Polythiophene-coated carbon nano boxes for efficient platinum-based catalysts for methanol electrooxidation.

To improve the efficiency of the methanol oxidation reaction (MOR) in direct methanol fuel cells (DMFCs), it is essential to develop catalysts with high catalytic activity. However, constructing polyatomic doped carbon nanomaterials and understanding the interaction mechanisms between dopant elements remain significant challenges. In this study, we propose nitrogen-doped carbon nanobox (CNB) derived from Zeolitic Imidazolate Framework-67 (ZIF-67) crystals as precursors to serve as carriers for highly efficient platinum nanoparticles (Pt NPs). We synthesized platinum/poly(3,4-propylenedioxythiophene)/carbon nanobox (Pt/PProDOT/CNB) composites by wrapping CNB around PProDOT films via in situ oxidative polymerization. This unique structural design provides several advantages to the catalyst, including a large active surface area, numerous accessible electrocatalytic active centers, an optimized electronic structure, and good electronic conductivity. The Pt/PProDOT/CNB composites demonstrated excellent methanol oxidation performance, with a remarkable mass activity (MA) of 1639.9 mA mg-1Pt and a high electrochemical active surface area (ECSA) of 160.8 m2/g. Furthermore, the catalyst exhibited good CO resistance and outstanding durability.

Understanding the Photoinduced Desorption and Oxidation of CO on Ru(0001) Using a Neural Network Potential Energy Surface.

The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (Te, Tl)-AIMDEF addresses such complex modeling, it imposes severe computational costs, hindering quantitative comparison with experimental desorption probabilities. In order to bypass this limitation, we utilize the embedded atom neural network method to construct a potential energy surface (PES) for the coadsorption of CO and O on Ru(0001). Our results demonstrate that this PES not only reproduces the short-time ab initio dynamics but is also able to yield statistically significant data for long lasting trajectories that correlate well with experimental findings. Furthermore, the analysis of the laser-induced dynamics reveals the existence of a dynamic trapping state that acts as a precursor for CO desorption, and it is not observed under thermal conditions. Altogether, our results validate the underlying theoretical framework, providing robust support for the description of not only the photoinduced desorption but also the oxidation of CO in terms of nonequilibrated but thermal hot electrons and phonons.

Self-Powered UV Photodetector Construction of the P(EDOS-TTh) Copolymer-Modified ZnO Nanoarray.

To solve the problem that zinc oxide nanorods (ZnO NRs)-based self-powered ultraviolet (UV) photodetectors cannot obtain both higher responsiveness and shorter response time, P(EDOS-TTh) was prepared using 3,4-ethylenedioxyselenphene (EDOS) and terthiophene (TTh) as copolymers, which modify the ZnO NRs surface, and the ZnO/P(EDOS-TTh) P-N junction self-powered UV device is assembled. The effect of the number of electrochemical polymerization cycles on the UV photodetection performance of ZnO/P(EDOS-TTh) P-N heterojunction was studied by adjusting the number of electrochemical polymerization cycles at the monomer molar ratio of 1:1. Benefiting from the enhanced built-in electric field of the ZnO/P(EDOS-TTh) interface, balancing photogenerated carriers, and charge separation and transport. The results show that the contact between N-type ZnO NRs and P-type P(EDOS-TTh) is best when the number of polymerization cycles is 3, due to the fact that EDOS-TTh and ZnO NRs form excellent P-N heterojunctions with strong internal electric fields, and the devices show good pyroelectric effect and UV photodetection performance. Under 0 V bias and 0.32 mW/cm2 UV irradiation, the responsivity (R) of ZnO/P(EDOS-TTh) reaches 3.31 mA/W, the detectivity (D*) is 7.25 × 1010 Jones, and the response time is significantly shortened. The rise time is 0.086 s, which exhibited excellent photoelectric properties and stability. UV photodetection performance with high sensitivity and fast response time is achieved.

Multicoverage Study of Femtosecond Laser-Induced Desorption of CO from Pd(111).

