Estimating the number of pure chemical components in a mixture by X-ray absorption spectroscopy

Automated Mixture Analysis via Structural Evaluation

The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (≥97%) in an efficient manner.

Relevance of the Iron Distribution in Natural Smectite Clays for the Thermal Stability of PMMA-Clay Nanocomposites

Polymer-clay nanocomposites have greater thermal stability compared to the pristine polymer matrix. This can be attributed to the physical barrier provided by the inclusion of 2D clay nanoparticles (especially of the smectite group), together with radical trapping related to the distribution of specific 3d atoms in the inorganic phase. To elucidate the relevance of the Fe3+ distribution in this synergic effect, the iron atoms present in octahedral sheets of natural nontronite clay (Non, 5.6 wt % Fe) or in maghemite (M) nanoparticles (γ-Fe2O3) were incorporated in a poly(methyl methacrylate) (PMMA) matrix. Na-laponite (Lap) clay was used to evaluate the contribution of the diffusion barrier effect to the increased thermal stability of a PMMA-Lap nanocomposite, as evidenced by the upshift of the thermogravimetric (TGA) curve compared to that for PMMA. The contribution of radical trapping to the thermal stability of the PMMA-Non nanocomposite was evidenced by a significant shift of the Fe K-edge rising edge position by -4.5 eV after iron reduction by heating in N2, while similar treatment of pristine nontronite did not lead to a significant rising edge shift in the X-ray absorption spectra (XAS). This downshift demonstrated the reduction of Fe3+ to Fe0, induced by the sequestration of radicals formed by PMMA depolymerization. Raman spectroscopy analysis evidenced the formation of graphitic char deposits above 400 °C, further improving the thermal stability of PMMA-Non by providing an additional physical barrier to mass transport. A fourth contribution of well-dispersed iron was the abstraction of carbon from the char by the iron carburization reaction, which hindered CO2 formation by oxidative coking. In contrast, no relevant contribution of graphitic layer deposition was observed for the PMMA-M-Lap nanocomposite, where its improved thermal stability was only due to the combined contributions of the gas diffusion barrier effect and radical trapping by iron atoms. The maghemite effectively captured the radicals confined by the clay sheets, resulting in significant stabilization of the nanocomposite, with a shift of the mass loss of the PMMA-M-Lap nanocomposite compared to PMMA-Lap.

Revealing the structure of the active sites for the electrocatalytic CO2 reduction to CO over Co single atom catalysts using operando XANES and machine learning

Transition-metal nitrogen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co-N-C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co-N-C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co-N-C catalysts, and further optimization of this class of electrocatalytic systems.

Zinc speciation in highly weathered tropical soils affected by large scale vegetable production

Agriculture in highly weathered tropical soils often requires considerable application of lime and fertilizers to ensure satisfactory plant nutrient levels. The consequences of these continue long-term applications is not well understood may induce changes in soil chemical properties, the abundance, and speciation of potentially toxic trace element and as well as of micronutrients in agriculture soils. In this study, we evaluated the adsorption (at pH 5) and speciation of Zn in tropical soils (both agricultural and native vegetation) as a function of fertilization and contact time using chemical fractionation analyses and X-ray absorption spectroscopy. The soils overall had high Zn adsorption capacities (∼ 700 mg kg-1), but the agricultural soil was approximately 30 % higher than of the soil under native vegetation, and the proportion of Zn in the mobile fraction was 35 % in native vegetation and 21 % in agricultural soils. Zn speciation via linear combination analysis showed a strong relationship with soil mineralogical composition and reveled that Zn associated with organic matter decreased while Zn associated with P increased after the conversion of soils from native vegetation to highly fertilized soil. Aluminosilicate soil minerals were identified as major sinks of soil Zn, accounting for 34 % of total Zn retention regardless of soil origin and land use. Association of Zn with phosphate (i.e., hopeite) was observed in the agricultural soil samples, which might be an unexpected Zn-bearing mineral in highly weathered tropical soils and could have impacts on Zn plant nutrition.

