The solubility of Ag NPs can affect their toxicity and persistence in the environment. We measured the solubility of organic-coated silver nanoparticles (Ag NPs) having particle diameters ranging ...from 5 to 80 nm that were synthesized using various methods, and with different organic polymer coatings including poly(vinylpyrrolidone) and gum arabic. The size and morphology of Ag NPs were characterized by transmission electron microscopy (TEM). X-ray absorption fine structure (XAFS) spectroscopy and synchrotron-based total X-ray scattering and pair distribution function (PDF) analysis were used to determine the local structure around Ag and evaluate changes in crystal lattice parameters and structure as a function of NP size. Ag NP solubility dispersed in 1 mM NaHCO(3) at pH 8 was found to be well correlated with particle size based on the distribution of measured TEM sizes as predicted by the modified Kelvin equation. Solubility of Ag NPs was not affected by the synthesis method and coating as much as by their size. Based on the modified Kelvin equation, the surface tension of Ag NPs was found to be ∼1 J/m(2), which is expected for bulk fcc (face centered cubic) silver. Analysis of XAFS, X-ray scattering, and PDFs confirm that the lattice parameter, a, of the fcc crystal structure of Ag NPs did not change with particle size for Ag NPs as small as 6 nm, indicating the absence of lattice strain. These results are consistent with the finding that Ag NP solubility can be estimated based on TEM-derived particle size using the modified Kelvin equation for particles in the size range of 5-40 nm in diameter.
Despite the increasing use of silver nanoparticles (Ag-NPs) in nanotechnology and their toxicity to invertebrates, the transformations and fate of Ag-NPs in the environment are poorly understood. ...This work focuses on the sulfidation processes of PVP-coated Ag-NPs, one of the most likely corrosion phenomena that may happen in the environment. The sulfur to Ag-NPs ratio was varied in order to control the extent of Ag-NPs transformation to silver sulfide (Ag₂S). A combination of synchrotron-based X-ray Diffraction (XRD) and Extended X-ray Absorption Fine Structure spectroscopy shows the increasing formation of Ag₂S with an increasing sulfur to Ag-NPs ratio. TEM observations show that Ag₂S forms nanobridges between the Ag-NPs leading to chain-like structures. In addition, sulfidation strongly affects surface properties of the Ag-NPs in terms of surface charge and dissolution rate. Both may affect the reactivity, transport, and toxicity of Ag-NPs in soils. In particular, the decrease of dissolution rate as a function of sulfide exposure may strongly limit Ag-NPs toxicity since released Ag⁺ ions are known to be a major factor in the toxicity of Ag-NPs.
Growing crystals by attaching particles
Crystals grow in a number a ways, including pathways involving the assembly of other particles and multi-ion complexes. De Yoreo
et al.
review the mounting ...evidence for these nonclassical pathways from new observational and computational techniques, and the thermodynamic basis for these growth mechanisms. Developing predictive models for these crystal growth and nucleation pathways will improve materials synthesis strategies. These approaches will also improve fundamental understanding of natural processes such as biomineralization and trace element cycling in aquatic ecosystems.
Science
, this issue
10.1126/science.aaa6760
Materials nucleate and grow by the assembly of small particles and multi-ion complexes.
BACKGROUND
Numerous lines of evidence challenge the traditional interpretations of how crystals nucleate and grow in synthetic and natural systems. In contrast to the monomer-by-monomer addition described in classical models, crystallization by addition of particles, ranging from multi-ion complexes to fully formed nanocrystals, is now recognized as a common phenomenon. This diverse set of pathways results from the complexity of both the free-energy landscapes and the reaction dynamics that govern particle formation and interaction.
Whereas experimental observations clearly demonstrate crystallization by particle attachment (CPA), many fundamental aspects remain unknown—particularly the interplay of solution structure, interfacial forces, and particle motion. Thus, a predictive description that connects molecular details to ensemble behavior is lacking. As that description develops, long-standing interpretations of crystal formation patterns in synthetic systems and natural environments must be revisited.
Here, we describe the current understanding of CPA, examine some of the nonclassical thermodynamic and dynamic mechanisms known to give rise to experimentally observed pathways, and highlight the challenges to our understanding of these mechanisms. We also explore the factors determining when particle-attachment pathways dominate growth and discuss their implications for interpreting natural crystallization and controlling nanomaterials synthesis.
ADVANCES
CPA has been observed or inferred in a wide range of synthetic systems—including oxide, metallic, and semiconductor nanoparticles; and zeolites, organic systems, macromolecules, and common biomineral phases formed biomimetically. CPA in natural environments also occurs in geologic and biological minerals. The species identified as being responsible for growth vary widely and include multi-ion complexes, oligomeric clusters, crystalline or amorphous nanoparticles, and monomer-rich liquid droplets.
