Abstract
Reducible oxides are widely used catalyst supports that can increase oxidation reaction rates by transferring lattice oxygen at the metal-support interface. There are many outstanding ...questions regarding the atomic-scale dynamic meta-stability (i.e., fluxional behavior) of the interface during catalysis. Here, we employ aberration-corrected
operando
electron microscopy to visualize the structural dynamics occurring at and near Pt/CeO
2
interfaces during CO oxidation. We show that the catalytic turnover frequency correlates with fluxional behavior that (a) destabilizes the supported Pt particle, (b) marks an enhanced rate of oxygen vacancy creation and annihilation, and (c) leads to increased strain and reduction in the CeO
2
support surface. Overall, the results implicate the interfacial Pt-O-Ce bonds anchoring the Pt to the support as being involved also in the catalytically-driven oxygen transfer process, and they suggest that oxygen reduction takes place on the highly reduced CeO
2
surface before migrating to the interfacial perimeter for reaction with CO.
Oxide-supported noble metal catalysts have been extensively studied for decades for the water gas shift (WGS) reaction, a catalytic transformation central to a host of large volume processes that ...variously utilize or produce hydrogen. There remains considerable uncertainty as to how the specific features of the active metal-support interfacial bonding-perhaps most importantly the temporal dynamic changes occurring therein-serve to enable high activity and selectivity. Here we report the dynamic characteristics of a Pt/CeO
system at the atomic level for the WGS reaction and specifically reveal the synergistic effects of metal-support bonding at the perimeter region. We find that the perimeter Pt
- O vacancy-Ce
sites are formed in the active structure, transformed at working temperatures and their appearance regulates the adsorbate behaviors. We find that the dynamic nature of this site is a key mechanistic step for the WGS reaction.
Intratumoral stimulatory dendritic cells (SDCs) play an important role in stimulating cytotoxic T cells and driving immune responses against cancer. Understanding the mechanisms that regulate their ...abundance in the tumor microenvironment (TME) could unveil new therapeutic opportunities. We find that in human melanoma, SDC abundance is associated with intratumoral expression of the gene encoding the cytokine FLT3LG. FLT3LG is predominantly produced by lymphocytes, notably natural killer (NK) cells in mouse and human tumors. NK cells stably form conjugates with SDCs in the mouse TME, and genetic and cellular ablation of NK cells in mice demonstrates their importance in positively regulating SDC abundance in tumor through production of FLT3L. Although anti-PD-1 'checkpoint' immunotherapy for cancer largely targets T cells, we find that NK cell frequency correlates with protective SDCs in human cancers, with patient responsiveness to anti-PD-1 immunotherapy, and with increased overall survival. Our studies reveal that innate immune SDCs and NK cells cluster together as an excellent prognostic tool for T cell-directed immunotherapy and that these innate cells are necessary for enhanced T cell tumor responses, suggesting this axis as a target for new therapies.
Differentiation of proinflammatory CD4+ conventional T cells (Tconv) is critical for productive antitumor responses yet their elicitation remains poorly understood. We comprehensively characterized ...myeloid cells in tumor draining lymph nodes (tdLN) of mice and identified two subsets of conventional type-2 dendritic cells (cDC2) that traffic from tumor to tdLN and present tumor-derived antigens to CD4+ Tconv, but then fail to support antitumor CD4+ Tconv differentiation. Regulatory T cell (Treg) depletion enhanced their capacity to elicit strong CD4+ Tconv responses and ensuing antitumor protection. Analogous cDC2 populations were identified in patients, and as in mice, their abundance relative to Treg predicts protective ICOS+ PD-1lo CD4+ Tconv phenotypes and survival. Further, in melanoma patients with low Treg abundance, intratumoral cDC2 density alone correlates with abundant CD4+ Tconv and with responsiveness to anti-PD-1 therapy. Together, this highlights a pathway that restrains cDC2 and whose reversal enhances CD4+ Tconv abundance and controls tumor growth.
