Ionic limiting molar conductivity calculation of Li-ion battery electrolyte based on mode coupling theory

A method is proposed based on mode coupling theory in which the ion transference number is introduced into the theory. The ionic limiting molar conductivities of LiPF6, LiClO4, LiBF4, LiCF3SO3, Li(CF3SO3)2N, LiC4F9SO3, and LiAsF6 in PC(propylene carbonate), GBL(gamma-butyrolactone), PC(propylene carbonate)/EMC(ethylmethyl carbonate), and PC(propylene carbonate)/DME(dimethoxyethane) are calculated based on this method, which does not involve any adjustable parameter. The results fit well to the literature data which are calculated by an empirically adjusted formula. This presents a potential way to calculate the conductivities of Li-ion battery electrolytes.     

Enantioselective separation in capillary electrophoresis using a novel mono-6-propylammonium-β-cyclodextrin as chiral selector

The chiral resolving ability of a novel single-isomer cationic β-cyclodextrin (CD), mono-6^A-propylammonium-6^A-deoxy-β-cyclodextrin chloride (PrAMCD), as a chiral selector in capillary electrophoresis (CE) is reported in this work for the enantioseparation of hydroxy, carboxylic acids and amphoteric analytes. The effect of chiral selector concentration on the resolution was studied. Good resolutions were achieved for hydroxy acids. Optimum resolutions were obtained even at 3.5 mM CD concentration for carboxylic acids. The electrophoretic method showed good linearity and reproducibility in terms of migration times and peak areas, which should make it suitable for routine analysis. In addition, baseline chiral separation of a six-acid mixture was achieved within 20 min. PrAMCD proved to be an effective chiral selector for acidic analytes.     

The atomic-level structure of bandgap engineered double perovskite alloys Cs2AgIn1−xFexCl6

Although lead-free halide double perovskites are considered as promising alternatives to lead halide perovskites for optoelectronic applications, state-of-the-art double perovskites are limited by their large bandgap. The doping/alloying strategy, key to bandgap engineering in traditional semiconductors, has also been employed to tune the bandgap of halide double perovskites. However, this strategy has yet to generate new double perovskites with suitable bandgaps for practical applications, partially due to the lack of fundamental understanding of how the doping/alloying affects the atomic-level structure. Here, we take the benchmark double perovskite Cs₂AgInCl₆ as an example to reveal the atomic-level structure of double perovskite alloys (DPAs) Cs₂AgIn_1−xFe_xCl₆ (x = 0–1) by employing solid-state nuclear magnetic resonance (ssNMR). The presence of paramagnetic alloying ions (e.g. Fe³⁺ in this case) in double perovskites makes it possible to investigate the nuclear relaxation times, providing a straightforward approach to understand the distribution of paramagnetic alloying ions. Our results indicate that paramagnetic Fe³⁺ replaces diamagnetic In³⁺ in the Cs₂AgInCl₆ lattice with the formation of [FeCl₆]³⁻·[AgCl₆]⁵⁻ domains, which show different sizes and distribution modes in different alloying ratios. This work provides new insights into the atomic-level structure of bandgap engineered DPAs, which is of critical significance in developing efficient optoelectronic/spintronic devices.

Through Fe³⁺-alloying, the bandgap of benchmark double perovskite Cs₂AgInCl₆ can be tuned from 2.8 eV to 1.6 eV. The atomic-level structure of Cs₂AgIn_1−xFe_xCl₆ was revealed by solid-state nuclear magnetic resonance (ssNMR).     

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.     

Study on the denoising method for the electromagnetic ultrasonic echoes from multiple interfaces

The electromagnetic ultrasound is used in the detection of interfaces of the adhesive multilayer structures to solve the unstable coupling problem in ultrasonic testing by traditional piezoelectric transducers. Based on the analysis of the transforming mechanism of electromag-netic ultrasound energy and the resultant dead zone from mutual inductance of the transducer, the wavelet filtering by soft-thresholding and adaptive noise canceling methods are used simul-taneously to the detected electromagnetic ultrasonic signals to overcome the drawbacks of the low signal to noise ratio （SNR） and the wide intrinsic dead zone of the transducer. Processed results in the interface detection of a three layered adhesive sample of steel and rubber materials demonstrate that the wavelet filtering enhances the SNR about 12dB while the adaptive noise canceling narrows the dead zone effectively.     

