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Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, with topologically protected linear electronic band crossings hosting massless chiral fermions. Here, we demonstrate a topological phase transition facilitated by light-induced shear motion of a single atomic layer at the surface of bulk WTe$_2$, thereby opening the door to nanoscale device concepts. Ultrafast terahertz fields enhanced at the apex of an atomically sharp tip resonantly couple to the key interlayer shear mode of WTe$_2$ via a ferroelectric dipole at the interface, inducing a structural phase transition at the surface to a metastable state. Subatomically resolved differential imaging, combined with hybrid-level density functional theory, reveals a shift of 7 $\pm$ 3 picometres in the top atomic plane. Tunnelling spectroscopy links electronic changes across the phase transition with the electron and hole pockets in the band structure, suggesting a reversible, light-induced annihilation of the topologically-protected Fermi arc surface states in the top atomic layer.

Many ligands commonly used to prepare nanoparticle catalysts with precise nanoscale features contain nitrogen (e.g., oleylamine); here, we found that the use of nitrogen-containing ligands during the synthesis of metal oxide nanoparticle catalysts substantially impacted product analysis during photocatalytic studies. We confirmed these experimental results via hybrid Density Functional Theory computations of the materials' electronic properties to evaluate their viability as photocatalysts for nitrogen reduction. This nitrogen ligand contamination, and subsequent interference in photocatalytic studies, is avoidable through the careful design of synthetic pathways that exclude nitrogen-containing constituents. This result highlights the urgent need for careful evaluation of catalyst synthesis protocols, as contamination by nitrogen-containing ligands may go unnoticed since the presence of nitrogen is often not detected or probed., Comment: 13 pages, 3 figures

In the quest for better energy storage solutions, the role of designing effective electrodes is crucial. Previous research has shown that using materials like single-side fluorinated graphene can improve the stability of ion insertion in few-layer graphene (FLG), which is vital as we move beyond lithium-ion batteries. Alternatives such as sodium and potassium, which are more abundant on Earth, appear promising, but thorough studies on how these ions insert into electrodes in stages are still needed. Our research focuses on the initial three alkali (Li, Na, K) and alkaline (Be, Mg, Ca) earth metals. Using Density Functional Theory (DFT) with advanced calculations, we've investigated how these ions interact with modified graphene at various stages of insertion. This method provides more precise electrical data and has helped us understand the complex interactions involved. Specifically, we found a new site for ion insertion that is energetically favorable. We also explored how modifying the graphene surface affects ions of different sizes and charges and examined how the number of graphene layers influences these interactions. Our discoveries are crucial for developing new materials that could replace lithium-ion batteries and provide a foundation for adjusting electrical properties in battery design through ion staging and surface modifications.

In this paper, a synergistic computational/experimental approach is presented for the rapid discovery and characterization of novel alloys within the compositionally complex (i.e., "medium/high entropy") refractory alloy space of Ti-V-Nb-Mo-Hf-Ta-W. This was demonstrated via a material design cycle aimed at simultaneously maximizing the objective properties of high specific hardness (hardness normalized by density) and high specific elastic modulus (elastic modulus normalized by density). This framework utilizes high-throughput computational thermodynamics and intelligent filtering to first reduce the untenably large alloy space to a feasible size, followed by an iterative design cycle comprised of high-throughput synthesis, processing, and characterization in batch sizes of 24 alloys. After the first iteration, Bayesian optimization was utilized to inform selection of the next batch of 24 alloys. This paper demonstrates the benefit of using batch Bayesian optimization (BBO) in material design, as significant gains in the objective properties were observed after only two iterations or "shots" of the design cycle without using any prior knowledge or physical models of how the objective properties relate to the design inputs (i.e., composition). Specifically, the hypervolume of the Pareto front increased by 54% between the first and second iterations. Furthermore, 10 of the 24 alloys in the second iteration dominated all alloys from the first iteration.