Evidence for Suzuki-Miyaura cross-couplings catalyzed by ligated Pd3-clusters: from cradle to grave

Pdn clusters offer unique selectivity and exploitable reactivity in catalysis. Understanding the behavior of Pdn clusters is thus critical for catalysis, applied synthetic organic chemistry and greener outcomes for precious Pd. The Pd3 cluster, [Pd3(μ-Cl)(μ-PPh2)2(PPh3)3][Cl] (denoted as Pd3Cl2), which exhibits distinctive reactivity, was synthesized and immobilized on a phosphine-functionalized polystyrene resin (denoted as immob-Pd3Cl2). The resultant material served as a tool to study closely the role of Pd3 clusters in a prototypical Suzuki-Miyaura cross-coupling of 4-fluoro-1-bromobenzene and 4-methoxyphenyl boronic acid at varying low Pd ppm concentrations (24, 45, and 68 ppm). Advanced heterogeneity tests such as Hg poisoning and the three-phase test showed that leached mononuclear or nanoparticulate Pd are unlikely to be the major active catalyst species under the reaction conditions tested. EXAFS/XANES analysis from (pre)catalyst and filtered catalysts during and after catalysis has shown the intactness of the triangular structure of the Pd3X2 cluster, with exchange of chloride (X) by bromide during catalytic turnover of bromoarene substrate. This finding is further corroborated by treatment of immob-Pd3Cl2 after catalyzing the Suzuki-Miyaura reaction with excess PPh3, which releases the cluster from the polymer support and so permits direct observation of [Pd3(μ-Br)(μ-PPh2)2(PPh3)3]+ ions by ESI-MS. No evidence is seen for a proposed intermediate in which the bridging halogen on the Pd3 motif is replaced by an aryl group from the organoboronic acid, i.e. formed by a transmetallation-first process. Our findings taken together indicate that the 'Pd3X2' motif is an active catalyst species, which is stabilized by being immobilized, providing a more robust Pd3 cluster catalyst system. Non-immobilized Pd3Cl2 is less stable, as is followed by stepwise XAFS of the non-immobilized Pd3Cl2, which gradually changes to a species consistent with 'Pdx(PPh3)y' type material. Our findings have far-reaching future implications for Pd3 cluster involvement in catalysis, showing that immobilization of Pd3 cluster species offers advantages for rigorous mechanistic examination and applied chemistries.

Intermediates during the Nucleation of Platinum Nanoparticles by a Reaction with Ethylene Glycol: Operando X-ray Absorption Spectroscopy Studies with a Microfluidic Cell

Using operando X-ray absorption spectroscopy in a continuous-flow microfluidic cell, we have investigated the nucleation of platinum nanoparticles from aqueous hexachloroplatinate solution in the presence of the reducing agent ethylene glycol. By adjusting flow rates in the microfluidic channel, we resolved the temporal evolution of the reaction system in the first few seconds, generating the time profiles for speciation, ligand exchange, and reduction of Pt. Detailed analysis of the X-ray absorption near-edge structure and extended X-ray absorption fine structure spectra with multivariate data analysis shows that at least two reaction intermediates are involved in the transformation of the precursor H₂PtCl₆ to metallic platinum nanoparticles, including the formation of clusters with Pt-Pt bonding before complete reduction to Pt nanoparticles.

Improved precision in As speciation analysis with HERFD-XANES at the As K-edge: the case of As speciation in mine waste

High-energy-resolution fluorescence-detected (HERFD) X-ray absorption near-edge spectroscopy (XANES) is a spectroscopic method that allows for increased spectral feature resolution, and greater selectivity to decrease complex matrix effects compared with conventional XANES. XANES is an ideal tool for speciation of elements in solid-phase environmental samples. Accurate speciation of As in mine waste materials is important for understanding the mobility and toxicity of As in near-surface environments. In this study, linear combination fitting (LCF) was performed on synthetic spectra generated from mixtures of eight measured reference compounds for both HERFD-XANES and transmission-detected XANES to evaluate the improvement in quantitative speciation with HERFD-XANES spectra. The reference compounds arsenolite (As2O3), orpiment (As2S3), getchellite (AsSbS3), arsenopyrite (FeAsS), kaňkite (FeAsO4·3.5H2O), scorodite (FeAsO4·2H2O), sodium arsenate (Na3AsO4), and realgar (As4S4) were selected for their importance in mine waste systems. Statistical methods of principal component analysis and target transformation were employed to determine whether HERFD improves identification of the components in a dataset of mixtures of reference compounds. LCF was performed on HERFD- and total fluorescence yield (TFY)-XANES spectra collected from mine waste samples. Arsenopyrite, arsenolite, orpiment, and sodium arsenate were more accurately identified in the synthetic HERFD-XANES spectra compared with the transmission-XANES spectra. In mine waste samples containing arsenopyrite and either scorodite or kaňkite, LCF with HERFD-XANES measurements resulted in fits with smaller R-factors than concurrently collected TFY measurements. The improved accuracy of HERFD-XANES analysis may provide enhanced delineation of As phases controlling biogeochemical reactions in mine wastes, contaminated soils, and remediation systems.

