ACS Omega最新文献

Novel polar cysteine analogues have been synthesized for the derivatization of oligonucleotide depurination impurities that may be formed under acidic conditions. Depurination impurities belong to a group that includes deamination and phosphate diester impurities, which are similar in chemical structure to each other and the parent oligonucleotide, and thus coelute by most chromatographic separation methods. The polar cysteine analogues react with depurination impurities and enable their complete separation from the parent oligonucleotide by weak anion exchange (WAX) chromatography. Optimized conditions for the derivatization reaction and the WAX analysis are presented. The ability of the WAX method to chromatographically separate deamination and phosphate diester impurities is also demonstrated, and therefore, the combination of chemical derivatization and WAX chromatography permits detection and quantification of the three major degradation products of phosphorothioate (PS) oligonucleotides.

In a quest to explore interconvertible assemblies of hydrates of cobalt(II), copper(II), and zinc(II) 2,6-pyridinedicarboxylate (26-pdc), complexes having cation of a chloro-substituted analogue N-{(10-chloroanthracen-9-yl)methyl}-3-(1H-imidazol-1-yl)propan-1-amine were investigated. In the case of cobalt and copper complexes, a crystallized stable hydrate and a less stable methanol hydrate were guided by concentration-dependent crystallizations. The unit-cells of the crystals of the methanol hydrates of the two cobalt and copper complexes each belong to the P1̅ space group but have different stoichiometries as well as large differences in packing. These hydrates could be reversibly crystallized in a predictable manner. The unit-cell volumes of the methanol hydrate of the cobalt complex were four-times smaller than that of the respective stable form (C2/c space group), whereas similar hydrates of the copper complex had a two-times smaller unit-cell volume than that of the stable form. The cations of the stable forms assembled together and formed zigzag ladder-like chains. The spaces present in between the assembled chains were filled with clusters of face to face stacked anions. The transformation to stable form required a bottom-up building process of the unit-cell starting from a smaller unit-cell of the less stable hydrates. Fluorescence spectroscopic studies showed the possibility of two forms of assemblies of the zinc-complex in solution, but crystallization had yielded only the stable form.

Remaining useful life (RUL) prediction is crucial for simplifying maintenance procedures and extending the lifespan of aero-engines. Therefore, research on RUL prediction methods for aero-engines is increasingly gaining attention. In particular, some existing deep neural networks based on multiscale features extraction have achieved certain results in RUL predictions for aero-engines. However, these models often overlook two critical factors that affect RUL prediction performance: (i) different time series data points have varying importance for RUL prediction, and (ii) the connections and similarities between different sensor data in both directions. This paper aims to extract valuable multiscale features from raw monitoring data containing multiple sensor measurements, considering the aforementioned factors, and leverage these features to enhance RUL prediction results. To this end, we propose a novel deep neural network based on multiscale features extraction, named Multi-Scale Temporal-Spatial feature-based hybrid Deep neural Network (MSTSDN). We conduct experiments using two aero-engine data sets, namely C-MAPSS and N-CMAPSS, to evaluate RUL prediction performance of MSTSDN. Experimental results on C-MAPSS data set demonstrate that MSTSDN achieves more accurate and timely RUL predictions compared to 12 existing deep neural networks specifically designed for predicting RUL of aero-engine, especially under multiple operational conditions and fault modes. And experimental results on N-CMAPSS data set eventually indicate that MSTSDN can effectively track and fit with the actual RUL during the engine degradation phase.