Layered two-dimensional (2D) materials, particularly MX$_2$ transition metal dichalcogenides (TMDCs), where M represents Mo or W and X is S, Se, or Te, exhibit unique properties when configured into hetero- and homo-bilayers with diverse stacking arrangements. This study emphasizes the impact of "magic angles" in twisted homobilayers that lead to transformative Moir\'e patterns, crucial for manipulating material properties. Our computational analysis of heterostructures covered 30 combinations from six MX$_2$ types, revealing that both the materials' choice and stacking significantly affect stability and band gap energy (E$_g$). Notably, the MoTe$_2$/WSe$_2$ heterostructure with a 60$^{\circ}$ twist demonstrates a direct E$_g$, underscoring its potential in novel applications. In homobilayers, both fully relaxed and low-strain scenarios were examined across various stackings and twists. We found that optimal bilayers' E$_g$ is highly dependent on stacking alignment. Particularly, 1T-MoS$_2$, WS$_2$, and WSe$_2$ can exhibit direct or indirect E$_g$ under specific twisted conditions, with 1T-MoS$_2$ showing a capability to transition between semiconductor and conductor states. Specific "magic angles" at 17.9$^{\circ}$ and 42.1$^{\circ}$ in twisted WS$_2$ and WSe$_2$ bilayers form symmetrical Moir\'e patterns, enabling fine-tuning of E$_g$ through angle adjustments and influencing electronic band flatness. Compared to monolayers, these TMDC bilayers display enhanced stability, which can be strategically adjusted. The use of controlled twisted angles in these bilayers emerges as a powerful tool in materials engineering, offering precise control over various electronic properties.

This paper focuses on the parameterization of the multipath propagation model (MPM) for indoor broadband power line communications (PLC), which up to now has been established in an heuristic way. The MPM model was originally proposed for outdoor channels in the band up to 20 MHz, but its number of parameters becomes extremely large when used to model indoor channel frequency responses (CFR), which are much more frequency-selective than outdoor ones, and the band is extended to 80 MHz. This work proposes a fitting procedure that addresses this problem. It allows determining the model parameters that yield the best fit to each channel of a large database of single-input single-output (SISO) experimental measurements acquired in typical home premises of different European countries. Then, the properties of the MPM parameters are analyzed. The study unveils the relation between the model parameters and the main characteristics of the actual CFR like the frequency selectivity and the average attenuation. It also estimates the probability density function (PDF) of each parameter and proposes a fitting distribution for each of them. Moreover, the relationship among the main parameters of the model is also explored. Provided results can be helpful for the development of MPM-based models for indoor broadband PLC., Comment: 9 pages, 7 figures

We present a highly efficient workflow for designing semiconductor structures with specific physical properties, which can be utilized for a range of applications, including photocatalytic water splitting. Our algorithm generates candidate structures composed of earth-abundant elements that exhibit optimal light-trapping, high efficiency in \ce{H2} and/or \ce{O2} production, and resistance to reduction and oxidation in aqueous media. To achieve this, we use an ionic translation model trained on the Inorganic Crystal Structure Database (ICSD) to predict over thirty thousand undiscovered semiconductor compositions. These predictions are then screened for redox stability under Hydrogen Evolution Reaction (HER) or Oxygen Evolution Reaction (OER) conditions before generating thermodynamically stable crystal structures and calculating accurate band gap values for the compounds. Our approach results in the identification of dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis, offering a significant advancement toward the conversion of sunlight into chemical fuels.

Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a learning-based NDT for network simulators. The proposed method offers a holistic representation of information flow in a wireless network by integrating node, edge, and path embeddings. Through this approach, the model is trained to map the network configuration to KPIs in a single forward pass. Hence, it offers a more efficient alternative to traditional simulation-based methods, thus allowing for rapid experimentation and optimization. Our proposed method has been extensively tested through comprehensive experimentation in various scenarios, including wired and wireless networks. Results show that it outperforms baseline learning models in terms of accuracy and robustness. Moreover, our approach achieves comparable performance to simulators but with significantly higher computational efficiency.