Isotopic and Spectroscopic Investigation of Mercury Accumulation in Houttuynia cordata Colonizing Historically Contaminated Soil

Houttuynia cordata Thunb (H. cordata) is a native vegetable colonizing mercury (Hg) mining sites in the southwest of China; it can accumulate high Hg concentrations in the rhizomes and roots (edible sections), and thus consumption of H. cordata represents an important Hg exposure source to human. Here, we studied the spatial distribution, chemical speciation, and stable isotope compositions of Hg in the soil-H. cordata system at the Wuchuan Hg mining region in China, aiming to provide essential knowledge for assessing Hg risks and managing the transfer of Hg from soils to plants and agricultural systems. Mercury was mainly compartmentalized in the outlayer (periderm) of the underground tissues, with little Hg being translocated to the vascular bundle of the stem. Mercury presented as Hg-thiolates (94% ± 8%), with minor fractional amount of nanoparticulate β-HgS (β-HgS_NP, 15% ± 4%), in the roots and rhizomes. Analysis of Hg stable isotope ratios showed that cysteine-extractable soil Hg pool (δ²⁰²Hg_cys), root and rhizome Hg (δ²⁰²Hg_root, δ²⁰²Hg_rhizome) were isotopically lighter than Hg in the bulk soils. A significant positive correlation between δ²⁰²Hg_cys and δ²⁰²Hg_root was observed, suggesting that cysteine-extractable soil Hg pool was an important Hg source to H. cordata. The slightly positive Δ¹⁹⁹Hg value in the plant (Δ¹⁹⁹Hg_root = 0.07 ± 0.07‰, 2SD, n = 21; Δ¹⁹⁹Hg_rhizome = 0.06 ± 0.06‰, 2SD, n = 22) indicated that minor Hg was sourced from the surface water. Our results are important to assess the risks of Hg in H. cordata, and to develop sustainable methods to manage the transfer of Hg from soils to agricultural systems.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry

Multivariate data analysis tools have become an integral part of modern analytical chemistry, and principal component analysis (PCA) is perhaps foremost among these. PCA is central in approaching many problems in data exploration, classification, calibration, modelling, and curve resolution. However, PCA is only one form of a broader group of factor analysis (FA) methods that are rarely employed by chemists. The dominance of PCA in chemistry is primarily a consequence of history and convenience, but this has obscured the potential advantages of other FA tools that are widely used in other fields. The purpose of this article, which is intended for those who are already familiar with the mathematical foundations and applications of PCA, is to develop a framework to relate PCA to other commonly used FA methods from the perspective of chemical applications. Specifically, PCA is compared to maximum likelihood factor analysis (MLFA), principal axis factorization (PAF) and maximum likelihood PCA (MLPCA). Similarities and differences are highlighted with regard to the assumptions and constraints of the models, algorithms employed, and calculation of scores and loadings. Practical aspects such as data dimensionality, preprocessing, rank estimation, improper solutions (Heywood cases), and software implementation are considered. The performance of the four methods is compared using both simulated and experimental data sets. While PCA provides the most reliable estimates when measurement error variance is uniform (homoscedastic noise) and MLPCA works best when the error covariance matrix is explicitly known, MLFA and PAF have the distinct advantage of providing information about measurement uncertainty and adapting to situations of unknown heteroscedastic errors, eliminating the need for scaling. Moreover, MLFA in particular is shown to be tolerant to deviations from model linearity. These results make a strong case for increased application of other FA methods in chemistry.