We study the strong coverage dependence of the femtosecond laser-induced desorption of CO from Pd(111) using molecular dynamics simulations that consistently include the effect of the laser-induced hot electrons on both the adsorbates and surface atoms. Adiabatic forces are obtained from a multicoverage neural network potential energy surface that we construct using data from density functional theory calculations for 0.33 and 0.75 monolayer (ML). Our molecular dynamics simulations performed for these two trained coverages and an additional intermediate coverage of 0.60 ML reproduce well the peculiarities of the experimental findings. The performed simulations also permit us to disentangle the relative role played by the excited electrons and phonons on the desorption process and discover interesting properties of the reaction dynamics as the relevance that the precursor physisorption well acquires during the dynamics as coverage increases.

Highly sensitive electrochemical sensing of norfloxacin by molecularly imprinted composite hollow spheres.

Poly (3,4-ethylenedioxythiophene) (PEDOT)-based molecularly imprinted electrochemical sensors have attracted widespread attention for monitoring contaminants in food and the environment. However, there are still problems such as poor hydrophilicity, easy agglomeration, and low selectivity in its preparation. In this work, a novel molecularly imprinted composite hollow sphere was prepared by a molecular imprinting technique using nitrogen-doped hollow carbon spheres as matrix material, and PEDOT and poly(methacrylic acid) as monomers. The selective binding capabilities and mechanism of the material to norfloxacin (NOR) were systematically investigated. Then the material-based sensor was constructed, and its electrochemical detection performance toward NOR was thoroughly studied. The sensor exhibited a wide linear range (0.0005-31 µM), a low detection limit (0.061 nM), satisfactory immunity to interference and stability. Besides, the sensor displayed better sensitivity and reliability (spiked recoveries of 98.0-105.2%, relative standard deviation of 3.45-5.69%) for detecting NOR in lake water, honey, and milk than high-performance liquid chromatography. This work provides a new strategy for developing poly(3,4-ethylenedioxythiophene)-based molecularly imprinted electrochemical sensors.

Poly (3, 4-propylenedioxythiophene)/Hollow carbon sphere composites supported Pt NPs to facilitate methanol oxidation reactions.

Direct methanol fuel cells (DMFCs) are thought of as portable, sustainable, and non-polluting energy devices. The exploration of efficient and affordable catalysts for the methanol oxidation reaction (MOR) is significant for the industrial application of DMFCs. In this study, nitrogen-doped hollow carbon spheres (HCS) derived from polydopamine were proposed for the catalyst support for platinum nanoparticles (Pt NPs) for serving as the anode catalyst for DMFCs, and a composite support material was fabricated by in-situ oxidation of 3,4-ethylenedioxythiophene (ProDOT) with HCS to get core-shell structured poly(3,4-propylenedioxythiophene) (PProDOT)-embellished hollow carbon spheres (HCS) (PProDOT/HCS) for further improving the catalytic activity for supported catalyst. The results indicated that the platinum (Pt) on the surface of HCS was well dispersed, and the Pt became smaller and more evenly distributed with the introduction of PProDOT. Simultaneously, the Schottky junction formed between PProDOT and Pt NPs contributes to enhanced charge transfer and catalytic activity of the catalyst. Notably, the core-shell structure of the ternary catalyst, its excellent charge transfer capability, and the interaction between platinum and the support contribute to its high electrocatalytic activity. Electrochemical tests demonstrated that the PProDOT/HCS/Pt catalyst exhibited a mass activity of 1169.6 mA mg-1Pt for methanol oxidation in acidic electrolytes, surpassing the activity of the HCS/Pt catalyst (472.4 mA mg-1Pt) and commercial Pt/C (281.0 mA mg-1Pt).

Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing.

