Molecular dynamics-driven global tetra-atomic potential energy surfaces: Application to the AlF dimer

In this work, we present a general machine learning approach for full-dimensional potential energy surfaces for tetra-atomic systems. Our method employs an active learning scheme trained on {\it ab initio} points, which size grows based on the accuracy required. The training points are selected based on molecular dynamics simulations, choosing the most suitable configurations for different collision energy and mapping the most relevant part of the potential energy landscape of the system. The present approach does not require long-range information and is entirely general. As an example, we provide the full-dimensional AlF-AlF potential energy surface, requiring $\lesssim 0.1\%$ of the configurations to be calculated {\it ab initio}. Furthermore, we analyze the general properties of the AlF-AlF system, finding key difference with other reported results on CaF or bi-alkali dimers

Phononic transport in 1T prime-MoTe2: anisotropic structure with an isotropic lattice thermal conductivity

Molybdenum ditelluride (MoTe2) is an unique transition metal dichalcogenide owing to its energetically comparable 1H and 1T prime phases. This implies a high chance of coexistence of 1H-1T prime heterostructures which poses great complexity in the measurement of the intrinsic lattice thermal conductivities (kappa). In this work, via first-principles calculations, we examine the lattice-wave propagation and thermal conduction in this highly structurally anisotropic 1T prime MoTe2. Our calculation shows that the 1T prime phase has a sound velocity of 2.13 km/s (longitudinal acoustic wave), much lower than that of the 1H phase (4.05 km /s), indicating a staggered transmission of lattice waves across the boundary from 1H to 1T prime phase. Interestingly, the highly anisotropic 1T prime MoTe2 shows nearly isotropic and limited kappa_L of 13.02 W/mK, owing to a large Gruneisen parameter of acoustic flexural mode, heavy masses of Mo and Te elements and a low phonon group velocity. Accumulative kappa_L as a function of mean free path (MFP) indicates phonons with MFP less than ~300 nm contribute 80% of kappa_L and an inflection point at ~600 nm. Our results will be critical for understanding of the size dependent kappa_L of nanostructured 1T prime MoTe2

Boosting the sodium storage performance of iron selenides by a synergetic effect of vacancy engineering and spatial confinement

Recently, iron selenides have been considered as one of the most promising candidates for the anodes of sodium-ion batteries (SIBs) due to their cost-effectiveness and high theoretical capacity; however, their practical application is limited by poor conductivity, large volume variation and slow reaction kinetics during electrochemical reactions. In this work, spatially dual-carbon-confined Vₛₑ-Fe₃Se₄₋ₓSₓ/FeSe₂₋ₓSₓ nanohybrids with abundant Se vacancies (Vₛₑ-Fe₃Se₄₋ₓSₓ/FeSe₂₋ₓSₓ@NSC@rGO) are constructed via anion doping and carbon confinement engineering. The three-dimensional crosslinked carbon network composed of the nitrogen-doped carbon support derived from polyacrylic acid (PAA) and reduced graphene enhances the electronic conductivity, provides abundant channels for ion/electron transfer, ensures the structure integrity, and alleviates the agglomeration, pulverization and volume change of active material during the chemical reactions. Moreover, the introduction of S into iron selenides induces a large number of Se vacancies and regulates the electron density around iron atoms, synergistically improving the conductivity of the material and reducing the Na+ diffusion barrier. Based on the aforementioned features, the as-synthesized Vₛₑ-Fe₃Se₄₋ₓSₓ/FeSe₂₋ₓSₓ@NSC@rGO electrode possesses excellent electrochemical properties, exhibiting the satisfactory specific capacity of 630.1 mA h g−¹ after 160 cycles at 0.5 A/g and the reversible capacity of 319.8 mA h g−¹ after 500 cycles at 3 A/g with the low-capacity attenuation of 0.016 % per cycle. This investigation provides a feasible approach to develop high-performanc