In Vivo Formation of HgSe Nanoparticles and Hg-Tetraselenolate Complex from Methylmercury in Seabirds-Implications for the Hg-Se Antagonism

In vivo and in vitro evidence for detoxification of methylmercury (MeHg) as insoluble mercury selenide (HgSe) underlies the central paradigm that mercury exposure is not or little hazardous when tissue Se is in molar excess (Se:Hg > 1). However, this hypothesis overlooks the binding of Hg to selenoproteins, which lowers the amount of bioavailable Se that acts as a detoxification reservoir for MeHg, thereby underestimating the toxicity of mercury. This question was addressed by determining the chemical forms of Hg in various tissues of giant petrels Macronectes spp. using a combination of high energy-resolution X-ray absorption near edge structure and extended X-ray absorption fine structure spectroscopy, and transmission electron microscopy coupled to elemental mapping. Three main Hg species were identified, a MeHg-cysteinate complex, a four-coordinate selenocysteinate complex (Hg(Sec)₄), and a HgSe precipitate, together with a minor dicysteinate complex Hg(Cys)₂. The amount of HgSe decreases in the order liver > kidneys > brain = muscle, and the amount of Hg(Sec)₄ in the order muscle > kidneys > brain > liver. On the basis of biochemical considerations and structural modeling, we hypothesize that Hg(Sec)₄ is bound to the carboxy-terminus domain of selenoprotein P (SelP) which contains 12 Sec residues. Structural flexibility allows SelP to form multinuclear Hg_x(Se,Sec)_y complexes, which can be biomineralized to HgSe by protein self-assembly. Because Hg(Sec)₄ has a Se:Hg molar ratio of 4:1, this species severely depletes the stock of bioavailable Se for selenoprotein synthesis and activity to one μg Se/g dry wet in the muscle of several birds. This concentration is still relatively high because selenium is naturally abundant in seawater, therefore it probably does not fall below the metabolic need for essential selenium. However, this study shows that this may not be the case for terrestrial animals, and that muscle may be the first tissue potentially injured by Hg toxicity.

In Situ/Operando Electrocatalyst Characterization by X-ray Absorption Spectroscopy

During the last decades, X-ray absorption spectroscopy (XAS) has become an indispensable method for probing the structure and composition of heterogeneous catalysts, revealing the nature of the active sites and establishing links between structural motifs in a catalyst, local electronic structure, and catalytic properties. Here we discuss the fundamental principles of the XAS method and describe the progress in the instrumentation and data analysis approaches undertaken for deciphering X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectra. Recent usages of XAS in the field of heterogeneous catalysis, with emphasis on examples concerning electrocatalysis, will be presented. The latter is a rapidly developing field with immense industrial applications but also unique challenges in terms of the experimental characterization restrictions and advanced modeling approaches required. This review will highlight the new insight that can be gained with XAS on complex real-world electrocatalysts including their working mechanisms and the dynamic processes taking place in the course of a chemical reaction. More specifically, we will discuss applications of in situ and operando XAS to probe the catalyst's interactions with the environment (support, electrolyte, ligands, adsorbates, reaction products, and intermediates) and its structural, chemical, and electronic transformations as it adapts to the reaction conditions.

Application of an Augmentation Method to MCR-ALS Analysis for XAFS and Raman Data Matrices in the Structural Change of Isopolymolybdates

We measured X-ray absorption fine structure (XAFS) and Raman spectra of isopolymolybdates(VI) in highly concentrated HNO3 solution (0.15 - 4.0 M), which change their geometries depending on the acid concentration, and performed the simultaneous resolution of the XAFS and Raman data using a multivariate curve resolution by alternating least-squares (MCR-ALS) analysis. In iterative ALS optimization, initial data matrices were prepared by two different methods. For low sensitivity of the XAFS spectra to the geometrical change of the isopolymolybdates, the MCR-ALS result of single XAFS data matrix shows a large dependence on the preparation method of the initial data matrices. This problem is improved by the simultaneous resolution of the XAFS and Raman data: the MCR-ALS result of an augmented matrix of these data has little dependence on the initial data matrices. This indicates that the augmentation method effectively improves the rotation ambiguities in the MCR-ALS analysis of the XAFS data.