Atomistic neural network potentials have achieved great success in accelerating atomistic simulations in complicated systems in recent years. They are typically based on the atomic decomposition of total properties, truncating the interatomic correlations to a local environment within a given cutoff radius. A more recently developed message passing (MP) neural network framework can, in principle, incorporate nonlocal effects through iteratively correlating some atoms outside the cutoff sphere with atoms inside, a process referred to as MP. However, how the model accuracy depends on the cutoff radius and the MP process has rarely been discussed. In this work, we investigate this dependence using a recursively embedded atom neural network method that possesses both local and MP features, in two representative systems: liquid H2O and solid Al2O3. We focus on how these settings influence predictions for structural and vibrational properties, namely, radial distribution functions (RDFs) and vibrational density of states (VDOSs). We find that while MP lowers test errors of energy and forces in general, it may not improve the prediction for RDFs and/or VDOSs if direct interatomic correlations in the local environment are insufficiently described. A cutoff radius exceeding the first neighbor shell is necessary, beyond which involving MP quickly enhances the model accuracy until convergence. This is a potentially more efficient way to increase the model accuracy than directly increasing the cutoff radius, especially with more memory savings in the GPU implementation. Our findings also suggest that using the mean test error as the measure of the model accuracy alone is inadequate.

Universal machine learning for the response of atomistic systems to external fields.

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.

Accurate and Interpretable Dipole Interaction Model-Based Machine Learning for Molecular Polarizability.

Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although physical models or statistical machine learning models have been proposed, either a lack of accurate description of local chemical environments or demanding a large number of samples for training has limited their practical applications. In this study, we combine a physically inspired dipole interaction model and an accurate neural network method for predicting the polarizability tensors of molecules. With the local chemical environment precisely described and the requirement of rotational covariance naturally fulfilled, this hybrid model is proven to give an accurate molecular polarizability prediction, essentially reducing the number of training samples. The atomic polarizabilities are physically interpretable and transferable to larger molecules unseen in the training set. This promising method may find its wide range of applications, such as spectroscopic simulations and the construction of polarizable force fields.

Stereodynamics of adiabatic and non-adiabatic energy transfer in a molecule surface encounter.

Molecular energy transfer and reactions at solid surfaces depend on the molecular orientation relative to the surface. While such steric effects have been largely understood in electronically adiabatic processes, the orientation-dependent energy transfer in NO scattering from Au(111) was complicated by electron-mediated nonadiabatic effects, thus lacking a clear interpretation and posing a great challenge for theories. Herein, we investigate the stereodynamics of adiabatic and nonadiabatic energy transfer via molecular dynamics simulations of NO(v = 3) scattering from Au(111) using realistic initial orientation distributions based on accurate neural network fitted adiabatic potential energy surface and electronic friction tensor. Our results reproduce the observed stronger vibrational relaxation for N-first orientation and enhanced rotational rainbow for O-first orientation, and demonstrate how adiabatic anisotropic interactions steer molecules into the more attractive N-first orientation to experience more significant energy transfer. Remaining disagreements with experiment suggest the direction for further developments of nonadiabatic theories for gas-surface scattering.

REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems.

In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called recursively embedded atom neural network model takes advantages of both the physically inspired atomic descriptor based neural networks and the message-passing based neural networks. Implemented in the PyTorch framework, the training process is parallelized on both the central processing unit and the graphics processing unit with high efficiency and low memory in which all hyperparameters can be optimized automatically. We demonstrate the state-of-the-art accuracy, high efficiency, scalability, and universality of this package by learning not only energies (with or without forces) but also dipole moment vectors and polarizability tensors in various molecular, reactive, and periodic systems. An interface between a trained model and LAMMPs is provided for large scale molecular dynamics simulations. We hope that this open-source toolbox will allow for future method development and applications of machine learned potential energy surfaces and quantum-chemical properties of molecules, reactions, and materials.

Two-stage particle separation channel based on standing surface acoustic wave.