The apple 14-3-3 gene MdGRF6 negatively regulates salt tolerance

The 14-3-3 (GRF, general regulatory factor) regulatory proteins are highly conserved and are widely distributed throughout the eukaryotes. They are involved in the growth and development of organisms via target protein interactions. Although many plant 14-3-3 proteins were identified in response to stresses, little is known about their involvement in salt tolerance in apples. In our study, nineteen apple 14-3-3 proteins were cloned and identified. The transcript levels of Md14-3-3 genes were either up or down-regulated in response to salinity treatments. Specifically, the transcript level of MdGRF6 (a member of the Md14-3-3 genes family) decreased due to salt stress treatment. The phenotypes of transgenic tobacco lines and wild-type (WT) did not affect plant growth under normal conditions. However, the germination rate and salt tolerance of transgenic tobacco was lower compared to the WT. Transgenic tobacco demonstrated decreased salt tolerance. The transgenic apple calli overexpressing MdGRF6 exhibited greater sensitivity to salt stress compared to the WT plants, whereas the MdGRF6-RNAi transgenic apple calli improved salt stress tolerance. Moreover, the salt stress-related genes (MdSOS2, MdSOS3, MdNHX1, MdATK2/3, MdCBL-1, MdMYB46, MdWRKY30, and MdHB-7) were more strongly down-regulated in MdGRF6-OE transgenic apple calli lines than in the WT when subjected to salt stress treatment. Taken together, these results provide new insights into the roles of 14-3-3 protein MdGRF6 in modulating salt responses in plants

Heterogeneous engineering and carbon confinement strategy to synergistically boost the sodium storage performance of transition metal selenides

Transition metal selenides (TMSs) stand out as a promising anode material for sodium-ion batteries (SIBs) owing to their natural resources and exceptional sodium storage capacity. Despite these advantages, their practical application faces challenges, such as poor electronic conductivity, sluggish reaction kinetics and severe agglomeration during electrochemical reactions, hindering their effective utilization. Herein, the dual-carbon-confined CoSe /FeSe @NC@C nanocubes with heterogeneous structure are synthesized using ZIF-67 as the template by ion exchange, resorcin-formaldehyde (RF) coating, and subsequent in situ carbonization and selenidation. The N-doped porous carbon promotes rapid electrolyte penetration and minimizes the agglomeration of active materials during charging and discharging, while the RF-derived carbon framework reduces the cycling stress and keeps the integrity of the material structure. More importantly, the built-in electric field at the heterogeneous boundary layer drives electron redistribution, optimizing the electronic structure and enhancing the reaction kinetics of the anode material. Based on this, the nanocubes of CoSe /FeSe @NC@C exhibits superb sodium storage performance, delivering a high discharge capacity of 512.6 mA h g at 0.5 A g after 150 cycles and giving a discharge capacity of 298.2 mA h g at 10 A g with a CE close to 100.0 % even after 1000 cycles. This study proposes a viable method to synthesize advanced anodes for SIBs by a synergy effect of heterogeneous interfacial engineering and a carbon confinement strategy. [Abstract copyright: Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction

The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the hyperspectral image as endmembers. This paper develops a quadratic clustering-based simplex volume maximization (CSVM) approach to effectively alleviate spectral variability and extract endmembers. CSVM first adopts spatial clustering based on simple linear iterative clustering to obtain a set of homogeneous partitions and uses spectral purity analysis to choose pure pixels. The average of the chosen pixels in each partition is taken as a representative endmember, which reduces the effect of local-scope spectral variability. Then an improved spectral clustering based on k-means is implemented to merge homologous representative endmembers to further reduce the effect of large-scope spectral variability, and final endmember collection is determined by the simplex with maximum volume. Experimental results show that CSVM reduces the average spectral angle distance on Samson, Jasper Ridge and Cuprite datasets to below 0.02, 0.06 and 0.09, respectively, provides the root mean square errors of abundance maps on Samson and Jasper Ridge datasets below 0.25 and 0.10, and exhibits good noise robustness. By contrast, CSVM provides better results than other state-of-the-art algorithms