Nonprecious Triple-Atom Catalysts with Ultrahigh Activity for Electrochemical Reduction of Nitrate to Ammonia: A DFT Screening.

Electrochemical nitrate reduction to ammonia (NORR) is promising to not only tackle environmental issues caused by nitrate but also produce ammonia at room temperatures. However, two critical challenges are the lack of effective electrocatalysts and the understanding of related reaction mechanisms. To overcome these challenges, we employed first-principles calculations to thoroughly study the performance and mechanisms of triple-atom catalysts (TACs) composed of transition metals (including 27 homonuclear TACs and 4 non-noble bimetallic TACs) anchored on N-doped carbon (NC).

Innovative Polymeric Coatings with Dual Antifouling and Light-Activated Bactericidal Functions for Urinary Catheter Applications.

Catheter-associated urinary tract infections (CAUTIs) present significant health risks in medical settings, necessitating innovative solutions to prevent bacterial colonization on catheter surfaces. This study introduces a novel polymeric coating with dual antifouling and light-activated bactericidal properties to enhance the bactericidal efficacy of urinary catheters. The coatings were synthesized using a one-step process involving pyrogallol chemistry to deposit a copolymer composed of zwitterionic sulfobetaine for antifouling and sodium copper chlorophyllin, a photosensitizer that generates reactive oxygen species under light exposure to effectively kill bacteria.

Reaction Mechanism of Rapid CO Electroreduction to Propylene and Cyclopropane (C) over Triple Atom Catalysts.

The carbon monoxide reduction reaction (CORR) toward C and C products such as propylene and cyclopropane can not only reduce anthropogenic emissions of CO and CO but also produce value-added organic chemicals for polymer and pharmaceutical industries. Here, we introduce the concept of triple atom catalysts (TACs) that have three intrinsically strained and active metal centers for reducing CO to C products. We applied grand canonical potential kinetics (GCP-K) to screen 12 transition metals (M) supported by nitrogen-doped graphene denoted as M3N7, where M stands for Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, and Au.

A High-Entropy Single-Atom Catalyst Toward Oxygen Reduction Reaction in Acidic and Alkaline Conditions.

The design of high-entropy single-atom catalysts (HESAC) with 5.2 times higher entropy compared to single-atom catalysts (SAC) is proposed, by using four different metals (FeCoNiRu-HESAC) for oxygen reduction reaction (ORR). Fe active sites with intermetallic distances of 6.

Mitochondria-Associated Transcriptome Profiling via Localizable Aggregation-Induced Emission Photosensitizers in Live Cells.

In recent decades, there has been increasing interest in studying mitochondria through transcriptomic research. Various exogenous fusion protein-based proximity labeling methods have been reported that focus on the site of one particular protein/peptide and might also influence the corresponding localization or interactome. To enable unbiased and high spatial-resolution profiling of mitochondria-associated transcriptomes in live cells, a flexible RNA proximity labeling approach was developed using aggregation-induced emission (AIE) type photosensitizers (PSs) that possess great mitochondria-targeting capabilities.

Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach.

Cyclometalated Pt(II) complexes are popular phosphorescent emitters with color-tunable emissions. To render their practical applications as organic light-emitting diodes emitters, it is required to develop Pt(II) complexes with high radiative decay rate constant and photoluminescence (PL) quantum yield. Here, a general protocol is developed for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield based on the combination of first-principles quantum mechanical method, machine learning, and experimental calibration.

Construct exchange-correlation functional via machine learning.

Density functional theory has been widely used in quantum mechanical simulations, but the search for a universal exchange-correlation (XC) functional has been elusive. Over the last two decades, machine-learning techniques have been introduced to approximate the XC functional or potential, and recent advances in deep learning have renewed interest in this approach. In this article, we review early efforts to use machine learning to approximate the XC functional, with a focus on the challenge of transferring knowledge from small molecules to larger systems.

Mechanical transmission of dengue virus by Aedes aegypti may influence disease transmission dynamics during outbreaks.

Background: Dengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible.

[Regulatory effect of moxibustion on LTB4/MMP-9 in serum of patients with rheumatoid arthritis].

Objective: To observe the effects of moxibustion on the contents of leukotriene B4 (LTB4), interleukin-17 (IL-17), tumor necrosis factor-α (TNF-α) and matrix metalloproteinase -9 (MMP-9) in serum, and explore the protection mechanisms of moxibustion in the patients with rheumatoid arthritis (RA).

Methods: A total of 64 patients with RA were randomly divided into treatment group (=31) and control group (=33). The patients in the control group were treated with conventional medication for consecutive 5 weeks.

A semilocal machine-learning correction to density functional approximations.

Machine learning (ML) has demonstrated its potential usefulness for the development of density functional theory methods. In this work, we construct an ML model to correct the density functional approximations, which adopts semilocal descriptors of electron density and density derivative and is trained by accurate reference data of relative and absolute energies. The resulting ML-corrected functional is tested on a comprehensive dataset including various types of energetic properties.

Surface Lattice Modulation through Chemical Delithiation toward a Stable Nickel-Rich Layered Oxide Cathode.