Microfluidic technology has great advantages in the precise manipulation of micro and nano particles, and the collection method of micro and nano particles based on ultrasonic standing waves has attracted much attention for its high efficiency and simplicity of structure. This article proposes a two-stage particle separation channel using ultrasound. In the microfluidic channel, two different sound pressure regions are used to achieve the separation of particles with positive acoustic contrast factors. Through numerical simulation, the performance of three common piezoelectric substrate materials was compared qualitatively and quantitatively, and it was found that the output sound pressure intensity of 128°YX-LiNbO3 was high and the output was stable. At the same time, the influence of the number of electrode pairs of the interdigital transducer and the electrode voltage on the output sound wave is studied. Finally, 15 pairs of electrode pairs are selected, and the electrode voltages of the two sound pressure regions are 2.0 V and 3.0 V, respectively. After selecting the corresponding parameters, the separation process was numerically simulated, and the separation of three kinds of particles was successfully achieved. This work has laid a certain theoretical foundation for rapid disease diagnosis and real-time monitoring of the environment in practical applications.

Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality.

Recent advances in machine-learned interatomic potentials largely benefit from the atomistic representation and locally invariant many-body descriptors. It was, however, recently argued that including three-body (or even four-body) features is incomplete to distinguish specific local structures. Utilizing an embedded density descriptor made by linear combinations of neighboring atomic orbitals and realizing that each orbital coefficient physically depends on its own local environment, we propose a recursively embedded atom neural network model. We formally prove that this model can efficiently incorporate complete many-body correlations without explicitly computing high-order terms. This model not only successfully addresses challenges regarding local completeness and nonlocality in representative systems, but also provides an easy and general way to update local many-body descriptors to have a message-passing form without changing their basic structures.

Determining the Effect of Hot Electron Dissipation on Molecular Scattering Experiments at Metal Surfaces.

Nonadiabatic effects that arise from the concerted motion of electrons and atoms at comparable energy and time scales are omnipresent in thermal and light-driven chemistry at metal surfaces. Excited (hot) electrons can measurably affect molecule-metal reactions by contributing to state-dependent reaction probabilities. Vibrational state-to-state scattering of NO on Au(111) has been one of the most studied examples in this regard, providing a testing ground for developing various nonadiabatic theories. This system is often cited as the prime example for the failure of electronic friction theory, a very efficient model accounting for dissipative forces on metal-adsorbed molecules due to the creation of hot electrons in the metal. However, the exact failings compared to experiment and their origin from theory are not established for any system because dynamic properties are affected by many compounding simulation errors of which the quality of nonadiabatic treatment is just one. We use a high-dimensional machine learning representation of electronic structure theory to minimize errors that arise from quantum chemistry. This allows us to perform a comprehensive quantitative analysis of the performance of nonadiabatic molecular dynamics in describing vibrational state-to-state scattering of NO on Au(111) and compare directly to adiabatic results. We find that electronic friction theory accurately predicts elastic and single-quantum energy loss but underestimates multiquantum energy loss and overestimates molecular trapping at high vibrational excitation. Our analysis reveals that multiquantum energy loss can potentially be remedied within friction theory whereas the overestimation of trapping constitutes a genuine breakdown of electronic friction theory. Addressing this overestimation for dynamic processes in catalysis and surface chemistry will likely require more sophisticated theories.

Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach.

Modeling the ultrafast photoinduced dynamics and reactivity of adsorbates on metals requires including the effect of the laser-excited electrons and, in many cases, also the effect of the highly excited surface lattice. Although the recent ab initio molecular dynamics with electronic friction and thermostats, (Te,Tl)-AIMDEF [Alducin, M.; Phys. Rev. Lett. 2019, 123, 246802], enables such complex modeling, its computational cost may limit its applicability. Here, we use the new embedded atom neural network (EANN) method [Zhang, Y.; J. Phys. Chem. Lett. 2019, 10, 4962] to develop an accurate and extremely complex potential energy surface (PES) that allows us a detailed and reliable description of the photoinduced desorption of CO from the Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics simulations performed on this EANN-PES reproduce the (Te,Tl)-AIMDEF results with a remarkable level of accuracy. This demonstrates the outstanding performance of the obtained EANN-PES that is able to reproduce available density functional theory (DFT) data for an extensive range of surface temperatures (90-1000 K); a large number of degrees of freedom, those corresponding to six CO adsorbates and 24 moving surface atoms; and the varying CO coverage caused by the abundant desorption events.

A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations.