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•cDC2 initiate activation but not differentiation of antitumor CD4+ Tconv•Treg depletion relieves cDC2 suppression driving antitumor CD4+ Tconv differentiation•Human equivalent of mouse cDC2 are present in the tumor and draining lymph node•The balance of human cDC2/Treg in the TME dictates T cell quality and prognosis
A subtype of conventional dendritic cells, cDC2, are able to prime CD4+ T cells for antitumor functions and the presence of cDC2 in human cancer samples may serve as a predictive biomarker for survival and response to immune checkpoint blockade.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this work, we employ density functional theory simulations to investigate possible spin polarization of CeO
-(111) surface and its impact on the interactions between a ceria support and Pt ...nanoparticles. With a Gaussian type orbital basis, our simulations suggest that the CeO
-(111) surface exhibits a robust surface spin polarization due to the internal charge transfer between atomic Ce and O layers. In turn, it can lower the surface oxygen vacancy formation energy and enhance the oxide reducibility. We show that the inclusion of spin polarization can significantly reduce the major activation barrier in the proposed reaction pathway of CO oxidation on ceria-supported Pt nanoparticles. For metal-support interactions, surface spin polarization enhances the bonding between Pt nanoparticles and ceria surface oxygen, while CO adsorption on Pt nanoparticles weakens the interfacial interaction regardless of spin polarization. However, the stable surface spin polarization can only be found in the simulations based on the Gaussian type orbital basis. Given the potential importance in the design of future high-performance catalysts, our present study suggests a pressing need to examine the surface ferromagnetism of transition metal oxides in both experiment and theory.
ClinVar provides open access to variant classifications shared from many clinical laboratories. Although most classifications are consistent across laboratories, classification differences exist. To ...facilitate resolution of classification differences on a large scale, clinical laboratories were encouraged to reassess outlier classifications of variants with medically significant differences (MSDs). Outliers were identified by first comparing ClinVar submissions from 41 clinical laboratories to detect variants with MSDs between the laboratories (650 variants). Next, MSDs were filtered for variants with ≥3 classifications (244 variants), of which 87.6% (213 variants) had a majority consensus in ClinVar, thus allowing for identification of outlier classifications in need of reassessment. Laboratories with outlier classifications were sent a custom report and encouraged to reassess variants. Results were returned for 204 (96%) variants, of which 62.3% (127) were resolved. Of those 127, 64.6% (82) were resolved due to reassessment prompted by this study and 35.4% (45) resolved by a previously completed reassessment. This study demonstrates a scalable approach to classification resolution and capitalizes on the value of data sharing within ClinVar. These activities will help the community move toward more consistent variant classifications, which will improve the care of patients with, or at risk for, genetic disorders.
To facilitate resolution of sequence variant classification differences in ClinVar, clinical laboratories were sent a custom report of outlier interpretations and encouraged to reassess variants. Among 650 variants with medically significant differences between laboratories, 31% (204 variants) had an outlier classification, of which 62.3% (127 variants) were reclassified and resolved upon outlier laboratory reassessment alone. This study demonstrates a scalable approach to tackle a portion of classification resolution and capitalizes on the value of data sharing within ClinVar.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, ...for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.
Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with ...millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision.
Metalattices are artificial 3D solids, periodic on sub-100 nm length scales, that enable the functional properties of materials to be tuned. However, because of their complex structure, predicting ...and characterizing their properties is challenging. Here we demonstrate the first nondestructive measurements of the mechanical and structural properties of metalattices with feature sizes down to 14 nm. By monitoring the time-dependent diffraction of short wavelength light from laser-excited acoustic waves in the metalattices, we extract their acoustic dispersion, Young’s modulus, filling fraction, and thicknesses. Our measurements are in excellent agreement with macroscopic predictions and potentially destructive techniques such as nanoindentation and scanning electron microscopy, with increased accuracy over larger areas. This is interesting because the transport properties of these metalattices do not obey bulk predictions. Finally, this approach is the only way to validate the filling fraction of metalattices over macroscopic areas. These combined capabilities can enable accurate synthesis of nanoenhanced materials.
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IJS, KILJ, NUK, PNG, UL, UM
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