Diarylethenes (DAEs) are an important class of photoswitchable compounds that typically undergo reversible photochemical conversions between the open and closed cyclized forms upon treatment with UV light or visible light. In this study, we introduced thioacid functional groups to several photochromic dithienylethene (DTE) derivatives and established a method that can be used to prepare these photoswitchable thioacids. Four thioacid-functionalized diarylethene derivatives were synthesized through the activation of carboxylic acids with N-hydroxysuccinimide, followed by reactions with sodium hydrosulfide with yields over 90%. These derivatives exhibited reversible photoswitching and photochromic properties upon treatment with ultraviolet (UV) and visible lights. The thioacid groups on these compounds can act as reaction sites for attaching other desirable functionalities. The photochromic properties of these new derivatives were characterized by using ultraviolet–visible (UV–vis) spectroscopy. The photocyclizations of one of the derivatives and its potassium salt were also characterized by using nuclear magnetic resonance (NMR) spectroscopy. The anions of the thioacid formed water-soluble photochromic systems, and their applications as colorimetric sensors in agarose hydrogels were demonstrated.

The continuous advancement of industry and technology has significantly increased electronic waste, which contributes to the depletion of valuable metal reserves. Therefore, it is crucial to recycle precious metals in electronic waste effectively and sustainably. This study introduces a novel approach by applying a carbazole–phosphazene-based polymer, EBE-06, in a two-stage leaching method for efficient metal extraction. In the first leaching stage, tin is selectively separated using an acid solution at a controlled pH. In the second stage, valuable metals such as gold are recovered through adsorption onto EBE-06. The polymer exhibited a 99% gold adsorption rate within 1 h, independent of pH, and a maximum adsorption capacity of 1.787 g of gold per gram of polymer. The desorption process yielded 95% efficiency, with the polymer maintaining 94% efficiency over three cycles of use.

Recently, ABX₃ materials have garnered significant attention due to their diverse applications in photovoltaics, catalysis, and optoelectronics as well as their remarkable efficiency in energy conversion. However, progress has been somewhat slow due to the high expenses of the experiment or the time-consuming density functional theory (DFT) calculation. In this study, we utilized the extreme gradient boosting (XGBoost) algorithm to facilitate the discovery and characterization of ABX₃ compounds based on vast data sets generated by DFT calculations. While the XGBoost algorithm provides a powerful tool for accelerating the discovery of ABX₃ compounds, it is crucial to acknowledge that different DFT approximation levels can significantly impact the predicted band gaps, potentially introducing discrepancies when compared with experimental values. In the first step, we predict the space group of 13947 oxides and halides using the Open Quantum Materials Database and elemental features. Our analysis yields classification accuracies ranging from 82.39% to 99.14% across these materials. Following this, XGBoost regression algorithms are employed to interrogate the data set, enabling predictions of volume (achieving an optimal accuracy of 98.41%, with a mean absolute error (MAE) of 2.395 ų and a root-mean-square error (RMSE) of 4.416 ų), formation energy (an optimal accuracy of 97.36%, with an MAE of 0.075 eV/atom and an RMSE of 0.132 eV/atom), and band gap energy (an optimal accuracy of 87.00%, an MAE of 0.391 eV, and an RMSE of 0.574 eV). Finally, these prediction models are employed to identify the possible space groups for each of the 1252 new ABX₃ formulas. Then, we predict the volume, the formation energy, and the band gap energy for each candidate space group. Through these predictive models, machine learning accelerates the exploration of new materials with enhanced performance and functionality.

The current study focuses on investigating the potential of produced graphene oxide (GO)/oil-based polyurethane composite films as a drug carrier for 5-fluorouracil (5-FU). Polyurethane was synthesized starting from blends of castor oil and sunflower oil-based glyceride, followed by GO and 5-FU anticancer drug bearing film production by solution casting. GO/PU composite film samples were characterized by FTIR, TGA and SEM analysis, confirming the PU production and distribution of 5-FU drug at a homogeneous level in GO/PU films. Experimental design studies were carried out to provide insight into the influence of GO incorporation, the amount of loaded drug, and the release medium pH value on 5-FU release behavior. The amount of 5-FU delivered from GO/PU composites displayed a tendency to increase at high GO ratios and high pH values, with the obtained maximum ratio of 91.4%. From release kinetics studies, the pH-sensitive behavior of GO/PU composites was observed following a Higuchi or zero-order kinetic model depending on the GO ratio, indicating a sustained release of the drug. The in vitro cytotoxicity effect of GO/PU film through 5-FU drug release was confirmed against the MCF-7 human breast cancer cell line, while good biocompatibility of the drug-free GO/PU film against the L-929 mouse fibroblast cell line was confirmed via MTT assay test. Overall, the findings support that produced GO/PU composites hold potential for clinical drug delivery applications as a 5-FU drug carrier.