Particle-based pathways exceed the scope of classical theories, which assume that a new phase appears via monomer-by-monomer addition to an isolated cluster. Theoretical studies have attempted to identify the forces that drive CPA, as well as the thermodynamic basis for appearance of the constituent particles. However, neither a qualitative consensus nor a comprehensive theory has emerged. Nonetheless, concepts from phase transition theory and colloidal physics provide many of the basic features needed for a qualitative framework. There is a free-energy landscape across which assembly takes place and that determines the thermodynamic preference for particle structure, shape, and size distribution. Dynamic processes, including particle diffusion and relaxation, determine whether the growth process follows this preference or another, kinetically controlled pathway.
OUTLOOK
Although observations of CPA in synthetic systems are reported for diverse mineral compositions, efforts to establish the scope of CPA in natural environments have only recently begun. Particle-based mineral formation may have particular importance for biogeochemical cycling of nutrients and metals in aquatic systems, as well as for environmental remediation. CPA is poised to provide a better understanding of biomineral formation with a physical basis for the origins of some compositions, isotopic signatures, and morphologies. It may also explain enigmatic textures and patterns found in carbonate mineral deposits that record Earth’s transition from an inorganic to a biological world.
A predictive understanding of CPA, which is believed to dominate solution-based growth of important semiconductor, oxide, and metallic nanomaterials, promises advances in nanomaterials design and synthesis for diverse applications. With a mechanism-based understanding, CPA processes can be exploited to produce hierarchical structures that retain the size-dependent attributes of their nanoscale building blocks and create materials with enhanced or novel physical and chemical properties.
Major gaps in our understanding of CPA.
Particle attachment is influenced by the structure of solvent and ions at solid-solution interfaces and in confined regions of solution between solid surfaces. The details of solution and solid structure create the forces that drive particle motion. However, as the particles move, the local structure and corresponding forces change, taking the particles from a regime of long-range to short-range interactions and eventually leading to particle-attachment events.
Field and laboratory observations show that crystals commonly form by the addition and attachment of particles that range from multi-ion complexes to fully formed nanoparticles. The particles involved in these nonclassical pathways to crystallization are diverse, in contrast to classical models that consider only the addition of monomeric chemical species. We review progress toward understanding crystal growth by particle-attachment processes and show that multiple pathways result from the interplay of free-energy landscapes and reaction dynamics. Much remains unknown about the fundamental aspects, particularly the relationships between solution structure, interfacial forces, and particle motion. Developing a predictive description that connects molecular details to ensemble behavior will require revisiting long-standing interpretations of crystal formation in synthetic systems, biominerals, and patterns of mineralization in natural environments.
Memory B cells play a fundamental role in host defenses against viruses, but to date, their role has been relatively unsettled in the context of SARS-CoV-2. We report here a longitudinal single-cell ...and repertoire profiling of the B cell response up to 6 months in mild and severe COVID-19 patients. Distinct SARS-CoV-2 spike-specific activated B cell clones fueled an early antibody-secreting cell burst as well as a durable synchronous germinal center response. While highly mutated memory B cells, including pre-existing cross-reactive seasonal Betacoronavirus-specific clones, were recruited early in the response, neutralizing SARS-CoV-2 RBD-specific clones accumulated with time and largely contributed to the late, remarkably stable, memory B cell pool. Highlighting germinal center maturation, these cells displayed clear accumulation of somatic mutations in their variable region genes over time. Overall, these findings demonstrate that an antigen-driven activation persisted and matured up to 6 months after SARS-CoV-2 infection and may provide long-term protection.
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•Seasonal coronavirus memory B cells contribute to the early anti-SARS-CoV-2 response•Spike-specific memory B cells with a resting phenotype increase up to 6 months•Somatic mutations accumulate in SARS-CoV-2-specific memory B cells over time•Longitudinal study reveals a temporal switch to anti-RBD neutralizing memory B cells
The B cell response against SARS-CoV-2 shows a temporal shift from an extrafollicular reaction that includes memory B cells against seasonal coronaviruses toward a germinal center-dependent memory response encoding (spike-specific) neutralizing antibodies.
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The ...automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
The effects of alloying concentration on the aqueous corrosion behavior of aluminum-manganese-molybdenum (Al-Mn-Mo) alloys with 8–20 at% Mn and 0–30 at% Mo were investigated by experiments and ...atomistic simulations. The pitting potential of Al-Mn-Mo increased with Mo% from − 0.16–0.49 V. The passive film thickness depended on the total alloy concentration, while its compactness and defect density on the individual ones. Specifically, Al80Mn8Mo12 exhibited higher corrosion resistance than Al80Mn20 due to the formation of a more compact and less defective passive film, as explained by the roles Mo played in both the substrate and the passive film.
