Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports ...demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis.
The utilization of physical organic molecular descriptors for the quantitative description of reaction outcomes in multivariate linear regression models is demonstrated as an effective tool for
a priori
prediction and mechanistic interrogation.
Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature representations. ...Nevertheless, their ability in modeling relations between the samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or nongrid) data representation and analysis. In this article, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new minibatch GCN (called miniGCN hereinafter), which allows to train large-scale GCNs in a minibatch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without retraining networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batchwise network training (enabling the combination of CNNs and GCNs), we explore three fusion strategies: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS data sets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regard to the single CNN or GCN models. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_GCN for the sake of reproducibility.
•MnOx/biochar and FeOx/biochar significantly improved ozonization of ATZ.•Enhanced formation of •OH was observed in the catalytic ozonation systems.•Increased Lewis acid sites and electron transfer ...contributed for the O3 catalyzation.•Toxicity of ATZ had been largely eliminated after the heterogeneous catalytic ozonation.
After reaction with permanganate or ferrate, the resulted Mn-loaded and Fe-loaded biochar (MnOx/biochar and FeOx/biochar) exhibited excellent catalytic ozonation activity. O3 (2.5 mg/L) eliminated 48% of atrazine (ATZ, 5 μM) within 30 min at pH 7.0, while under identical conditions, ozonation efficiency of ATZ increased to 83% and 100% in MnOx/biochar and FeOx/biochar (20 mg/L) heterogeneous catalytic systems, respectively. Radical scavenger experiment and electron paramagnetic resonance (EPR) analysis confirmed that hydroxyl radical (•OH) was the dominant oxidant. Total Lewis acid sites on MnOx/biochar and FeOx/biochar were 3.5 and 4.1 times of that on the raw biochar, which induced enhanced adsorption of O3 and its subsequent decomposition into •OH. Electron transfer via redox pairs on MnOx/biochar and FeOx/biochar was observed by cyclic voltammetry scans, which also functioned in the improved catalytic capacity. Degradation pathways of ATZ in MnOx/biochar and FeOx/biochar ozonation systems were proposed, with 34.6% and 44.8% of dechlorination effect accomplished within 30 min of reaction, which was improved by 4.1 and 5.3 times compared to pure ozonation. After 12-hour treatment, acute toxicity of ATZ oxidation products was reduced from 38.3% of pure ozonation system to 14.5% and 6.3% of activated ozonation systems with MnOx/biochar and FeOx/biochar, respectively. Mn-loaded biochar and Fe-loaded biochar have great potential for heterogeneous catalytic ozonation of polluted water.
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Selective electrocatalytic reduction of NO to ammonia provides a promising way to remove pollutant NO and realize green ammonia synthesis under ambient conditions. The key to promote the practical ...application of NO reduction reaction (NORR) is to develop economical and efficient electrocatalysts to replace Pt-based catalysts. In this work, we designed a series of transition metal (TM) single-atom catalysts (SACs) supported on BP monolayer with a B vacancy (TM@BP, TM = Ti ~ Zn, Zr ~ Ag, and Hf ~ Au), and systematically investigated their performance of electrocatalytic NORR by means of density functional theory (DFT). Taking the changes of free energy of the first (NO→NOH/NO→NHO) and last (OH → H2O/NH2 → NH3) hydrogenation steps of NORR as the screening criteria, seven TM@BP (TM = Ti, V, Cu, Rh, Ag, Ir, and Au) were selected from eighteen candidates. All possible reaction pathways of NORR to produce NH3 and byproducts (N2O, N2 and H2) were considered. Our calculated results show that Ti@BP, V@BP, Cu@BP, and Au@BP not only exhibit similar catalytic activity to Pt-based catalysts, but also have excellent NORR selectivity toward NH3, so they are predicted to be the most promising catalysts. This work provides useful guidance for the design and exploration of efficient NORR electrocatalysts.
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•NORR on 23 TM single-atoms anchored on defective BP monolayer is studied.•7 potential SACs are screened out by a two-steps screening strategy.•Ti, V, Cu, Rh, Ag, Ir, Au@BP show high NORR catalytic activity.•Ti, V, Cu, Au@BP can well inhibit the formation of by-products N2O, N2, and H2.
