Dealloyed porous gold anchored by: In situ generated graphene sheets as high activity catalyst for methanol electro-oxidation reaction

A novel one-step method to prepare the nanocomposites of reduced graphene oxide (RGO)/nanoporous gold (NPG) is realized by chemically dealloying an Al2Au precursor. The RGO nanosheets anchored on the surface of NPG have a cicada wing like shape and act as both conductive agent and buffer layer to improve the catalytic ability of NPG for methanol electro-oxidation reaction (MOR). This improvement can also be ascribed to the microstructure change of NPG in dealloying with RGO. This work inspires a facile and economic method to prepare the NPG based catalyst for MOR

Physics-data-driven intelligent optimization for large-scale meta-devices

Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices.Comment: manuscripts:19 pages, 4 figures; Supplementary Information: 11 pages, 12 figure

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol–1 Å–1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost

Crosstalk-free achromatic full Stokes imaging polarimetry metasurface enabled by polarization-dependent phase optimization

Imaging polarimetry is one of the most widely used analytical technologies for object detection and analysis. To date, most metasurface-based polarimetry techniques are severely limited by narrow operating bandwidths and inevitable crosstalk, leading to detrimental effects on imaging quality and measurement accuracy. Here, we propose a crosstalk-free broadband achromatic full Stokes imaging polarimeter consisting of polarization-sensitive dielectric metalenses, implemented by the principle of polarization-dependent phase optimization. Compared with the single-polarization optimization method, the average crosstalk has been reduced over three times under incident light with arbitrary polarization ranging from 9 μm to 12 μm, which guarantees the measurement of the polarization state more precisely. The experimental results indicate that the designed polarization-sensitive metalenses can effectively eliminate the chromatic aberration with polarization selectivity and negligible crosstalk. The measured average relative errors are 7.08%, 8.62%, 7.15%, and 7.59% at 9.3, 9.6, 10.3, and 10.6 μm, respectively. Simultaneously, the broadband full polarization imaging capability of the device is also verified. This work is expected to have potential applications in wavefront detection, remote sensing, light-field imaging, and so forth

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Evacuation simulation for cabin crew

[Objectives] This paper researches on the evacuation of cabin crew which has a number of uncertainties. During evacuation, the behavior and attributes of personnel and the space state of the cabin should be considered. [Methods] Through the functional division of the personnel model structure, the personnel evacuation model structure is determined, the personnel attributes are modelled, and the rules of personnel protection are established. Through dividing the cabin space model, plotting the cabin environment and grading the safety of subenvironments, the obstacles in the cabin are simulated and represented mathematically for the calculation via the 3D simulation software. Then by 3D evacuation simulation software, simulation tests for both single and multiple evacuation paths are carried out respectively. Besides, the selection of evacuation paths by the personnel in different working areas under specific condition are simulated so as to calculate the distance and time of evacuation. [Results] The simulation results show that the evacuation model of the cabin crew can simulate the evacuation process accurately. [Conclusions] The research findings provide basis for the auxiliary design of the cabin

Taming the Electromagnetic Boundaries via Metasurfaces: From Theory and Fabrication to Functional Devices

As two-dimensional metamaterials, metasurfaces have received rapidly increasing attention from researchers all over the world. Unlike three-dimensional metamaterials, metasurfaces can be utilized to control the electromagnetic waves within one infinitely thin layer, permitting substantial advantages, such as easy fabrication, low cost, and high degree of integration. This paper reviews the history and recent development of metasurfaces, with particular emphasis on the theory and applications relating to the frequency response, phase shift, and polarization state control. Based on the current status of various applications, the challenges and future trends of metasurfaces are discussed

Facilitating Ab Initio QM/MM Free Energy Simulations by Gaussian Process Regression with Derivative Observations

In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results

Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching

An efficient and accurate reference potential simulation protocol is proposed for producing ab initio quantum mechanical molecular mechanical (AI-QM/MM) quality free energy profiles for chemical reactions in a solvent or macromolecular environment. This protocol involves three stages: (a) using force matching to recalibrate a semi-empirical quantum mechanical (SE-QM) Hamiltonian for the specific reaction under study; (b) employing the recalibrated SE-QM Hamiltonian (in combination with molecular mechanical force fields) as the reference potential to drive umbrella samplings along the reaction pathway; and (c) computing AI-QM/MM energy values for collected configurations from the sampling and performing weighted thermodynamic perturbation to acquire AI-QM/MM corrected reaction free energy profile. For three model reactions (identity SN2 reaction, Menshutkin reaction, and glycine proton transfer reaction) in aqueous solution and one enzyme reaction (Claisen arrangement in chorismate mutase), our simulations using recalibrated PM3 SE-QM Hamiltonians well reproduced QM/MM free energy profiles at the B3LYP/6–31G* level of theory all within 1 kcal/mol with a 20 to 45 fold reduction in the computer time