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•Mo enhanced the aqueous corrosion resistance and pitting potential of Al-Mn-Mo.•Passive layer thickness depends on the total alloy composition of Mn and Mo.•Selective surface dissolution of Mo increased free volume at metal/oxide interface.•Simulation showed barrier characteristics of oxide increased with free volume in Al.
CAPTION(S): Table S1: Patients with reported thrombocytopenia post SARS/CoV-2 vaccination Byline: Eun-Ju Lee, Douglas B. Cines, Terry Gernsheimer, Craig Kessler, Marc Michel, Michael D. Tarantino, ...John W. Semple, Donald M. Arnold, Bertrand Godeau, Michele P. Lambert, James B. Bussel
Structure of Ferrihydrite, a Nanocrystalline Material Michel, F. Marc; Ehm, Lars; Antao, Sytle M ...
Science (American Association for the Advancement of Science),
06/2007, Letnik:
316, Številka:
5832
Journal Article
Recenzirano
Despite the ubiquity of ferrihydrite in natural sediments and its importance as an industrial sorbent, the nanocrystallinity of this iron oxyhydroxide has hampered accurate structure determination by ...traditional methods that rely on long-range order. We uncovered the atomic arrangement by real-space modeling of the pair distribution function (PDF) derived from direct Fourier transformation of the total x-ray scattering. The PDF for ferrihydrite synthesized with the use of different routes is consistent with a single phase (hexagonal space group P6₃mc; a = ~5.95 angstroms, c = ~9.06 angstroms). In its ideal form, this structure contains 20% tetrahedrally and 80% octahedrally coordinated iron and has a basic structural motif closely related to the Baker-Figgis δ-Keggin cluster. Real-space fitting indicates structural relaxation with decreasing particle size and also suggests that second-order effects such as internal strain, stacking faults, and particle shape contribute to the PDFs.
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels ...in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to 1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and 2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website. 11 http://www.cardiacatlas.org.
This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 ...patients were included: 107 formed the training sample (55 ± 14 years, 58 women) and 38 the separate test sample (62 ± 12 years, 22 women). Patients had glioma, brain metastases, meningioma, or no enhancing lesion. T1, T2-FLAIR, diffusion-weighted imaging, low-dose, and standard-dose postcontrast T1 sequences were acquired. A deep network was trained to process the precontrast and low-dose sequences to predict "virtual" surrogate images for contrast-enhanced T1. Once trained, the deep learning method was evaluated on the test sample. The discrepancies between the predicted virtual images and the standard-dose MRIs were qualitatively and quantitatively evaluated using both automated voxel-wise metrics and a reader study, where 2 radiologists graded image qualities and marked all visible enhancing lesions.
The automated analysis of the test brain MRIs computed a structural similarity index of 87.1% ± 4.8% between the predicted virtual sequences and the reference contrast-enhanced T1 MRIs, a peak signal-to-noise ratio of 31.6 ± 2.0 dB, and an area under the curve of 96.4% ± 3.1%. At Youden's operating point, the voxel-wise sensitivity (SE) and specificity were 96.4% and 94.8%, respectively. The reader study found that virtual images were preferred to standard-dose MRI in terms of image quality (P = 0.008). A total of 91 reference lesions were identified in the 38 test T1 sequences enhanced with full dose of contrast agent. On average across readers, the brain lesion SE of the virtual images was 83% for lesions larger than 10 mm (n = 42), and the associated false detection rate was 0.08 lesion/patient. The corresponding positive predictive value of detected lesions was 92%, and the F1 score was 88%. Lesion detection performance, however, dropped when smaller lesions were included: average SE was 67% for lesions larger than 5 mm (n = 74), and 56% with all lesions included regardless of their size. The false detection rate remained below 0.50 lesion/patient in all cases, and the positive predictive value remained above 73%. The composite F1 score was 63% at worst.
The proposed deep learning method for virtual contrast-enhanced T1 brain MRI prediction showed very high quantitative performance when evaluated with standard voxel-wise metrics. The reader study demonstrated that, for lesions larger than 10 mm, good detection performance could be maintained despite a 4-fold division in contrast agent usage, unveiling a promising avenue for reducing the gadolinium exposure of returning patients. Small lesions proved, however, difficult to handle for the deep network, showing that full-dose injections remain essential for accurate first-line diagnosis in neuro-oncology.
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