Medicago truncatula Ferroportin2 mediates iron import into nodule symbiosomes

Iron is an essential cofactor for symbiotic nitrogen fixation, required by many of the enzymes involved, including signal transduction proteins, O₂ homeostasis systems, and nitrogenase itself. Consequently, host plants have developed a transport network to deliver essential iron to nitrogen-fixing nodule cells. Ferroportin family members in model legume Medicago truncatula were identified and their expression was determined. Yeast complementation assays, immunolocalization, characterization of a tnt1 insertional mutant line, and synchrotron-based X-ray fluorescence assays were carried out in the nodule-specific M. truncatula ferroportin Medicago truncatula nodule-specific gene Ferroportin2 (MtFPN2) is an iron-efflux protein. MtFPN2 is located in intracellular membranes in the nodule vasculature and in inner nodule tissues, as well as in the symbiosome membranes in the interzone and early-fixation zone of the nodules. Loss-of-function of MtFPN2 alters iron distribution and speciation in nodules, reducing nitrogenase activity and biomass production. Using promoters with different tissular activity to drive MtFPN2 expression in MtFPN2 mutants, we determined that expression in the inner nodule tissues is sufficient to restore the phenotype, while confining MtFPN2 expression to the vasculature did not improve the mutant phenotype. These data indicate that MtFPN2 plays a primary role in iron delivery to nitrogen-fixing bacteroids in M. truncatula nodules.

Spectral Decomposition of X-ray Absorption Spectroscopy Datasets: Methods and Applications

X-ray absorption spectroscopy (XAS) today represents a widespread and powerful technique, able to monitor complex systems under in situ and operando conditions, while external variables, such us sampling time, sample temperature or even beam position over the analysed sample, are varied. X-ray absorption spectroscopy is an element-selective but bulk-averaging technique. Each measured XAS spectrum can be seen as an average signal arising from all the absorber-containing species/configurations present in the sample under study. The acquired XAS data are thus represented by a spectroscopic mixture composed of superimposed spectral profiles associated to well-defined components, characterised by concentration values evolving in the course of the experiment. The decomposition of an experimental XAS dataset in a set of pure spectral and concentration values is a typical example of an inverse problem and it goes, usually, under the name of multivariate curve resolution (MCR). In the present work, we present an overview on the major techniques developed to realize the MCR decomposition together with a selection of related results, with an emphasis on applications in catalysis. Therein, we will highlight the great potential of these methods which are imposing as an essential tool for quantitative analysis of large XAS datasets as well as the directions for further development in synergy with the continuous instrumental progresses at synchrotron sources.

Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure

Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.

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Random forest models accurately identify atomic coordination environments from XANES

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World's largest X-ray absorption spectra database

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Transferable model performance to experimental spectra

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New insights from interpreting machine-learning models

The characterization of atomic local environments in a material is important in many physical and chemical fields. Among various techniques, X-ray absorption spectroscopy (XAS) is one of the most widely used methods. However, the analysis of XAS is often qualitative and contrastive, requiring reference spectra from compounds that may not be available. This work introduces a machine-learning (ML)-based approach that directly predicts the atomic environment labels from the X-ray absorption near-edge structure (XANES) by training on a large computed XANES dataset. This data-driven approach shows excellent accuracy exceeding 80% in both computational and experimental tests. The application of ML models to spectroscopy will likely gather considerable interest in the near future, with accelerated or even on-the-fly interpretation of spectra directly from experiments. Such ML-accelerated approaches are expected to bring about a transformative leap in the pace of materials discovery and design.