The storage of hydrogen gas (H$_2$) has presented a significant challenge that has hindered its use as a fuel source for transportation. To meet the Department of Energy's ambitious goals of achieving $50$ g L$^{-1}$ volumetric and $6.5$ wt \% gravimetric uptake targets, materials-based approaches are essential. Designing materials that can efficiently store hydrogen gas requires careful tuning of the interactions between the gaseous H$_2$ and the surface of the material. Metal-Organic Frameworks (MOFs) and Covalent-Organic Frameworks (COFs) have emerged as promising materials due to their exceptionally high surface areas and tunable structures that can improve gas-framework interactions. However, weak binding enthalpies have limited the success of many current candidates, which fail to achieve even $10$ g L$^{-1}$ volumetric uptake at ambient temperatures. To overcome this challenge, We utilized quantum mechanical (QM) based force fields (FF) to investigate the uptake and binding enthalpies of 3 linkers chelated with 7 different transition metals (TM), including both precious metals (Pd and Pt) and first row TM (Co, Cu, Fe, Ni, Mn), to design 24 different COFs in-silico. By applying QM-based FF with grand canonical Monte Carlo (GCMC) from 0-700 bar and 298 K, We demonstrated that Co-, Ni-, Mn-, Fe-, Pd-, and Pt-based MSUCOFs can already achieve the Department of Energy's hydrogen storage targets for 2025. Surprisingly, the COFs that incorporated the more affordable and abundant first-row TM often outperformed the precious metals. This promising development brings us one step closer to realizing a hydrogen-based energy economy.

Detecting a non-zero electric dipole moment (EDM) in a particle would unambiguously signify physics beyond the Standard Model. A potential pathway towards this is the detection of a nuclear Schiff moment, the magnitude of which is enhanced by the presence of nuclear octupole deformation. However, due to the low production rate of isotopes featuring such "pear-shaped" nuclei, capturing, detecting, and manipulating them efficiently is a crucial prerequisite. Incorporating them into synthetic diamond optical crystals can produce defects with defined, molecule-like structures and isolated electronic states within the diamond band gap, increasing capture efficiency, enabling repeated probing of even a single atom, and producing narrow optical linewidths. In this study, we used density functional theory (DFT) to investigate the formation, structure, and electronic properties of crystal defects in diamond containing $^{229}$Pa, a rare isotope that is predicted to have an exceptionally strong nuclear octupole deformation. In addition, we identified and studied stable lanthanide-containing defects with similar electronic structures as non-radioactive proxies to aid in experimental methods. Our findings hold promise for the existence of such defects and can contribute to the development of a quantum information processing-inspired toolbox of techniques for studying rare isotopes.

The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellite imagery for this task, various works have applied deep neural networks for predicting multispectral images in photorealistic quality. However, the important area of vegetation dynamics has not been thoroughly explored. Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe. Our multi-modal transformer model Contextformer leverages spatial context through a vision backbone and predicts the temporal dynamics on local context patches incorporating meteorological time series in a parameter-efficient manner. The GreenEarthNet dataset features a learned cloud mask and an appropriate evaluation scheme for vegetation modeling. It also maintains compatibility with the existing satellite imagery forecasting dataset EarthNet2021, enabling cross-dataset model comparisons. Our extensive qualitative and quantitative analyses reveal that our methods outperform a broad range of baseline techniques. This includes surpassing previous state-of-the-art models on EarthNet2021, as well as adapted models from time series forecasting and video prediction. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predicting vegetation health and behaviour in response to climate variability and extremes., Comment: Accepted at CVPR 2024. We provide open source code and pre-trained weights to reproduce our experimental results under https://github.com/vitusbenson/greenearthnet

Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days for just one sample. In this manner, the most promising candidate samples could be extracted from an extensive database and subjected to further in-depth analysis. Despite their broad applicability, it can be challenging to apply ML methods to a given chemical problem since a multitude of design decisions must be made, such as the molecular descriptor to use or the optimizer to train the model. Here we present a methodology for the meaningful exploration of a given molecular problem through classification experiments. This conceptually simple methodology results in transferable insight on the selected problem and can be used as a platform from which prediction difficulty is estimated, molecular representations are tested and refined, and more precise or ambitious projects can be undertaken. Physicochemical insight can also be obtained. This methodology is illustrated through the use of multiple molecular descriptors for the prediction of enthalpy, Gibbs' free energy, zero-point vibrational energy, and constant-volume calorific capacity of the molecules from the public database QM9 [Ramakrishnan2014] with 133,885 organic molecules. A noteworthy result is that for the classification problem we propose, the low-resolution descriptor `atomic composition' [Tchagang2019] can reach a classification rate almost on par with the high-resolution `sorted Coulomb matrix' [Rupp2012,Montavon2012,Hansen2013] ($>90\%$), provided that an appropriate optimizer is used during training., Comment: 24 pages, 9 figures

The spin current density functional theory (SCDFT) is the generalization of the standard DFT to treat a fermionic system embedded in the effective external field produced by the spin-orbit coupling interaction. Even in the absence of a spin polarization, the SCDFT requires the electron-electron potential to depend on the spin currents $\mathbf{J}^x$, $\mathbf{J}^y$ and $\mathbf{J}^z$, which only recently was made possible for practical relativistic quantum-mechanical simulations [Phys. Rev. B {\bf 102}, 235118 (2020)]. Here, we apply the SCDFT to the quantum spin-Hall phase and show how it improves (even qualitatively) the description of its electronic structure relative to the DFT. We study the Bi (001) 2D bilayer and its band insulator to topological insulator phase transition (via $s+p_z \leftrightarrow p_x +ip_y$ band inversion) as a function of mechanical strain. We show that the explicit account of spin currents in the electron-electron potential of the SCDFT is key to the appearance of a Dirac cone at the $\Gamma$ point in the valence band structure at the onset of the topological phase transition. Finally, the valence band structure of this system is rationalized using a simple first-order $\mathbf{k} \cdot \mathbf{p}$ quasi-degenerate perturbation theory model., Comment: 5 pages, 2 figures

We study invariants associated to Smale spaces obtained from an expanding endomorphism on a (closed connected Riemannian) flat manifold. Specifically, the relevant invariants are the $K$-theory of the associated $C^*$-algebras and Putnam's homology theory for Smale spaces. The latter is isomorphic to the groupoid homology of the groupoids used to construct the $C^*$-algebras., Comment: 21 pages

We introduce and characterize the independent fluctuating two-ray (IFTR) fading model, a class of fading models consisting of two specular components which fluctuate independently, plus a diffuse component modeled as a complex Gaussian random variable. The IFTR model complements the popular fluctuating two-ray (FTR) model, on which the specular components are fully correlated and fluctuate jointly. The chief probability functions of the received SNR in IFTR fading, including the PDF, CDF and MGF, are expressed in closed-form, having a functional form similar to other state-of-the-art fading models. Then, the IFTR model is empirically validated using multiple channels measured in rather diverse scenarios, including line of sight (LOS) millimeter-wave, land mobile satellite (LMS) and underwater acoustic communication (UAC), showing a better fit than the original FTR model and other models previously used in these environments. Additionally, the performance of wireless communication systems operating under IFTR fading is evaluated in closed-form in two scenarios: (i) exact and asymptotic bit error rate for a family of coherent modulations; and (ii) exact and asymptotic outage probability., Comment: 11 pages, 8 figures. arXiv admin note: text overlap with arXiv:1611.05063

We explore the operad of finite posets and its algebras. We use order polytopes to investigate the combinatorial properties of zeta values. By generalizing a family of zeta value identities, we demonstrate the applicability of this approach. In addition, we offer new proofs of some of Ramanujan's results on the properties of Eulerian numbers, interpreting his work as dealing with series inheriting the algebraic structure of disjoint unions of points. Finally, we establish a connection between our findings and the linear independence of zeta values., Comment: We discovered how to generalize our results to arbitrary finite posets. We explain the relationship between our work and linear independence of zeta values

