predicted membrane-solvent compatibility, with polysulfone (RED = 0.6) and cellulose acetate (RED = 0.9) dissolving completely within 24 h, while PVDF (RED = 1.9) and PBI (RED = 1.1) maintained structural integrity throughout a 7-day exposure period. Furthermore, PVDF demonstrated superior performance with minimal weight gain (3.0%), hydrophobic surface characteristics (122° water contact angle), and enhanced mechanical properties following DES exposure. Comprehensive chemical and morphological characterization confirmed PVDF’s chemical stability and revealed a surface-selective interaction mechanism involving simultaneous PEG (pore-forming agent) extraction and DES component adsorption. Adsorption kinetics followed pseudo-first-order behavior with reversible characteristics, best described by the Temkin isotherm model (R² = 0.9987). PVDF membranes-maintained separation functionality with average lignin rejection (75.2 ± 7.69%) and demonstrated filtration permeability of 2.0 ± 0.34 LMH/bar. This methodology provides a rational approach for membrane selection in emerging solvent systems, contributing to the advancement of sustainable separation technologies for DES-based biomass processing applications.

Science & Technology - Other Topics↗
DOE OSTI2025

Esterification of 3-quinuclidinol, a marker for the incapacitant BZ, for analysis by EI-GC-MS in OPCW test matrices

The analysis by EI-GC-MS of 3-quinuclidinol (3Q), a marker for the riot control and incapacitating agent quinuclinidyl benzilate (BZ), using several, new acylation strategies is described. After evaluating and optimizing conditions for the acylations, these were tested on their ability to successfully derivatize 3Q in three different matrices featured in the 44th Organisation for the Prohibition of Chemical Weapons (OPCW) proficiency test (PT). As 3Q is a highly polar compound, the work here describes acylation strategies that generate 3Q analogs with superior gas chromatographic profiles relative to the underivatized material. The acyl groups studied in this work included acetyl, benzoyl, pentafluorobenzoyl and the bis(3,5-trifluoromethyl)benzoyl. The acylated 3Q products provide sharp, detectable peaks with significantly different retention times from that of the unmodified 3Q. Thus, the retention times for acetyl-, benzoyl-, pentafluorobenzoyl- and 3,5-bis(trifluoromethyl)benzoyl-3Q were determined to be 15.9, 24.9, 22.8 and 22.1 min respectively, in stark contrast to the unmodified 3Q which provides a sharp peak with a retention time centered at ~ 13.5 min only at high concentrations (> 10 µg/mL or µg/g) while a broad peak (RT ~ 15–15.5 min) at low concentrations (< 5 µg/mL or µg/g). The developed protocol was used to derivatize 3Q in three separate matrices. The first two matrices were liquid samples featured during the 44th OPCW PT at two separate concentrations in each (5 and 50 µg/mL). The last matrix was a soil sample featured in the same 44th OPCW PT wherein the 3Q had been spiked at a 12 µg/g concentration. The approach involves the extraction of 3Q from all matrices followed by its acylation resulting in a second reportable analytical method for an OPCW PT. The presented derivatization strategies should find wide applicability particularly in laboratories involved with the analysis of this chemical weapon agent degradation product and those participating in the yearly OPCW PTs.

3-quinuclidinol↗
DOE OSTI2022

Machine-learning for designing nanoarchitectured materials by dealloying

Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Controlled electrochemical nutrient delivery to enhance marine primary productivity

Oceanic photosynthesis contributes to approximately half of the Earth’s net annual primary productivity. Marine photosynthetic productivity has a high degree of heterogeneity due to spatial and temporal co-limitations of light, temperature, and/or nutrients. Across coastal, near-shore, and open ocean regions, insufficient concentrations of key nutrients (e.g., N, P, Fe) can limit primary productivity. Although studies have shown a significant increase in primary productivity with the addition of low doses of trace nutrients, a sustainable approach to reliably deliver and maintain low doses of nutrients and ensure their bioavailability, remains a challenge. Chemical nutrient addition has relied on the use of chelating agents to ensure nutrient bioavailability, but synthetic chelators are persistent environmental pollutants. In this study, we demonstrate for the first time the use of a controlled electrochemical nutrient delivery (CEND) approach to accelerate the growth of phytoplankton without the need for such chelators. Our study uses commercial stainless-steel electrodes to deliver low concentrations of iron to enhance growth rates in the microalga Picochlorum celeri TG2. To demonstrate the process control offered by the CEND method, we evaluate iron delivery as a function of pulse time, pulse frequency, and rest time between pulses. Our data show that at the same total Fe dose of 163 ppb, electrochemical iron delivery can achieve 9.54 ± 0.58 mg biomass/µg Fe, which is comparable to 9.14 ± 0.17 mg biomass/µg Fe achieved with chemical iron additions that include the synthetic chelating agent ethylenediaminetetraacetic acid (EDTA). Further, when different total iron doses (163 ppb, 325 ppb, and 650 ppb) were delivered over 72 h using CEND, biomass yield per iron dose was higher at lower doses: 9.54 ± 0.58 mg biomass/µg Fe at 163 ppb vs. 4.32 ± 0.32 mg biomass/µg Fe at 650 ppb. This highlights the benefits of CEND in delivering frequent and low doses of nutrients for improved process efficiency. Preliminary assessments show both lower cost and reduced greenhouse gas emissions from electrochemical over chemical iron additions with EDTA. The CEND approach opens new pathways to enhance marine primary productivity, without the unintended environmental impacts of synthetic chelators.

analytical chemistry↗