Arctigenin (ARG), a natural lignans compound isolated from Arctium lappa L. In this study, the anti-tumor effect of ARG on prostate cancer cell PC-3M and the mechanism of apoptosis and autophagy ...induced by phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling pathway were discussed, and further confirmed by the joint treatment of ARG and PI3K inhibitor LY294002. Here, the effect of ARG on cell viability was evaluated in PC-3M cells by Cell Counting Kit-8 reagent (CCK-8) assay. After the treatment of ARG, colony formation assay was used to detect the anti-proliferation effect. Annexin V-fluoresceine isothiocyanate/propidium iodide (FITC/PI) kit and 4′,6-diamidino-2-phenylindole (DAPI) staining were used to detect the apoptosis level, and cell cycle changes were analyzed by flow cytometry. The expression of autophagy was detected by acridine orange staining. In addition, the expression levels of apoptosis and autophagy-related proteins were analyzed by Western blot. The result showed that different concentrations of ARG inhibited the proliferation of PC-3M cells. DAPI staining and flow cytometry showed that ARG induced PC-3M cell apoptosis and arrested cell in G0/G1 phase. Acridine orange staining showed that ARG induced autophagy in PC-3M cells. Western blot experiments showed that ARG inhibited the expression of Bcl-2, promoted the expression of Bax and cleaved caspase-3. At the same time, the expression of autophagy-related proteins LC3B-II and Beclin-1 increased after ARG treatment, but P62 decreased. In addition, further studies have shown that treatment with LY294002 enhanced the effects of ARG on the expression of proteins associated with apoptosis and autophagy, indicating that ARG may induce apoptosis and autophagy through PI3K/Akt/mTOR pathway.
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Carbon dioxide electroreduction (CO2ER) presents a promising strategy for environmentally friendly CO2 utilization due to its low energy consumption. Single-atom nanozymes (SANs), ...amalgamating the benefits of single-atom catalysts and nanozymes, have become a hot topic in catalysis. Inspired by the intricate structure of cytochrome P450, we designed 81 sandwich-like SANs using Group-VIII transition metals (TMN4-S-TM’N4) and evaluated their performance in CO2ER using density functional theory (DFT). Our investigation revealed that most SANs display superior catalytic activity and improved specific product selectivity in comparison to the Cu (211) surface. Notably, IrN4-S-TMN4 (TM = Co, Rh, Pd) exhibited selective CO2 reduction to CO with remarkable limiting potentials (UL) of −0.11, −0.07, and −0.09 V, respectively, demonstrating potential as artificial CO dehydrogenases. Furthermore, RuN4-S-RuN4 exhibited formate dehydrogenase-like activity, resulting in selective production of HCOOH at a UL of −0.10 V. Machine learning analysis elucidated that the exceptional activity and selectivity of these SANs stemmed from precise modulation of electron density on sulfur atoms, achieved by varying transition metals in the subsurface. Our research not only identifies exceptional SANs for CO2ER but also provides insights into innovative methods for regulating non-bonding interactions and achieving sustainable CO2 conversion.
Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding ...fractional abundances. In recent years, nonnegative matrix factorization (NMF)-based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely, the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of the previous methods ignore another important insightful property possessed by a natural hyperspectral image (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on the previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying an HSI and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we first consider such prior in HSI by encoding it as the non-local total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the blind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on an alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.
In recent years, the rapidly growing attention on MXenes makes the material a rising star in the 2D materials family. Although most researchers' interests are still focused on the properties of bare ...MXenes, little attention has been paid to the surface chemistry of MXenes and MXene‐based nanocomposites. To this end, this Review offers a comprehensive discussion on surface modified MXene‐based nanocomposites for energy conversion and storage (ECS) applications. Based on the structure and reaction mechanism, the related synthesis methods toward MXenes are briefly summarized. After the discussion of existing surface modification techniques, the surface modified MXene‐based nanocomposites and their inherent chemical principles are presented. Finally, the application of these surface modified nanocomposites for supercapacitors (SCs), lithium/sodium–ion batteries (LIBs/SIBs), and electrocatalytic water splitting is discussed. The challenges and prospects of MXene‐based nanocomposites for future ECS applications are also presented.
