Ti2N nitride MXene evokes the Mars-van Krevelen mechanism to achieve high selectivity for nitrogen reduction reaction

We address the low selectivity problem faced by the electrochemical nitrogen (N2) reduction reaction (NRR) to ammonia (NH3) by exploiting the Mars-van Krevelen (MvK) mechanism on two-dimensional (2D) Ti2N nitride MXene. NRR technology is a viable alternative to reducing the energy and greenhouse gas emission footprint from NH3 production. Most NRR catalysts operate by using an associative or dissociative mechanism, during which the NRR competes with the hydrogen evolution reaction (HER), resulting in low selectivity. The MvK mechanism reduces this competition by eliminating the adsorption and dissociation processes at the sites for NH3 synthesis. We show that the new class of 2D materials, nitride MXenes, evoke the MvK mechanism to achieve the highest Faradaic efficiency (FE) towards NH3 reported for any pristine transition metal-based catalyst—19.85% with a yield of 11.33 μg/cm2/hr at an applied potential of − 250 mV versus RHE. These results can be expanded to a broad class of systems evoking the MvK mechanism and constitute the foundation of NRR technology based on MXenes.

Relating the topology of Dirac Hamiltonians to quantum geometry: When the quantum metric dictates Chern numbers and winding numbers

Quantum geometry has emerged as a central and ubiquitous concept in quantum sciences, with direct consequences on quantum metrology and many-body quantum physics. In this context, two fundamental geometric quantities are known to play complementary roles:~the Fubini-Study metric, which introduces a notion of distance between quantum states defined over a parameter space, and the Berry curvature associated with Berry-phase effects and topological band structures. In fact, recent studies have revealed direct relations between these two important quantities, suggesting that topological properties can, in special cases, be deduced from the quantum metric. In this work, we establish general and exact relations between the quantum metric and the topological invariants of generic Dirac Hamiltonians. In particular, we demonstrate that topological indices (Chern numbers or winding numbers) are bounded by the quantum volume determined by the quantum metric. Our theoretical framework, which builds on the Clifford algebra of Dirac matrices, is applicable to topological insulators and semimetals of arbitrary spatial dimensions, with or without chiral symmetry. This work clarifies the role of the Fubini-Study metric in topological states of matter, suggesting unexplored topological responses and metrological applications in a broad class of quantum-engineered systems.

General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification

A major challenge in many machine learning tasks is that the model expressive power depends on model size. Low-rank tensor methods are an efficient tool for handling the curse of dimensionality in many large-scale machine learning models. The major challenges in training a tensor learning model include how to process the high-volume data, how to determine the tensor rank automatically, and how to estimate the uncertainty of the results. While existing tensor learning focuses on a specific task, this paper proposes a generic Bayesian framework that can be employed to solve a broad class of tensor learning problems such as tensor completion, tensor regression, and tensorized neural networks. We develop a low-rank tensor prior for automatic rank determination in nonlinear problems. Our method is implemented with both stochastic gradient Hamiltonian Monte Carlo (SGHMC) and Stein Variational Gradient Descent (SVGD). We compare the automatic rank determination and uncertainty quantification of these two solvers. We demonstrate that our proposed method can determine the tensor rank automatically and can quantify the uncertainty of the obtained results. We validate our framework on tensor completion tasks and tensorized neural network training tasks.

Synthesis of Broad-Specificity Activity-Based Probes for Exo-β-Mannosidases

Exo--mannosidases are a broad class of stereochemically retaining hydrolases that are essential for the breakdown of complex carbohydrate substrates found in all kingdoms of life. Yet the detection of exo--mannosidases...

Lieb's Theorem and Maximum Entropy Condensates

Coherent driving has established itself as a powerful tool for guiding a many-body quantum system into a desirable, coherent non-equilibrium state. A thermodynamically large system will, however, almost always saturate to a featureless infinite temperature state under continuous driving and so the optical manipulation of many-body systems is considered feasible only if a transient, prethermal regime exists, where heating is suppressed. Here we show that, counterintuitively, in a broad class of lattices Floquet heating can actually be an advantageous effect. Specifically, we prove that the maximum entropy steady states which form upon driving the ground state of the Hubbard model on unbalanced bi-partite lattices possess uniform off-diagonal long-range order which remains finite even in the thermodynamic limit. This creation of a `hot' condensate can occur on any driven unbalanced lattice and provides an understanding of how heating can, at the macroscopic level, expose and alter the order in a quantum system. We discuss implications for recent experiments observing emergent superconductivity in photoexcited materials.