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3-4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

Particle separation in microfluidics using different modal ultrasonic standing waves.

Microfluidic technology has great advantages in the precise manipulation of micro and nano particles, and the separation of micro and nano particles based on ultrasonic standing waves has attracted much attention for its high efficiency and simplicity of structure. This paper proposes a device that uses three modes of ultrasonic standing waves to continuously separate particles with positive acoustic contrast factor in microfluidics. Three modes of acoustic standing waves are used simultaneously in different parts of the microchannel. According to the different acoustic radiation force received by the particles, the particles are finally separated to the pressure node lines on both sides and the center of the microchannel. In this separation method, initial hydrodynamic focusing and satisfying various equilibrium constraints during the separation process are the key. Through numerical simulation, the resonance frequency of the interdigital transducer, the distribution of sound pressure in the liquid, and the relationship between the interdigital electrode voltage and the output sound pressure are obtained. Finally, the entire separation process in the microchannel was simulated, and the separation of the two particles was successfully achieved. This work has laid a certain theoretical foundation for the rapid diagnosis of diseases in practical applications.

Simulation analysis of mixing in passive microchannel with fractal obstacles based on Murray's law.

In this paper, we designed fractal obstacles according to Murray's law and set them in a microchannel. We study the influence of the numbers of fractal obstacles, channel widths, branch widths, and the distance between fractal obstacles on mixing efficiency. The optimized micromixer has a high mixing efficiency of more than 90% at all velocities. This paper focuses on the analysis of the variation of mixing efficiency and pressure drop in the range of Reynolds number (Re) 0.1-150. The simulation results show that when the fluid velocity is low, the mixing efficiency of the fluids is mainly improved by molecular diffusion, when the fluid velocity is high, the microchannel with fractal obstacles can promote chaotic convection of the fluids and improve the mixing efficiency. The fractal structure based on Murray's law can be widely used in the design of passive micromixer.

Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials.

Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic simulations with ab initio accuracy. While constructing NN PESs, their training data points are often sampled by molecular dynamics trajectories. This strategy can be however inefficient for reactive systems involving rare events. Here, we develop an uncertainty-driven active learning strategy to automatically and efficiently generate high-dimensional NN-based reactive potentials, taking a gas-surface reaction as an example. The difference between two independent NN models is used as a simple and differentiable uncertainty metric, allowing us to quickly search in the uncertainty space and place new samples at which the PES is less reliable. By interfacing this algorithm with the first-principles simulation package, we demonstrate that a globally accurate NN potential of the H2 + Ag(111) system can be constructed with merely ∼150 data points. This PES can be further refined to describe H2 dissociation on Ag(100) by adding ∼130 more configurations on this facet. The entire process is completely automatic and self-terminated once the relative error criterion is fulfilled. Impressively, data points sampled by this uncertainty-driven strategy are substantially fewer than by the traditional trajectory-based sampling. The final NN PES not only converges well the quantum dissociation probability of the molecule but also well-reproduces the phonon properties of the substrate and is capable of describing surface temperature effects. These results show the potential of this active learning approach in developing high-dimensional NN reactive potentials in gas and condensed phases.

Towards bridging the structure gap in heterogeneous catalysis: the impact of defects in dissociative chemisorption of methane on Ir surfaces.

A quantitative understanding of the role played by defect sites in heterogeneous catalysis is of great importance in designing new and more effective catalysts. In this work, we report a detailed dynamic study of a key step in methane steam reforming under experimentally relevant conditions on a new high-dimensional potential energy surface determined from first principles data with the aid of machine learning, with which the interactions of CH4 with both the flat Ir(111) and stepped Ir(332) surfaces are described. In particular, we argue based on our simulations that the experimentally observed "negatively activated" dissociative chemisorption of methane on Ir surfaces could be due to a combined effect of defects and high substrate temperature, which lowers the reaction barrier relative to that on terraces. Furthermore, a model based on dynamic information of trapping and reaction channels is proposed, which allows a quantitative prediction of the initial sticking probability for different defect densities, thus helping to close the so-called structure gap in heterogeneous catalysis.