Cyanide-Isolated Cobalt Catalyst for Ultraefficient Advanced Oxidation Treatment

Catalyst design with a “Co–N–C” structure at the atomic level has shown great interest for peroxymonosulfate (PMS) activation toward advanced oxidation water treatment. Here, we present an innovative way of producing cobalt hexacyanocobaltate (Co-HCC) with an abundance of atomically isolated CoII–NC sites at the outer surface. This material allows ultraefficient PMS activation to generate plenty of sulfate and hydroxyl radicals, with a turnover frequency much higher than those of most cobalt-based catalysts reported so far and even the homogeneous catalysis by Co2+ ions. We gained fundamental insights on its unprecedently high catalytic performance based on experimental results and computational study. Then, we controlled the growth of Co-HCC on a ceramic membrane to form a confined oxidation environment that utilizes the extended surface area and maximal exposure of short-lived radicals for a fast removal of organic pollutants that enter the pores. As a result, this catalytic membrane achieves complete disruption of micropollutants under a water flux up to 10,000 LMH (merely 0.2 s retention time) and further >90% mineralization of organic pollutants in complex industrial wastewater matrices (<100 s retention time), together with the merits of operational simplicity and great longevity (2 weeks continuous run). Our study elicits a new milestone in “Co–N–C” catalyst structure design for PMS activation and highlights the great interest of producing catalytic membranes for a confined treatment of organic pollutants from partial oxidation to complete mineralization as a new benchmark

The role of a reverse vortex finder for the design of a high performance uniflow cyclone

Swirl vane uniflow cyclones have advantages of a compact structure and low pressure drop but the problem of low particle collection efficiency. In the present study, we demonstrated that the performance of a swirl vane uniflow cyclone can be significantly improved by installing a reverse vortex finder (RVF) behind the swirl vane additionally. It was found that the RVF can act as a vortex stabilizer, which can lower the turbulent kinetic energy and eliminate the central vortex swing phenomenon, resulting in a low pressure drop and increasing the particle collection efficiency. In addition, the RVF can not only increase the particle residence time but also form vortex flow inside the reverse vortex finder resulting in the enhance particle collection efficiency. Finally, we demonstrated that our newly designed swirl vane uniflow cyclone with a RVF has the best performance among various types of cyclones. Copyright © 2022 American Association for Aerosol Research</p

Scatting theory for reflective Fourier ptychographic diffraction tomography

A forward model is presented to an inverse scattering problem that arises in the application of reflective Fourier ptychographic microscopy. The model allows us to determine the 3D distributions of refractive index for weakly scattering semi-transparent objects using Fourier ptychographic tomography. The derivation results show that both transmission types reported previously and reflective type present in this article are corresponding to a specific application in Wolf's work (Emil Wolf, Opt. Commun. 1969 1(4) pp. 153-156). The transmission type measures the forward scattering field while the reflective type measures the backward scattering field. This model is also available for holographic metho

Azide Functional Monolayers Grafted to a Germanium Surface: Model Substrates for ATR-IR Studies of Interfacial Click Reactions

High-quality azide-functional substrates are prepared by a low temperature reaction of 11-bromoundecyltrichlorosilane with UV–ozone-treated germanium ATR-IR plates followed by nucleophilic substitution of the terminal bromine by addition of sodium azide. The resulting monolayer films are characterized by atomic force microscopy (AFM), contact angle analysis, X-ray photoelectron spectroscopy (XPS), attenuated total reflectance infrared spectroscopy (ATR-IR), and ellipsometry. XPS and ellipsometric thickness data correspond well to the results of molecular model calculations confirming the formation of a densely packed azide-functional monolayer. These azide-functional substrates enable interfacial “click” reactions with complementary alkyne-functional molecules to be studied <i>in situ</i> by ATR-IR. To illustrate their potential utility for kinetic studies we show that, in the presence of copper(I) catalyst, the azide-modified surfaces react rapidly and quantitatively with 5-chloro-pentyne to form triazoles via a 1,3-dipolar cycloaddition reaction. Time-resolved ATR-IR measurements indicate that the interfacial click reaction is initially first order in azide concentration as expected from the reaction mechanism, with a rate constant of 0.034 min^–1, and then transitions to apparent second order dependence, with a rate constant of 0.017 min^–1/(chains/nm²), when the surface azide and triazole concentrations become similar, as predicted by Oyama et al. The reaction achieves an ultimate conversion of 50% consistent with the limit expected due to steric hindrance of the 5-chloro-pentyne reactant at the surface

GraphFP is robust to uncertainty presented in cell type labels.