Recently, MXenes have gained increasing attention in the field of energy conversion and storage (ECS). Meanwhile, the unique surface chemistry of MXenes endows them with great potential in the construction of 2D based nanocomposites. To this end, the present work offers a comprehensive summary of surface modified MXene‐based nanocomposites for ECS applications.
Electrochemical CO2 reduction into value-added fuels and chemicals is regarded as a highly efficient way to achieve a carbon neutral cycle. Recently, two-dimensional metal-organic frameworks (2D ...MOFs) have attracted much attention in CO2 reduction reaction (CO2RR). Herein, we employed density functional theory (DFT) to study the catalytic performance of 48 kinds of the π-d conjugated 2D layered MOFs, i.e., TM-BHX, composed of transition metal ions and multidentate organic ligands, such as benzenehexaol (BHO), benzenehexathiol (BHT) and benzenehexaselenolate (BHS), for CO2RR. By investigating the thermodynamic stability and electrochemical stability, conductivity and the free energy change of the first hydrogenation step (CO2 + H+ + e− → ∗COOH or CO2 + H+ + e− → ∗HCOO), nine TM-BHX were selected from 48 MOFs, including TM-BHT (TM = Cr, Fe, Co, Ru, Rh, Ir) and TM-BHS (TM = Ru, Rh, Ir). Possible reaction pathways of CO2 reduction into C1 products were explored to determine the CO2RR mechanism. Our results showed that among 9 candidates, Cr-, Fe-, Co-BHT, and Ir–BHS not only exhibit high activity with low limiting potential (−0.30, −0.29, 0, and −0.49 V, respectively), but also have high CO2RR selectivity with the positive value of UL(CO2) – UL(H2), so they are promising CO2RR electrocatalysts. This work provides a new kind of 2D MOFs as efficient CO2RR electrocatalysts for experimental research.
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•48 2D MOFs with TM-X4 moieties were designed as electrocatalysts for CO2 reduction.•9 candidates were screened out from 48 TM-BHX by a rational strategy.•The formation mechanism of CO2 to all C1 products was explored and compared with HER.•Cr-, Fe-, Co-BHT and Ir–BHS show high catalytic activity and selectivity for CO2RR.
Melatonin (N‐acetyl‐5‐methoxytryptamine) plays important roles in plant defences against a variety of biotic and abiotic stresses, including UV‐B stress. Molecular mechanisms underlying functions of ...melatonin in plant UV‐B responses are poorly understood. Here, we show that melatonin effect on molecular signalling pathways, physiological changes and UV‐B stress resistance in Arabidopsis. Both exogenous and endogenous melatonin affected expression of UV‐B signal transduction pathway genes. Experiments using UV‐B signalling component mutants cop1‐4 and hy5‐215 revealed that melatonin not only acts as an antioxidant to promote UV‐B stress resistance, but also regulates expression of several key components of UV‐B signalling pathway, including ubiquitin‐degrading enzyme (COP1), transcription factors (HY5, HYH) and RUP1/2. Our findings indicate that melatonin delays and subsequently enhances expression of COP1, HY5, HYH and RUP1/2, which act as central effectors in UV‐B signalling pathway, thus regulating their effects on antioxidant systems to protect the plant from UV‐B stress.
Several studies have demonstrated that melatonin plays a role in UV‐B responses, however, the molecular mechanism whereby melatonin affects the UV‐B pathway was not clear. This study examined the function of melatonin in molecular signaling pathways, physiological changes, and UV‐B stress resistance under UV‐B radiation in Arabidopsis. Exogenous melatonin treatment experiment indicated that melatonin could enhance the transcriptional level of genes on UV‐B signaling pathway and ameliorate ROS damage caused by UV‐B stress. This result was verified in SNAT overexpressing lines and knock‐down mutant.
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