The nuclear waste problem is one of the main interests of the rare earth and actinide elements chemistry. Studies of Actinide-containing compounds are at the frontier of the applications of current theoretical methods due to the need to consider relativistic effects and approximations to the Dirac equation in them. Here, we employ four-component relativistic quantum calculations and scalar approximations to understand the contribution of f-type atomic orbitals in the chemical bonding of actinides (Ac) to organic ligands. We studied the relativistic quantum structure of an isostructural family made of Plutonium (Pu), Americium (Am), Californium (Cf), and Berkelium (Bk) atoms with the redox-active model ligand; DOPO (2,4,6,8-tetra-tert-butyl-1-oxo-1H-phenoxazin-9-olate). Crystallographic structures were available to validate our calculations for all mentioned elements except for Cf. In short, state-of-the-art relativistic calculations were performed at different levels of theory to investigate the relativistic effects and electron correlations on geometrical structures and bonding energies of $Ac$-DOPO$_3$ complexes ($Ac$=Pu, Am, Cf, Bk) : 1) the scalar relativistic zeroth order regular approximation (ZORA) within the hybrid density functional theory (DFT) and 2) the four-component Dirac equation with the Dirac-Hartree-Fock (4c-DHF) and L\'evy-Leblond (LL) Hamiltonians. We show that scalar DFT-ZORA could be used as an efficient theoretical approximation to first approximate the geometry and electronic properties of actinides which are difficult to synthesize or characterize; but knowing that the higher levels of theory, like the 4c-DHF, gives closer results to experiments than the scalar DFT-ZORA. We also performed spin-free calculations of geometric parameters for the Americium and Berkelium compounds.

Relativistic deformed kinematics are usually considered as a way to capture residual effects of a fundamental quantum gravity theory. These kinematics present a non-commutative addition law for the momenta, so that the total momentum of a multi-particle system depends on the specific ordering in which the momenta are composed. We explore in the present work how this property may be used to generate an asymmetry between particles and antiparticles through a particular ordering prescription, resulting in a violation of CPT symmetry. We study its consequences for muon decay, obtaining a difference in the lifetimes of the particle and the antiparticle as a function the new high-energy scale parameterizing such a relativistic deformed kinematics., Comment: 13 pages. Version published in Symmetry (prior to journal proofs); the article belongs to the Special Issue "Symmetry, CPT and Astroparticles"

We establish the correspondence between two apparently unrelated but in fact complementary approaches of a relativistic deformed kinematics: the geometric properties of momentum space and the loss of absolute locality in canonical spacetime, which can be restored with the introduction of a generalized spacetime. This correspondence is made explicit for the case of $\kappa$-Poincar\'e kinematics and compared with its properties in the Hopf algebra framework., Comment: 18 pages

In this paper, we study the flow of signals through linear paths with the nonlinear condition that a node emits a signal when it receives external stimuli or when two incoming signals from other nodes arrive coincidentally with a combined amplitude above a fixed threshold. Sets of such nodes form a polychrony group and can sometimes lead to cascades. In the context of this work, cascades are polychrony groups in which the number of nodes activated as a consequence of other nodes is greater than the number of externally activated nodes. The difference between these two numbers is the so-called profit. Given the initial conditions, we predict the conditions for a vertex to activate at a prescribed time and provide an algorithm to efficiently reconstruct a cascade. We develop a dictionary between polychrony groups and graph theory. We call the graph corresponding to a cascade a chinampa. This link leads to a topological classification of chinampas. We enumerate the chinampas of profits zero and one and the description of a family of chinampas isomorphic to a family of partially ordered sets, which implies that the enumeration problem of this family is equivalent to computing the Stanley-order polynomials of those partially ordered sets., Comment: 32 pages. We changed the exposition, removed unfinished work, and added bibliography. To appear on "Algorithms"

An approach to study a generalization of the classical-quantum transition for general systems is proposed. In order to develop the idea, a deformation of the ladder operators algebra is proposed that contains a realization of the quantum group $SU(2)_q$ as a particular case. In this deformation Planck's constant becomes an operator whose eigenvalues approach $\hbar $ for small values of $n$ (the eigenvalue of the number operator), and zero for large values of $n$ (the system is classicalized)., Comment: 20 pages, no figures