China has vast proven coal reserves, encompassing a wide variety of types. However, traditional coal classification methods have limitations, often leading to inaccurate classification and inefficient utilization of coal resources. To address this issue, this paper introduces the Extreme Learning Machine (ELM) as a novel coal classification method, based on the near-infrared reflectance spectroscopy (NIRS) of coal. Initially, we collected NIRS data from coal samples using the SVC-HR-1024 spectrometer. Given the high dimensionality and strong linear correlations in NIRS data, we conducted preprocessing to enhance the usefulness of the data. In experiments, the ELM model demonstrated good classification performance. However, due to the random generation of input layer weights and hidden layer biases in the ELM model, its performance can be unstable, preventing the model from fully realizing its potential. To overcome this shortcoming, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the ELM model. Simulation results showed that the PSO-ELM model achieved a 9.68% improvement in classification accuracy compared to the original ELM model. Furthermore, we optimized the PSO algorithm by introducing exponentially decaying inertia factors and position-variant particles to further reduce the risk of the algorithm falling into local optima. The improved Position-Adaptive Inertia PSO-ELM (PAIPSO-ELM) model achieved an additional 2% increase in classification accuracy over the PSO-ELM model, without a significant increase in training time. In summary, this paper proposes a coal spectral classification method based on the PAIPSO-ELM model, effectively overcoming the limitations of traditional classification methods while meeting industrial demands for classification accuracy and speed.

Lanthanide nitride (LnN) materials have garnered significant interest in recent years due to their promising potential as heterogeneous catalysts for green ammonia synthesis under low temperature and pressure reaction conditions. Here, we report on the synthesis of an extended series of lanthanide (Ln) nitride powders (Ln = lanthanum, cerium, neodymium, samarium, gadolinium, terbium, dysprosium, erbium, lutetium) and their structural and vibrational properties. Polycrystalline powders were fabricated using a ball milling mechanochemical process, and their structural properties were assessed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The experimental lattice constants deduced from XRD and TEM were compared with density functional theory-based calculated lattice constants using the Perdew–Burke–Ernzerhof exchange-correlation functional. We show that the calculated lattice constants are within 1–1.5% of the experimental values for the majority of the LnN species─a notable increase in accuracy over prior computational approaches. The frequencies of Raman scattering from the LO(Γ) phonon are reported across the series and compare well with published thin-film data on a smaller selection of the series. As expected, there is a linear relationship between the LO(Γ) phonon frequency and atomic number. Finally, we demonstrate that Raman spectroscopy can be used to detect the presence of a nanoscale oxide layer on the surface of ErN powders.

Amorphous 3 nm large ultrasmall (usTiO₂) and 7 nm large anatase (nTiO₂) nanoparticles (NPs) were successfully prepared and characterized by TEM, FTIR, DRX, UV–vis, and DLS techniques. The MALDI-TOF/MS was shown to be effective in assessing the surface chemistry but fragmentation processes precluded its use for evaluation of particle size distribution. In fact, the laser causes the fragmentation not only of amorphous TiO₂ NPs but also of the material subjected to heat treatment and crystallization at 450 °C for 4 h upon interaction with the DHB matrix and TFA ionizing agent. No significant difference could be observed in the spectrum by varying the particle size, indicating the high stability of the TiO₂ dimer and its low aggregates in the gaseous phase. In short, MALDI-TOF/MS is effective for the direct analysis of nanoparticle surfaces, especially the interaction of functionalizing molecular species with the inorganic components, which in combination with the other techniques demonstrated to be ideal for the detailed characterization of nanomaterials.