Rational Synthesis of Iron/Nitrogen-Doped Carbon Catalyst through a Spatial Isolation Strategy for Efficient Oxygen Reduction in Acidic and Alkaline Media

It remains challenging to rationally synthesize iron/nitrogen-doped carbon (Fe/N-C) catalysts with rich Fe−N_x atomic active sites for improved oxygen reduction reaction (ORR) electrocatalysis. A highly efficient Fe/N-C catalyst, which has been synthesized through a spatial isolation strategy, is reported. Derived from bioinspired polydopamine (PDA)-based hybrid microsphere precursors, it is a multifunctional carrier that loads atomically dispersed Fe³⁺/Zn²⁺ ions through coordination interactions and N-rich melamine through electrostatic attraction and covalent bonding. The Zn²⁺ ions and melamine in the precursor efficiently isolate Fe³⁺ atoms upon pyrolysis to form rich Fe−N_x atomic active sites, and generate abundant micropores during high-temperature treatment; as a consequence, the resultant Fe-N/C catalyst contains rich catalytically active Fe−N_x sites and a hierarchical porous structure. The catalyst exhibits improved ORR activity that is superior to and close to that of Pt/C in alkaline and acidic solutions, respectively.     

Plasmons and optical excitations in graphene rings

A simple semiclassical drude-like conductivity of graphene is employed to describe plasmon excitations of graphene in the ring structures. A quasi-static self-consistent integral equation approach is performed, allowing the calculation of all the plasmon modes with different angular momentum l. Among them only the dipole modes (l?=?1) will couple out to the radiation modes, which in turn can be excited optically by the plane waves, and the excitation energies as a function of the ratio of the radius of the inner hole to that of the outer ring have also been investigated. It is demonstrated that the energy of symmetric modes will monotonically decrease as the ratio rises, and the energy of antisymmetric modes does not exhibit a monotonically increasing behavior as in a three-dimensional metallic ring, but first reduces and then increases. These predictions are tested by full-wave simulations using the optical conductivity of graphene that was obtained from the random phase approximation (RPA).     

A family of single‐isomer,dicationic cyclodextrin chiral selectors for capillary electrophoresis: Mono‐6A‐ammonium‐6C‐butylimidazolium‐β‐cyclodextrin chlorides

The first member of the single‐isomer, dicationic cyclodextrin (CD) family, 6^A‐ammonium‐6^C‐butylimidazolium‐β‐cyclodextrin chlorides (AMBIMCD), has been synthesized, analytically characterized, and used to separate a variety of acidic enantiomers and amino acids by CE. Starting from mono‐6^A‐azido‐β‐cyclodextrin, the cationic imidazolium and ammonium moieties were subsequently introduced onto primary ring of β‐cyclodextrin via nucleophilic addition and Staudinger reaction. The analytically pure AC regio‐isomer CD was further obtained via column chromatography. This dicationic CD exhibited excellent enantioselectivities for selected analytes at concentration as low as 0.5 mM, which were even better than those of its mono‐imidazolium or ammonium‐substitued counterpart CDs at 10 equivalent concentrations. The effective mobilities of all studied analytes were found to decrease with the concentration of AMBIMCD. Inclusion complexation in combination with eletrostatic interactions seemed to account for the enhanced chiral discrimination process.     

Propagating surface plasmon polaritons for remote excitation surface-enhanced Raman scattering spectroscopy

To illustrate the attractive potentials of remote-excitation surface enhanced Raman scattering (RE-SERS), we review the fundamental concepts and typical applications of propagating surface plasmon polaritons (PSPPs). Based on the RE-SERS technic, the adsorbed molecules are protected from being directly illuminated, while the merits of SERS still remained. However, the critical limitations of applying RE-SERS hinder its rapid development. Hence, drawing an overview about the PSPPs would be beneficial for further promoting the significance of RE-SERS in biological application and investigating the mechanism of surface catalytic reactions.