The storage of hydrogen (H$_2$) is of economic and ecological relevance, because it could potentially replace petroleum-based fuels. However, H$_2$ storage at mild condition remains one of the bottlenecks for its widespread usage. In order to devise successful H$_2$ storage strategies, there is a need for a fundamental understanding of the weak and elusive hydrogen interactions at the quantum mechanical level. One of the most promising strategies for storage at mild pressure and temperature is physisorption. Porous materials are specially effective at physisorption, however the process at the quantum level has been under-studied. Here, we present quantum calculations to study the interaction of H$_2$ with building units of porous materials. We report 240 H$_2$ complexes made of different transition metal (Tm) atoms, chelating ligands, spins, oxidation states, and geometrical configurations. We found that both the dispersion and electrostatics interactions are the major contributors to the interaction energy between H$_2$ and the transition metal complexes. The binding energy for some of these complexes is in the range of at least 10 kJ/mol for many interactions sites, which is one of these main requirements for practical H$_2$ storage. Thus, these results are of fundamental nature for practical H$_2$ storage in porous materials., Comment: 5 pages, 5 figures

We investigate the effect of lattice disorder and local correlation effects in finite and periodic silicene structures caused by carbon doping using first-principles calculations. For both finite and periodic silicene structures, the electronic properties carbon-doped monolayers are dramatically changed by controlling the doping sites in the structures, which is related to the amount of disorder introduced in the lattice and electron-electron correlation effects. By changing the position of the carbon dopants, we found that a Mott-Anderson transition is achieved. Moreover, the band gap is determined by the level of lattice disorder and electronic correlation effects. Finally, these structures are ferromagnetic even under disorder which has potential applications in Si-based nanoelectronics, such as field-effect transistors (FETs)., Comment: 10 pages, 6 figures

This study examines the potential of superconductivity in transition metal (TM) intercalated bilayer graphene through a systematic study of the electronic and magnetic properties. We determine the electronic structure for all first row TM elements in the stable honeycomb configuration between two layers of graphene using density functional theory (DFT). Through an analysis of the electron density, we assess the induction of the magnetic moment in each case, where a comparison of the ferromagnetic and antiferromagnetic configurations allow us to ascertain an estimated exchange coupling between the transition-metal elements. By analyzing the electronic properties, we find that the carbon $p$-bands are degenerate with the TM $d$-bands and form an electron pocket below the Fermi energy at the $\Gamma-$point. These hybridized bands are analogous to the carbon $p$-band effect that produces superconductivity in intercalated graphite with alkali and alkaline-earth metals. Furthermore, since the bands are hybridized with the TM $d$-bands, their magnetic properties may provide bosonic modes from their spin-coupling to preserve the unique linear dispersion present in monolayer graphene. This study provides a designing route by using TMs for tuning magneto-electric Dirac materials and will encourage future experimental studies to further the fundamental knowledge of unconventional superconductivity.

Orthogonal frequency division multiplexing (OFDM) signals with rectangularly windowed pulses exhibit low spectral confinement. Two approaches usually referred to as pulse-shaping and active interference cancellation (AIC) are classically employed to reduce the out-of-band emission (OOBE) without affecting the receiver. This paper proposes a spectral shaping method that generalizes and unifies these two strategies. To this end, the OFDM carriers are shaped with novel pulses, referred to as generalized pulses, that consist of the ones used in conventional OFDM systems plus a series of cancellation terms aimed at reducing the OOBE of the former. Hence, each generalized pulse embeds all the terms required to reduce its spectrum in the desired bands. This leads to a data-independent optimization problem that notably simplifies the implementation complexity and allows the analytical calculation of the resulting power spectral density (PSD), which in most methods found in the literature can only be estimated by means of simulations. As an example of its performance, the proposed technique allows complying with the stringent PSD mask imposed by the EN 50561-1 with a data carrier loss lower than 4%. By contrasts, 28% of the data carriers have to be nulled when pulse-shaping is employed in this scenario.

