Nanozyme Rich in Oxygen Vacancies Derived from Mn-Based Metal–Organic Gel for the Determination of Alkaline Phosphatase

Vacancy engineering as an effective strategy has been widely employed to regulate the enzyme–mimic activity of nanomaterials by adjusting the surface, electronic structure, and creating more active sites. Herein, we purposed a facile and simple method to acquire transition metal manganese oxide rich in oxygen vacancies (OVs-Mn2O3-400) by pyrolyzing the precursor of the Mn(II)-based metal–organic gel directly. The as-prepared OVs-Mn2O3-400 exhibited superior oxidase-like activity as oxygen vacancies participated in the generation of O2•–. Besides, steady state kinetic constant (Km) and catalytic kinetic constant (Ea) suggested that OVs-Mn2O3-400 had a stronger affinity toward 3,3′,5,5′-tetramethylbenzidine and possessed prominent catalytic performance. By taking 2-phospho-l-ascorbic acid as the substrate, which can be converted into reducing substance ascorbic acid in the presence of alkaline phosphatase (ALP), OVs-Mn2O3-400 can be applied as an efficient nanozyme for ALP colorimetric analysis without the help of destructive H2O2. The colorimetric sensor established by OVs-Mn2O3-400 for ALP detection showed a good linearity from 0.1 to 12 U/L and a lower limit of detection of 0.054 U/L. Our work paves the way for designing enhanced enzyme-like activity nanozymes, which is of significance in biosensing

Nanozymes from Cu(II) Metal–Organic Gel and Melamine for Highly Active Peroxidase-Like Activity to Detect Alkaline Phosphatase

Enzyme-like catalytic activity and efficiency of carbon-based nanomaterials are closely linked to their size, heteroatom composition, and structure, and hence the heteroatom regulation needs to be further explored. In this work, a simple and efficient strategy was proposed to develop Cu-doped 2D carbon material C3N4 (Cu-C3N4-550) with excellent catalytic performance by pyrolyzing precursors of Cu(II) metal–organic gel (MOG) and melamine directly. Due to its sufficient metal active sites and adequate specific surface areas, the as-prepared Cu-C3N4-550 was endowed with excellent peroxidase-like activity to promote the oxidation of 3,3′,5,5′-tetramethylbenzidine owing to the generation of •OH in the catalytic reaction. It was amazing to find that the peroxidase mimic activity of the prepared Cu-C3N4-550 has enhanced 32.3-fold compared with bare C3N4. High peroxidase-like activity of Cu-C3N4-550 was influenced severely by the addition of antioxidant ascorbic acid (AA), alkaline phosphatase (ALP) as a typical hydrolase could catalyze substrate 2-phospho-l-ascorbic acid into AA, while AA was capable of capturing •OH generated from the catalytic reaction of Cu-C3N4-550. Hence, a sensitive, selective, and colorimetric method for the detection of ALP was established, the linear concentration of ALP in this colorimetric sensor from 0.4 to 20 U/L was acquired with a low detection limit of 0.32 U/L. This work not only provides ideas for designing enhanced peroxidase-like activity nanozymes in practical biological analysis but also broadens the MOG derivatives and carbon-based nanomaterials in colorimetric applications

Isobaric Vapor–Liquid Equilibria and Extractive Distillation Process Design for Separating Ethanol and Diethoxymethane

The purification of diethoxymethane (DEM) is a challenge because unreacted ethanol forms an azeotrope with DEM in the acetalization reaction of formaldehyde with ethanol to produce DEM. In this work, extractive distillation with dimethyl sulfoxide (DMSO) as an entrainer was adopted to separate the mixture of ethanol and DEM. Isobaric vapor–liquid equilibrium data of ethanol + DEM and DEM + DMSO were measured at 101.3 kPa, and the thermodynamic consistency of the experimental data was tested by the Fredenslund and van Ness test methods. The activity coefficient models, which are nonrandom two-liquid (NRTL), universal quasichemical (UNIQUAC), and Wilson models, were successfully applied to correlate the experimental data. Then, the feasibility of separating the azeotrope with DMSO as the entrainer, the operating region and the separation sequence were studied by thermodynamic topological analysis. The extractive distillation process was designed and optimized by the sequential iterative procedure based on the total annual cost (TAC). The results show that DMSO can be used to separate ethanol and DEM by extractive distillation

Vibrational energy redistribution and vibrational dynamics of methanol mixed with Rhodamine 101 dye

Vibrational energy transfer that occurs after photoexcitation can be tracked in the first several femtoseconds by ultrafast time- and frequency-resolved CARS (coherent anti-Stokes Raman scattering) spectroscopy. Vibrational energy transfer from high-frequency modes to lower ones through chemical bond of pure methanol and methanol/Rhodamine 101 (Rh101) solution is detected. Through comparison and analysis of the experimental results, it is found that surrounding molecules have a significant influence on vibrational energy transfer and vibrational couplings among relevant modes.</p

Improving measurement efficiency of complex transistor structures using surface plasmon resonances

Mueller matrix spectroscopy ellipsometry (MMSE) combined with machine learning method is a promising metrology tool for assisting high-volume production of complex transistor structures such as Gate-all-around (GAA). Conventional wide-spectrum MMSE cannot avoid measurement errors caused by large dispersions. In the meantime, the large amount of data in wide-spectrum reduces the metrology efficiency. In this work, a new metrology method is developed using the effects of near-field electric fields. First, two important transistor structures within the GAA architecture are used as metrology objects, including isotropic and anisotropic structures. The near-field electric fields and Mueller matrix spectra based on different dimensions are calculated using rigorous coupled wave analysis. Next, machine learning method has been used to compare the training time and test accuracy of the wide-spectrum (0.2 ~ 1.5 μm) with that of the sensitive spectrum (the bands where surface plasmon resonances or localized plasmonic resonances occur). The training time for the sensitive spectrum is shortened by about 60%, with the same test accuracy. The proposed method improves the metrology efficiency of complex transistor structures and reduces the dispersion error caused by wide spectrum, thus improving the reliability of high-density integration

Video_1_Integrate prediction of machine learning for single ACoA rupture risk: a multicenter retrospective analysis.MP4

BackgroundStatistically, Anterior communicating aneurysm (ACoA) accounts for 30 to 35% of intracranial aneurysms. ACoA, once ruptured, will have an acute onset and cause severe neurological dysfunction and even death. Therefore, clinical analysis of risk factors related to ACoA and the establishment of prediction model are the benefits to the primary prevention of ACoA.MethodsAmong 1,436 cases of single ACoA patients, we screened 1,325 valid cases, classified risk factors of 1,124 cases in the ruptured group and 201 cases in the unruptured group, and assessed the risk factors, respectively, and predicted the risk of single ACoA rupture by using the logistic regression and the machine learning.ResultsIn the ruptured group (84.8%) of 1,124 cases and the unruptured group (15.2%) of 201 cases, the multivariable logistic regression (MLR) model shows hemorrhagic stroke history (OR 95%CI, p:0.233 (0.120–0.454),ConclusionWe combined the analysis of MLR, RF, and PCA models to conclude that hemorrhagic stroke history and gender affect single ACoA rupture. The RF model with web dynamic nomogram, allows for real-time personalized analysis based on different patients’ conditions, which is a tremendous advantage for the primary prevention of single ACoA rupture.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=178501.</p