Nickel-rich layered oxides (NLOs) are considered as one of the most promising cathode materials for next-generation high-energy lithium-ion batteries (LIBs), yet their practical applications are currently challenged by the unsatisfactory cyclability and reliability owing to their inherent interfacial and structural instability. Herein, we demonstrate an approach to reverse the unstable nature of NLOs through surface solid reaction, by which the reconstructed surface lattice turns stable and robust against both side reactions and chemophysical breakdown, resulting in improved cycling performance. Specifically, conformal La(OH) nanoshells are built with their thicknesses controlled at nanometer accuracy, which act as a Li capturer and induce controlled reaction with the NLO surface lattices, thereby transforming the particle crust into an epitaxial layer with localized Ni/Li disordering, where lithium deficiency and nickel stabilization are both achieved by transforming oxidative Ni into stable Ni.

Self-Consistent-Charge Density-Functional Tight-Binding Parameters for Modeling an All-Solid-State Lithium Battery.

All-solid-state lithium-ion batteries have been a promising solution for next-generation energy storage due to their safety and potentially high energy density. In this work, we developed a density-functional tight-binding (DFTB) parameter set for modeling solid-state lithium batteries, focusing on the band alignment at electrolyte/electrode interfaces. Despite DFTB being widely applied in the simulation of large-scale systems, parametrization is usually done for single materials, and less attention is paid to band alignment among multiple materials.

High-performance plasmonic polymer modulators through mode hybridization and electro-thermomechanical effects.

In this work, an electro-optical polymer modulator with double-layered gold nanostrips, a polymer nanograting, and a metal substrate is proposed and designed. Interestingly, mode hybridization between the Fabry-Pérot (F-P) and anti-bonding modes is formed, and strongly depends on the nanograting size, which can be controllably modulated by an injection current. The simulation and calculation results show that the temperature sensitivity and large structural sensitivity for the polymer modulator could remain constant during the current-tuning process, and a near-zero reflectance and a low linewidth of 13.

Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation.

The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density.

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.

Carrier-Envelope-Phase Modulated Currents in Scanning Tunneling Microscopy.

Carrier-envelope-phase (CEP) stable optical pulses combined with state-of-the-art scanning tunneling microscopy (STM) can track and control ultrafast electronic tunneling currents. On the basis of nonequilibrium Green's function formalism, we present a time and frequency domain theoretical study of CEP-stable pulse-induced tunneling currents between an STM tip and a metal substrate. It is revealed that the experimentally observed phase shift between the maximum tunneling current and maximum electric field is caused by the third-order response to the electric field.

Vibration-mediated resonant charge separation across the donor-acceptor interface in an organic photovoltaic device.

Examination of a recent open-system Ehrenfest dynamics simulation suggests that a vibration-mediate resonance may play a pivotal role in the charge transfer across a donor-acceptor interface in an organic solar cell. Based on this, a concise dissipative two-level electronic system coupled to a molecular vibrational mode is proposed and solved quantum mechanically. It is found that the charge transfer is enhanced substantially when the vibrational energy quanta is equal to the electronic energy loss across the interface.

Capillary electrophoresis-based methodology for screening of oligonucleotide aptamers.

As a new molecular recognition element, oligonucleotide aptamer not only has higher affinity and specificity to target molecules, but also has the advantages of wide recognition range, in vitro synthesis and chemical stability compared with conventional antibodies. Since a kind of screening method termed systematic evolution of ligands by exponential enrichment (SELEX) was reported, scientists have extensively researched the methodology of how to highly and efficiently screen out aptamers from a library consisting of a large number of random oligonucleotides. Certainly capillary electrophoresis-based screening methodologies, including nonequilibrium capillary electrophoresis of equilibrium mixtures, equilibrium capillary electrophoresis of equilibrium mixtures, non-SELEX, ideal-filter capillary electrophoresis, capillary transient isotachophoresis, etc.

Coherent Real-Space Charge Transport Across a Donor-Acceptor Interface Mediated by Vibronic Couplings.

There is growing experimental and theoretical evidence that vibronic couplings, couplings between electronic and nuclear degrees of freedom, play a fundamental role in ultrafast excited-state dynamics in organic donor-acceptor hybrids. Whereas vibronic coupling has been shown to support charge separation at donor-acceptor interfaces, so far, little is known about its role in the real-space transport of charges in such systems. Here we theoretically study charge transport in thiophene:fullerene stacks using time-dependent density functional tight-binding theory combined with Ehrenfest molecular dynamics for open systems.

Toward the Exact Exchange-Correlation Potential: A Three-Dimensional Convolutional Neural Network Construct.

A deep neural network is constructed to yield in principle exact exchange-correlation potential. It requires merely the electron densities of small molecules and ions and yet can determine the exchange-correlation potentials of large molecules. We train and validate the neural network based on the data for H and HeH and subsequently determine the ground-state electron density of stretched HeH, linear H, and H-He-He-H.

STM Imaging of Electron Migration in Real Space and Time: A Simulation Study.

Using a simulation protocol that mimics ultrafast scanning tunneling microscopy (STM) experiments, we demonstrate how pump-probe ultrafast STM may be used to image electron migration in molecules. Two pulses are applied to a model system, and the time-integrated current through the tip is calculated versus the delay time and tip position to generate STM images. With suitable pump and probe parameters, the images can track charge migration with atomistic spatial and femtosecond temporal resolutions.