Dangerous radioactive waste leftover from the Cold War era nuclear weapons production continues to contaminate sixteen sites around the United States. Although many challenges and obstacles exist in decontaminating these sites, two particularly difficult tasks associated with cleanup of this waste are extracting and separating actinide elements from the remainder of the solution, containing other actinide elements and non actinide elements. Developing effective methods for performing these separations is possible by designing new chelating agents that form stable complexes with actinide elements, and by investigating the interactions between the chelating agents and the actinide elements. In this work, new chelating agents (or ligands) with potential to facilitate the separation of radioactive waste are designed for Th, Pa, and U using relativistic Density Functional Theory (DFT) inconjunction with a high-throughput algorithm. We show that both methodologies can be combined efficiently to accelerate discovery and design of new ligands for separation of the radioactive actinides. The main hypothesis that we test with this approach is that the strength of secondary coordination sphere (SCS) can be tuned to increase the selectivity of binding with different actinides. More specifically, we show that links that connect two of the catecholamide ligands via covalent interactions are then added to increase the overall stability of the complex. The effect of increase in the selectivity is also observed when non-covalent interaction is used between ligands. We apply this approach for Th, Pa, and U, and discover linkers that can be used with other ligands., Comment: 22 pages, 13 figures, Supporting information (5 pages)

This paper proposes a novel approach to the statistical characterization of non-central complex Gaussian quadratic forms (CGQFs). Its key strategy is the generation of an auxiliary random variable (RV) that converges in distribution to the original CGQF. Since the mean squared error between both is given in a simple closed-form formulation, the auxiliary RV can be particularized to achieve the required accuracy. The technique is valid for both definite and indefinite CGQFs and yields simple expressions of the probability density function (PDF) and the cumulative distribution function (CDF) that involve only elementary functions. This overcomes a major limitation of previous approaches, in which the complexity of the resulting PDF and CDF prevents from using them for subsequent calculations. To illustrate this end, the proposed method is applied to maximal ratio combining systems over correlated Rician channels, for which the outage probability and the average bit error rate are derived.

The tetraaza [Co$^\text{II}$N$_4$H]$^{2+}$ complex (\textbf{1}) is remarkable for its ability to selectively reduce CO$_2$ to CO with 45\% Faradaic efficiency and a CO to H$_2$ ratio of 3:2. We employ density functional theory (DFT) to determine the reasons behind the unusual catalytic properties of \textbf{1} and the most likely mechanism for CO$_2$ reduction. The selectivity for CO$_2$ over proton reduction is explained by analyzing the catalyst's affinity for the possible ligands present under typical reaction conditions: acetonitrile, water, CO$_2$, and bicarbonate. After reduction of the catalyst by two electrons, formation of [Co$^\text{I}$N$_4$H]$^{+}$-CO$_{2}^{-}$ is strongly favored. Based on thermodynamic and kinetic data, we establish that the only likely route for producing CO from here consists of a protonation step to yield [Co$^\text{I}$N$_4$H]$^{+}$-CO$_{2}$H, followed by reaction with CO$_2$ to form [Co$^\text{II}$N$_4$H]$^{2+}$-CO and bicarbonate. This conclusion corroborates the idea of a direct role of CO$_2$ as a Lewis acid to assist in {C-O} bond dissociation, a conjecture put forward by other authors to explain recent experimental observations. The pathway to formic acid is predicted to be forbidden by high activation barriers, in accordance with the products that are known to be generated by \textbf{1}. Calculated physical observables such as standard reduction potentials and the turnover frequency for our proposed catalytic cycle are in agreement with available experimental data reported in the literature. The mechanism also makes a prediction that may be experimentally verified: that the rate of CO formation should increase linearly with the partial pressure of CO$_2$., Comment: 8 pages, 2 figures