Product-form estimators: exploiting independence to scale up Monte Carlo

We introduce a class of Monte Carlo estimators that aim to overcome the rapid growth of variance with dimension often observed for standard estimators by exploiting the target’s independence structure. We identify the most basic incarnations of these estimators with a class of generalized U-statistics and thus establish their unbiasedness, consistency, and asymptotic normality. Moreover, we show that they obtain the minimum possible variance amongst a broad class of estimators, and we investigate their computational cost and delineate the settings in which they are most efficient. We exemplify the merger of these estimators with other well known Monte Carlo estimators so as to better adapt the latter to the target’s independence structure and improve their performance. We do this via three simple mergers: one with importance sampling, another with importance sampling squared, and a final one with pseudo-marginal Metropolis–Hastings. In all cases, we show that the resulting estimators are well founded and achieve lower variances than their standard counterparts. Lastly, we illustrate the various variance reductions through several examples.

Large deviation principle for piecewise monotonic maps with density of periodic measures

We show that a piecewise monotonic map with positive topological entropy satisfies the level-2 large deviation principle with respect to the unique measure of maximal entropy under the conditions that the corresponding Markov diagram is irreducible and that the periodic measures of the map are dense in the set of ergodic measures. This result can apply to a broad class of piecewise monotonic maps, such as monotonic mod one transformations and piecewise monotonic maps with two monotonic pieces.

Conformational Consequences for Compatible Osmolytes on Thermal Denaturation

Compatible osmolytes are a broad class of small organic molecules employed by living systems to combat environmental stress by enhancing the native protein structure. The molecular features that make for a superior biopreservation remain elusive. Through the use of time-resolved and steady-state spectroscopic techniques, in combination with molecular simulation, insight into what makes one molecule a more effective compatible osmolyte can be gained. Disaccharides differing only in their glycosidic bonds can exhibit different degrees of stabilization against thermal denaturation. The degree to which each sugar is preferentially excluded may explain these differences. The present work examines the biopreservation and hydration of trehalose, maltose, and gentiobiose.

Beyond the mean: a flexible framework for studying causal effects using linear models

Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-based models. The proposed method extends the literature, which mainly focuses on causal effects on the mean level and the variance of an outcome variable. For example, we show how to compute the probability that an outcome variable realizes within a target range of values given an intervention, a causal quantity we refer to as the probability of treatment success. We link graph-based causal quantities defined via the do-operator to parameters of the model implied distribution of the observed variables using so-called causal effect functions. Based on these causal effect functions, we propose estimators for causal quantities and show that these estimators are consistent and converge at a rate of $$N^{-1/2}$$ N - 1 / 2 under standard assumptions. Thus, causal quantities can be estimated based on sample sizes that are typically available in the social and behavioral sciences. In case of maximum likelihood estimation, the estimators are asymptotically efficient. We illustrate the proposed method with an example based on empirical data, placing special emphasis on the difference between the interventional and conditional distribution.

ZnFe2O4, a Green and High-Capacity Anode Material for Lithium-Ion Batteries: A Review

Ferrites, a broad class of ceramic oxides, possess intriguing physico-chemical properties, mainly due to their unique structural features, that, during these last 50–60 years, made them the materials of choice for many different applications. They are, indeed, applied as inductors, high-frequency materials, for electric field suppression, as catalysts and sensors, in nanomedicine for magneto-fluid hyperthermia and magnetic resonance imaging, and, more recently, in electrochemistry. In particular, ZnFe2O4 and its solid solutions are drawing scientists’ attention for the application as anode materials for lithium-ion batteries (LIBs). The main reasons are found in the low cost, abundance, and environmental friendliness of both Zn and Fe precursors, high surface-to-volume ratio, relatively short path for Li-ion diffusion, low working voltage of about 1.5 V for lithium extraction, and the high theoretical specific capacity (1072 mA h g−1). However, some drawbacks are represented by fast capacity fading and poor rate capability, resulting from a low electronic conductivity, severe agglomeration, and large volume change during lithiation/delithiation processes. In this review, the main synthesis methods of spinels will be briefly discussed before presenting the most recent and promising electrochemical results on ZnFe2O4 obtained with peculiar morphologies/architectures or as composites, which represent the focus of this review.