High-Energy Resolution Fluorescence Detected X-Ray Absorption Spectroscopy: A Powerful New Structural Tool in Environmental Biogeochemistry Sciences

The study of the speciation of highly diluted elements by X-ray absorption spectroscopy (XAS) is extremely challenging, especially in environmental biogeochemistry sciences. Here we present an innovative synchrotron spectroscopy technique: high-energy resolution fluorescence detected XAS (HERFD-XAS). With this approach, measurement of the XAS signal in fluorescence mode using a crystal analyzer spectrometer with a ∼1-eV energy resolution helps to overcome restrictions on sample concentrations that can be typically measured with a solid-state detector. We briefly describe the method, from both an instrumental and spectroscopic point of view, and emphasize the effects of energy resolution on the XAS measurements. We then illustrate the positive impact of this technique in terms of detection limit with two examples dealing with Ce in ecologically relevant organisms and with Hg species in natural environments. The sharp and well-marked features of the HERFD-X-ray absorption near-edge structure spectra obtained enable us to determine unambiguously and with greater precision the speciation of the probed elements. This is a major technological advance, with strong benefits for the study of highly diluted elements using XAS. It also opens new possibilities to explore the speciation of a target chemical element at natural concentration levels, which is critical in the fields of environmental and biogeochemistry sciences.

Practical review on the use of synchrotron based micro- and nano- X-ray fluorescence mapping and X-ray absorption spectroscopy to investigate the interactions between plants and engineered nanomaterials

The increased use of engineered nanomaterials (ENMs) in commercial products and the continuous development of novel applications, is leading to increased intentional and unintentional release of ENMs into the environment with potential negative impacts. Particularly, the partition of nanoparticles (NPs) to waste water treatment plant (WWTP) sludge represents a potential threat to agricultural ecosystems where these biosolids are being applied as fertilizers. Moreover, several applications of ENMs in agriculture and soil remediation are suggested. Therefore, detailed risk assessment should be done to evaluate possible secondary negative impacts. The impact of ENMS on plants as central component of ecosystems and worldwide food supply is of primary relevance. Understanding the fate and physical and chemical modifications of NPs in plants and their possible transfer into food chains requires specialized analytical techniques. Due to the importance of both chemical and physical factors to consider for a better understanding of ENMs behavior in complex matrices, these materials can be considered a new type of analyte. An ideal technique should require minimal sample preparation, be non-destructive, and offer the best balance between sensitivity, chemical specificity, and spatial resolution. Synchrotron radiation (SR) techniques are particularly adapted to investigate localization and speciation of ENMs in plants. SR X-ray fluorescence mapping (SR-XFM) offers multi-elemental detection with lateral resolution down to the tens of nm, in combination with spatially resolved X-ray absorption spectroscopy (XAS) speciation. This review will focus on important methodological aspects regarding sample preparation, data acquisition and data analysis of SR-XFM/XAS to investigate interactions between plants and ENMs.

The duality of UiO-67-Pt MOFs: connecting treatment conditions and encapsulated Pt species by operando XAS

XAS study of Pt-functionalized UiO-67 MOFs shows that 2 types of catalytically active sites can be formed in MOF cavities isolated Pt-complexes and Pt nanoparticles.

Unravelling redox processes of Li7MnN4 upon electrochemical Li extraction–insertion using operando XAS

Evolution upon electrochemical oxidation of the Li₇MnN₄ Mn K-edge absorption spectra has been described using 3 distinctive local environments.

Solving local structure around dopants in metal nanoparticles with ab initio modeling of X-ray absorption near edge structure

XANES analysis guided by ab initio modeling is proposed for refinement of local environments around metal impurities in heterogeneous catalysts.

Effects of calcium on the chromophores of dissolved organic matter and their interactions with copper

The binding of Ca(2+) and Cu(2+) by dissolved organic matter (DOM) exemplified by Suwannee River Humic Acid (SRHA) was examined in this study. Metal-DOM interactions were quantified using linear and log-transformed DOM absorbance spectra acquired at varying pHs and total Ca(2+) and Cu(2+) concentrations. The binding of Cu(2+) by DOM was examined in the presence of three different Ca(2+) concentrations. Interactions of Ca(2+) with DOM chromophores were accompanied by the emergence of features in the differential spectra that were dissimilar from those characteristic for the binding of Cu(2+). The amount of Ca(2+) and Cu(2+) bound by DOM was ascertained using changes of the slopes of log-transformed absorbance spectra in the range of wavelength 350-400 nm (denoted as S350-400) and comparing the data with model predictions. The observed effects indicated the occurrence of the replacement of a characteristic number of protons by the bound Ca(2+) and Cu(2+) ions.