The oxygen reduction (ORR) and oxygen evolution reactions (OER) in Zn-air batteries (ZABs) require highly efficient, cost-effective and stable electrocatalysts as replacements to traditionally high cost, inconsistently stable and low poison resistant Platinum group metals (PGM) catalysts. Although, nitrogen-doped carbon nanotube (NCNT) arrays have been developed over recent decades through various advanced technologies are now capable of catalyzing ORR efficiently, their underdeveloped bifunctional property, hydrophobic surface, and detrimental preparation strategy are found to limit practical large-scale commercialization for effective rechargeable ZABs. Here, we have demonstrated fabrication of a three-dimensional (3D) nickel foam supported NCNT arrays with CoNi nanoparticles (NPs) encapsulated within the apical domain (denoted as CoNi@NCNT/NF) that exhibits excellent bifunctional catalytic performance toward both ORR (onset potential of 0.97 V vs. RHE) and OER (overpotential of 1.54 V vs. RHE at 10 mA cm$^{-2}$). We further examined the practicability of this CoNi@NCNT/NF material being used as an air electrode for rechargeable ZAB coin cell and pouch cell systems. The ZAB coin cell showed a peak power density of 108 mW cm$^{-2}$ with an energy density of 845 Wh kg$_{Zn}^{-1}$ and robust rechargeability over 28h under ambient conditions, which exceeds the performance of PGM catalysts and leading non-PGM electrocatalysts. In addition, density functional theory (DFT) calculations revealed that the ORR and OER catalytic performance of the CoNi@NCNT/NF electrode are mainly derived from the d-orbitals from the CoNi NPs encapsulated within the apical dominant end of the NCNTs., Comment: 49 pages, 7 figures

Modern nanoscience has focused on two-dimensional (2D) layer structure materials which have garnered tremendous attention due to their unique physical, chemical and electronic properties since the discovery of graphene in 2004. Recent advancement in graphene nanotechnology opens a new avenue of creating 2D bilayer graphene (BLG) intercalates. Using first-principles DFT techniques, we have designed 20 new materials \textit{in-silico} by intercalating first row transition metals (TMs) with BLG, i.e. 10 layered structure and 10 bulk crystal structures of TM intercalated in BLG. We investigated the equilibrium structure and electronic properties of layered and bulk structure BLG intercalated with first row TMs (Sc-Zn). The present DFT calculations show that the 2$p_z$ sub-shells of C atoms in graphene and the 3$d_{yz}$ sub-shells of the TM atoms provide the electron density near the Fermi level controlling the material properties of the BLG-intercalated materials. This article highlights how the Dirac point moves in both the BLG and bulk-BLG given a different TM intercalated materials. The implications of controllable electronic structure and properties of intercalated BLG-TM for future device applications are discussed. This work opens up new avenues for the efficient production of two-dimensional and three-dimensional carbon-based intercalated materials with promising future applications in nanomaterial science., Comment: 60 pages, 9 figures. arXiv admin note: text overlap with arXiv:1701.03936 by other authors

High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the zigzag and armchair directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies., Comment: 25 pages, 6 figures

Covalent-organic frameworks (COFs) are intriguing platforms for designing functional molecular materials. Here, we present a computational study based on van der Waals dispersion-corrected hybrid density functional theory (DFT-D) to design boroxine-linked and triazine-linked COFs intercalated with Fe. Keeping the original $P-6m2$ symmetry of the pristine COF (COF-Fe-0), we have computationally designed seven new COFs by intercalating Fe atoms between two organic layers. The equilibrium structures and electronic properties of both the pristine and Fe-intercalated COF materials are investigated here. We predict that the electronic properties of COFs can be fine tuned by adding Fe atoms between two organic layers in their structures. Our calculations show that these new intercalated-COFs are promising semiconductors. The effect of Fe atoms on the electronic band structures and density of states (DOSs) has also been investigated using the aforementioned DFT-D method. The contribution of the $d$-subshell electron density of the Fe atoms plays an important role in improving the semiconductor properties of these new materials. These intercalated-COFs provide a new strategy to create semi-conducting materials within a rigid porous network in a highly controlled and predictable manner., Comment: 39 pages. arXiv admin note: text overlap with arXiv:1703.02613