Hydroxide promotes ion pairing in the NaNO[subscript 2]?NaOH?H[subscript 2]O system
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Ultrafast pump–probe nano-imaging combines scanning probe-based optical near-field microscopy with ultrafast spectroscopy to enable imaging with deep sub-wavelength spatial resolution, femtosecond temporal resolution and simultaneous spectral resolution. Ultrafast nano-imaging has gained increased attention for its ability to provide far-from-equilibrium excitation and excited-state contrast. With coherent and nonlinear probing, coupled electron, spin and lattice dynamics on elementary timescale and length scale can be resolved. Through nano-movies, ultrafast nano-imaging visualizes correlated quantum dynamics underlying the properties of solid-state materials, semiconductors, molecular electronic, photonic, photovoltaic and other functional materials. With nanometre spatial resolution, this method probes elementary dynamic processes across multiple length scales that are otherwise obscured in conventional ultrafast spectroscopy in which heterogeneities are spatially averaged. Furthermore, this Primer describes the theoretical background and experimental implementation of ultrafast nano-imaging; signal interpretation and modelling; representative examples and a perspective for the future development of the field.
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The present invention relates to compositions including nano-particles and a nano-structured support matrix, methods of their preparation and applications thereof. The compositions of the present invention are particularly suitable for use as anode material for lithium-ion rechargeable batteries. The nano-structured support matrix can include nanotubes, nanowires, nanorods, and mixtures thereof. The composition can further include a substrate on which the nano-structured support matrix is formed. The substrate can include a current collector material.
Mitochondria are crucial regulators of the intrinsic pathway of cancer cell death. The high sensitivity of cancer cells to mitochondrial dysfunction offers opportunities for emerging targets in cancer therapy. Herein, magnetic nano-transducers, which convert external magnetic fields into physical stress, are designed to induce mitochondrial dysfunction to remotely kill cancer cells. Spindle-shaped iron oxide nanoparticles were synthesized to maximize cellular internalization and magnetic transduction. The magneto-mechanical transduction of nano-transducers in mitochondria enhances cancer cell apoptosis by promoting a mitochondrial quality control mechanism, referred to as mitophagy. Furthermore, in the liver cancer animal model, nano-transducers are infused into the local liver tumor via the hepatic artery. After treatment with a magnetic field, in vivo mitophagy-mediated cancer cell death was also confirmed by mitophagy markers, mitochondrial DNA damage assay, and TUNEL staining of tissues. This study is expected to contribute to the development of nanoparticle-mediated mitochondria-targeting cancer therapy and biological tools, such as magneto-genetics.
Direct synthesis of thin-film carbon nanomaterials on oxide-coated silicon substrates provides a viable pathway for building a dense array of miniaturized (micron-scale) electrochemical sensors with high performance. However, material synthesis generally involves many parameters, making material engineering based on trial and error highly inefficient. Here, we report a two-pronged strategy for producing engineered thin-film carbon nanomaterials that have a nano-graphitic structure. First, we introduce a variant of the metal-induced graphitization technique that generates micron-scale islands of nano-graphitic carbon materials directly on oxide-coated silicon substrates. A novel feature of our material synthesis is that, through substrate engineering, the orientation of graphitic planes within the film aligns preferentially with the silicon substrate. This feature allows us to use the Raman spectroscopy for quantifying structural properties of the sensor surface, where the electrochemical processes occur. Second, we find phenomenological models for predicting the amplitudes of the redox current and the sensor capacitance from the material structure, quantified by Raman. Our results indicate that the key to achieving high-performance micro-sensors from nano-graphitic carbon is to increase both the density of point defects and the size of the graphitic crystallites. Our study offers a viable strategy for building planar electrochemical micro-sensors with high-performance.
Innovation in microscopy has often been critical in advancing both fundamental science and technological progress. Notably, the evolution of ultrafast near-field optical nano-spectroscopy and nano-imaging has unlocked the ability to image at spatial scales from nanometers to ångströms and temporal scales from nanoseconds to femtoseconds. This approach revealed a plethora of fascinating light-matter states and quantum phenomena, including various species of polaritons, quantum phases, and complex many-body effects. This review focuses on the working principles and state-of-the-art development of ultrafast tip-enhanced and near-field microscopy, integrating diverse optical pump-probe methods across the terahertz (THz) to ultraviolet (UV) spectral ranges. It highlights their utility in examining a broad range of materials, including two-dimensional (2D), organic molecular, and hybrid materials. The review concludes with a spatio-spectral-temporal comparison of ultrafast nano-imaging techniques, both within already well-defined domains, and offering an outlook on future developments of ultrafast tip-based microscopy and their potential to address a wider range of materials.
Association of plastic particles with plant roots could represent a pathway for human consumption of plastic and plastic-associated organic contaminants.
Quantum chemical DLPNO-CCSD(T)/cc-pVDZ//B3LYP/6–31G(d) calculations have been performed to unravel the formation mechanism of buckminsterfullerene (C 60 ) via the reaction of the C 40 nano bowl (C 40 H 10 ) and corannulene (C 20 H 10 ). The generated potential energy surfaces and molecular properties were further utilized to evaluate equilibrium constants and rate constants of elementary chemical reactions involved in the C 60 synthesis. The overall C 40 H 10 + C 20 H 10 → C 60 + 10H 2 reaction is shown to be highly exoergic and hence thermodynamically favorable at temperatures above 700 K and a kinetically feasible pathway under high-temperature conditions was revealed. This route involves hydrogen atom abstraction steps to activate closed-shell reactants/intermediates alternating with cyclodehydrogenation reactions eventually zipping the edges between the reacting C 40 and C 20 units by consecutive closures of six- and five-membered rings between them. The reaction is driven/catalyzed by hydrogen atoms which carry out the hydrogen abstractions from C 60 H x and are later recovered on the cyclodehydrogenation stages. The initial association reaction, C 40 H 9 + C 20 H 10 → C 60 H 18 + H, linking the two units by a covalent C-C bond appears to represent the kinetic bottleneck of the whole multistep process. This reaction is fast at low temperatures but slows down and has its equilibrium shifted toward the reactants at high temperatures. Here, the feasibility of the proposed mechanism corroborates the hypothesis that C 60 can be produced through a series of elementary reactions typical for the PAH growth.
The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.
Enhanced TcO 4 − reduction by metallic Fe 0 in the presence of particulate and structural Si. Rhythmical precipitation of dissolved iron leads to formation of layered structures related to geological phenomena such as orbicular rocks and Liesegang rings.
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