Adsorption of molecular oxygen on doped graphene: atomic, electronic and magnetic properties

Adsorption of molecular oxygen on B-, N-, Al-, Si-, P-, Cr- and Mn-doped graphene is theoretically studied using density functional theory in order to clarify if O2 can change the possibility of using doped graphene for gas sensors, electronic and spintronic devices. O2 is physisorbed on B-, and Ndoped graphene with small adsorption energy and long distance from the graphene plane, indicating the oxidation will not happen; chemisorption is observed on Al-, Si-, P-, Cr- and Mn-doped graphene. The local curvature caused by the large bond length of X-C (X represents the dopants) relative to CC bond plays a very important role in this chemisorption. The chemisorption of O2 induces dramatic changes of electronic structures and localized spin polarization of doped graphene, and in particular, chemisorption of O2 on Cr-doped graphene is antiferromagnetic. The analysis of electronic density of states shows the contribution of the hybridization between O and dopants is mainly from the p or d orbitals. Furthermore, spin density shows that the magnetization locates mainly around the doped atoms, which may be responsible for the Kondo effect. These special properties supply a good choice to control the electronic properties and spin polarization in the field of graphene engineering.Comment: 7 pages, 10 figure

The proof of a conjecture on largest Laplacian and signless Laplacian H-eigenvalues of uniform hypergraphs

Let $\mathcal{A(}G\mathcal{)},\mathcal{L(}G\mathcal{)}$ and $\mathcal{Q(}G\mathcal{)}$ be the adjacency tensor, Laplacian tensor and signless Laplacian tensor of uniform hypergraph $G$, respectively. Denote by $\lambda (\mathcal{T})$ the largest H-eigenvalue of tensor $\mathcal{T}$. Let $H$ be a uniform hypergraph, and $H^{\prime}$ be obtained from $H$ by inserting a new vertex with degree one in each edge. We prove that $\lambda(\mathcal{Q(}H^{\prime}\mathcal{)})\leq\lambda(\mathcal{Q(}H\mathcal{)}).$ Denote by $G^{k}$ the $k$th power hypergraph of an ordinary graph $G$ with maximum degree $\Delta\geq2$. We will prove that $\{\lambda(\mathcal{Q(}$ is a strictly decreasing sequence, which imply Conjectrue 4.1 of Hu, Qi and Shao in \cite{HuQiShao2013}. We also prove that $\lambda(\mathcal{Q(}G^{k}\mathcal{)})$ converges to $\Delta$ when $k$ goes to infinity. The definiton of $k$th power hypergraph $G^{k}$ has been generalized as $G^{k,s}.$ We also prove some eigenvalues properties about $\mathcal{A(}G^{k,s}\mathcal{)},$ which generalize some known results. Some related results about $\mathcal{L(}G\mathcal{)}$ are also mentioned

On the α\alpha-index of minimally kk-(edge-)connected graphs for small kk

Let $G$ be a graph with adjacency matrix $A(G)$ and let $D(G)$ be the diagonal matrix of vertex degrees of $G$. For any real $\alpha \in [0,1]$, Nikiforov defined the $A_\alpha$-matrix of a graph $G$ as $A_\alpha(G)=\alpha D(G)+(1-\alpha)A(G)$. The largest eigenvalue of $A_\alpha(G)$ is called the $\alpha$-index or the $A_\alpha$-spectral radius of $G$. A graph is minimally $k$-(edge)-connected if it is $k$-(edge)-connected and deleting any arbitrary chosen edge always leaves a graph which is not $k$-(edge)-connected. In this paper, we characterize the minimally 2-edge-connected graphs and minimally 3-connected graph with given order having the maximum $\alpha$-index for $\alpha \in [\frac{1}{2},1)$, respectively.Comment: arXiv admin note: text overlap with arXiv:2301.0338

Bearing fault diagnosis based on active learning and random forest

Bearing plays an important role in rotating machineries and has received increasing attention in diagnosis of its faults accurately. This paper proposes a fault diagnosis approach exploiting active learning (AL) based on random forest (RF), which can perform accurate bearing fault diagnosis with most valuable samples. First, feature vectors are obtained by empirical mode decomposition (EMD) process for original vibration signals and selected as input of the system. Second, samples with highest uncertainty are selected through AL and added to the training set to train RF classifier. Finally, trained RF is employed to perform classification for bearing faults with testing set. Experimental results demonstrate that the proposed approach can effectively and accurately identify typical bearing faults

The post-Paris approach to mitigating Arctic warming—perspectives from shipping emissions reduction

The availability of increased Arctic shipping as a consequence of sea ice decline is a regional issue that is closely linked with international climate governance and global governance of the maritime industry. Sea ice decline creates favorable circumstances for the development of merchant shipping, but is accompanied by increases in greenhouse gas emissions. Reduction of greenhouse gas emissions from the shipping industry is of utmost importance to prevent the destruction of the fragile Arctic ecosystem. This paper focuses on the core content of the Paris Agreement and suggests that the International Maritime Organization could guide the shipping industry to reach a fair agreement with states that includes market-based measures, capacity building, and voluntary actions of shipping companies as non-state actors