Wavelength-Dependent Solar N2 Fixation into Ammonia and Nitrate in Pure Water

Solar-driven N2 fixation using a photocatalyst in water presents a promising alternative to the traditional Haber-Bosch process in terms of both energy efficiency and environmental concern. At present, the product of solar N2 fixation is either NH4+ or NO3-. Few reports described the simultaneous formation of ammonia (NH4+) and nitrate (NO3-) by a photocatalytic reaction and the related mechanism. In this work, we report a strategy to photocatalytically fix nitrogen through simultaneous reduction and oxidation to produce NH4+ and NO3- by W18O49 nanowires in pure water. The underlying mechanism of wavelength-dependent N2 fixation in the presence of surface defects is proposed, with an emphasis on oxygen vacancies that not only facilitate the activation and dissociation of N2 but also improve light absorption and the separation of the photoexcited carriers. Both NH4+ and NO3- can be produced in pure water under a simulated solar light and even till the wavelength reaching 730 nm. The maximum quantum efficiency reaches 9% at 365 nm. Theoretical calculation reveals that disproportionation reaction of the N2 molecule is more energetically favorable than either reduction or oxidation alone. It is worth noting that the molar fraction of NH4+ in the total product (NH4+ plus NO3-) shows an inverted volcano shape from 365 nm to 730 nm. The increased fraction of NO3- from 365 nm to around 427 nm results from the competition between the oxygen evolution reaction (OER) at W sites without oxygen vacancies and the N2 oxidation reaction (NOR) at oxygen vacancy sites, which is driven by the intrinsically delocalized photoexcited holes. From 427 nm to 730 nm, NOR is energetically restricted due to its higher equilibrium potential than that of OER, accompanied by the localized photoexcited holes on oxygen vacancies. Full disproportionation of N2 is achieved within a range of wavelength from ~427 nm to ~515 nm. This work presents a rational strategy to efficiently utilize the photoexcited carriers and optimize the photocatalyst for practical nitrogen fixation

Crystal Structure Assignment for Unknown Compounds from X‑ray Diffraction Patterns with Deep Learning

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation

Sequential co-reduction of nitrate and carbon dioxide enables selective urea electrosynthesis

Despite the recent achievements in urea electrosynthesis from co-reduction of nitrogen wastes (such as NO3−) and CO2, the product selectivity remains fairly mediocre due to the competing nature of the two parallel reduction reactions. Here we report a catalyst design that affords high selectivity to urea by sequentially reducing NO3− and CO2 at a dynamic catalytic centre, which not only alleviates the competition issue but also facilitates C−N coupling. We exemplify this strategy on a nitrogen-doped carbon catalyst, where a spontaneous switch between NO3− and CO2 reduction paths is enabled by reversible hydrogenation on the nitrogen functional groups. A high urea yield rate of 596.1 µg mg−1 h−1 with a promising Faradaic efficiency of 62% is obtained. These findings, rationalized by in situ spectroscopic techniques and theoretical calculations, are rooted in the proton-involved dynamic catalyst evolution that mitigates overwhelming reduction of reactants and thereby minimizes the formation of side products

Twin boundary defect engineering improves lithium-ion diffusion for fast-charging spinel cathode materials

Defect engineering on electrode materials is considered an effective approach to improve the electrochemical performance of batteries since the presence of a variety of defects with different dimensions may promote ion diffusion and provide extra storage sites. However, manipulating defects and obtaining an in-depth understanding of their role in electrode materials remain challenging. Here, we deliberately introduce a considerable number of twin boundaries into spinel cathodes by adjusting the synthesis conditions. Through high-resolution scanning transmission electron microscopy and neutron diffraction, the detailed structures of the twin boundary defects are clarified, and the formation of twin boundary defects is attributed to agminated lithium atoms occupying the Mn sites around the twin boundary. In combination with electrochemical experiments and first-principles calculations, we demonstrate that the presence of twin boundaries in the spinel cathode enables fast lithium-ion diffusion, leading to excellent fast charging performance, namely, 75% and 58% capacity retention at 5 C and 10 C, respectively. These findings demonstrate a simple and effective approach for fabricating fast-charging cathodes through the use of defect engineering

Intramuscular administration of glyoxylate rescues swine from lethal cyanide poisoning and ameliorates the biochemical sequalae of cyanide intoxication

Cyanide-a fast-acting poison-is easy to obtain given its widespread use in manufacturing industries. It is a high-threat chemical agent that poses a risk of occupational exposure in addition to being a terrorist agent. FDA-approved cyanide antidotes must be given intravenously, which is not practical in a mass casualty setting due to the time and skill required to obtain intravenous access. Glyoxylate is an endogenous metabolite that binds cyanide and reverses cyanide-induced redox imbalances independent of chelation. Efficacy and biochemical mechanistic studies in an FDA-approved preclinical animal model have not been reported. Therefore, in a swine model of cyanide poisoning, we evaluated the efficacy of intramuscular glyoxylate on clinical, metabolic, and biochemical endpoints. Animals were instrumented for continuous hemodynamic monitoring and infused with potassium cyanide. Following cyanide-induced apnea, saline control or glyoxylate was administered intramuscularly. Throughout the study, serial blood samples were collected for pharmacokinetic, metabolite, and biochemical studies, in addition, vital signs, hemodynamic parameters, and laboratory values were measured. Survival in glyoxylate-treated animals was 83% compared with 12% in saline-treated control animals (p < .01). Glyoxylate treatment improved physiological parameters including pulse oximetry, arterial oxygenation, respiration, and pH. In addition, levels of citric acid cycle metabolites returned to baseline levels by the end of the study. Moreover, glyoxylate exerted distinct effects on redox balance as compared with a cyanide-chelating countermeasure. In our preclinical swine model of lethal cyanide poisoning, intramuscular administration of the endogenous metabolite glyoxylate improved survival and clinical outcomes, and ameliorated the biochemical effects of cyanide