GraphFP was applied to the murine cerebral cortex dataset based on the labelling of 4 cell types with a coarse resolution (a-c) and the labelling of 7 cell types with a fine resolution (d-f), separately. The estimated Φ (a), W (b) and charted probability flow (c) by GraphFP based on the labelling of 4 clusters (“A-Neurons”, “B-Young Neuron”, “C-APs/RPs”, “D-IPs”). Aggregated results of the estimated Φ (d), W (e) and charted probability flow (f) by GraphFP based on the labelling of 7 clusters, averaging the results from i) “3-APs/RPs” and “5-APs/RPs”, ii) “2-Young Neurons” and “6-Young Neurons” and iii) “4-IPs” and “7-IPs”, separately, resulting in the same dimensions as those based on the labelling of 4 cell types.</p

The linear potential energy Φ quantifies cell differentiation potency.

(a) Boxplot of the linear potential energies of cells sampled different time stages of the embryonic murine cerebral cortex development. (b) Trend in the addictive inverse of linear potential (circular points connected by black lines with y axis on left-hand side) and temporal score (triangle points connected by red lines with y axis on right-hand side) across cell types. (c) The linear potential energy Φ estimated by GraphFP. (d) The stationary probability distribution pss of the cell types.</p

GraphFP accurately reconstructs the cell state-transition energy landscape of the murine cerebral cortex dataset.

(a) The gold standard trajectory of embryonic murine cerebral cortex development. (b) The t-SNE plot of cells from the murine cerebral cortex dataset, colored by their cell-type labels. (c) GraphFP estimated the linear potential energy Φ. (d) GraphFP estimated the cell-cell interaction matrix W. (e) Static linear potential energy landscape of cells on the t-SNE plot: cells are color-coded according to the linear potential energies Φs of their corresponding cell types. (f) The free energy (Eq (1)) of the system decreased over time. (g) The reconstructed potential energy landscape Ψ(t) of cell types (colored curves) over time. (h) The potential energies of the cell state pairs with the top 3 highest positive values of cell-cell interaction strengths wijs: “2-Young Neurons ← 1-Neurons” (left panel), “6-Young Neurons ← 3-APs/RPs” (middle panel), and “4-IPs ← 1-Neurons” (right panel). (i) The potential energies of the cell state pairs with the top 3 lowest negative values of cell-cell interaction strengths wijs: “6-Young Neurons ← 1-Neurons” (left panel), “4-IPs ← 3-APs/RPs” (middle panel), and “2-Young Neurons ← 4-IPs” (right panel).</p

GraphFP accurately quantifies the stochastic dynamics of the cell type frequencies by modelling cell-cell interactions.

GraphFP calculated the stochastic dynamics of the cell type frequencies p(t) with cell-cell interaction term (W ≠ 0; solid lines) and without cell-cell interaction term (W = 0; dashed lines). Triangle points are the estimated cell type frequencies at each time point where red represents the input data point to GraphFP, while blue represents the held-out data point to GraphFP. (a) Using all 4 time points as input. (b) Held-out E13.5. (c) Held-out E15.5. (d) Held-out E13.5 and E15.5.</p

Application of GraphFP to the mouse spinal cord injury dataset.

Fig A. GraphFP reconstructs the cell state-transition energy landscape on the mouse spinal cord injury scRNA-seq dataset. Fig B. The linear potential energy Φ quantifies cell differentiation potency. Table A. Evaluation of GraphFP’s performance on quantifying the stochastic dynamics of cell type frequencies with cell-cell interaction term (W ≠ 0) and without cell-cell interaction term (W = 0) on the mouse spinal cord injury dataset. (PDF)</p

Evaluation of GraphFP’s performance on quantifying the stochastic dynamics of cell-type frequencies with cell-cell interaction term (<i>W</i> ≠ 0) and without cell-cell interaction term (<i>W</i> = 0) on the murine cerebral cortex dataset.

Evaluation of GraphFP’s performance on quantifying the stochastic dynamics of cell-type frequencies with cell-cell interaction term (W ≠ 0) and without cell-cell interaction term (W = 0) on the murine cerebral cortex dataset.</p