Retrieving positions of closely packed sub-wavelength nanoparticles from their diffraction patterns

Distinguishing two objects or point sources located closer than the Rayleigh distance is impossible in conventional microscopy. Understandably, the task becomes increasingly harder with a growing number of particles placed in close proximity. It has been recently demonstrated that subwavelength nanoparticles in closely packed clusters can be counted by AI-enabled analysis of the diffraction patterns of coherent light scattered by the cluster. Here we show that deep learning analysis can determine the actual position of the nanoparticle in the cluster of subwavelength particles from a sing-shot diffraction pattern even if they are separated by distances below the Rayleigh resolution limit of a conventional microscope.Comment: 6 pages, 3 figure

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

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

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A solid‐state fibriform supercapacitor boosted by host – guest hybridization between the carbon nanotube scaffold and MXene nanosheets

Fiber-shaped supercapacitors with improved specific capacitance and high rate capability are a promising candidate as power supply for smart textiles. However, the synergistic interaction between conductive filaments and active nanomaterials remains a crucial challenge, especially when hydrothermal or electrochemical deposition is used to produce a core (fiber)-shell (active materials) fibrous structure. On the other hand, although 2D pseudocapacitive materials, e.g., Ti3 C2 T x (MXene), have demonstrated high volumetric capacitance, high electrical conductivity, and hydrophilic characteristics, MXene-based electrodes normally suffer from poor rate capability owing to the sheet restacking especially when the loading level is high and solid-state gel is used as electrolyte. Herein, by hosting MXene nanosheets (Ti3 C2 T x ) in the corridor of a scrolled carbon nanotube (CNT) scaffold, a MXene/CNT fiber with helical structure is successfully fabricated. These features offer open spaces for rapid ion diffusion and guarantee fast electron transport. The solid-state supercapacitor based on such hybrid fibers with gel electrolyte coating exhibits a volumetric capacitance of 22.7 F cm-3 at 0.1 A cm-3 with capacitance retention of 84% at current density of 1.0 A cm-3 (19.1 F cm-3 ), improved volumetric energy density of 2.55 mWh cm-3 at the power density of 45.9 mW cm-3 , and excellent mechanical robustness

Polarity-assisted formation of hollow-frame sheathed nitrogen-doped nanofibrous carbon for supercapacitors

Heteroatom-doped carbon nanostructures with uniform size and morphology, well-designed architectures, and minimized interfacial resistance have been recognized as promising electrode materials for energy storage, but remain a crucial challenge. Herein, we develop a general approach of polarity-induced decoration of a monolayer sheath of metal-organic framework (MOF) particles with excellent uniformity in size and morphology on electrospun polymer nanofibers. These hybrid nanofibers are facilely converted into nitrogen-doped nanofibrous carbon (denoted as N-NFC) during pyrolysis. The thus-obtained N-NFC features (1) a one-dimensional nanofibrous structure with a highly conductive core, (2) a monolayer sheath of hollow carbon-frames with uniform size and morphology, (3) plenty of micro/mesopores with a highly accessible surface area, and (4) a high N-doping level, all of which guarantee its good electrochemical performance with a high capacitance of 387.3 F g⁻¹ at 1 A g⁻¹. In a solid-state supercapacitor, it delivers excellent rate capability (78.0 F g⁻¹ at 0.2 A g⁻¹ and 64.0 F g⁻¹ at 1 A g⁻¹), an enhanced energy density of 7.9 W h kg⁻¹ at a power density of 219 W kg⁻¹, and outstanding cycling stability with 90% capacity retained over 10 000 cycles at 1 A g⁻¹.This work was supported by the National Natural Science Foundation of China (61704076), the Natural Science Foundation of Jiangsu Province (BK20171018), the NanjingTech Start-Up Grant (3983500150), and the Jiangsu Specially-Appointed Professor program (54935012)

Design of a wearable and shape-memory fibriform sensor for the detection of multimodal deformation

A wearable and shape-memory strain sensor with a coaxial configuration is designed, comprising a thermoplastic polyurethane fiber as the core support, well-aligned and interconnected carbon nanotubes (CNTs) as conductive filaments, and polypyrrole (PPy) coating as the cladding layer. In this design, the stress relaxation between CNTs is well confined by the outer PPy cladding layer, which endows the fibriform sensor with good reliability and repeatability. The microcracks generated when the coaxial fiber is under strain guarantee the superior sensitivity of this fibriform sensor with a gauge factor of 12 at 0.1% strain, a wide detectable range (from 0.1% to 50% tensile strain), and the ability to detect multimodal deformation (tension, bending, and torsion) and human motions (finger bending, breathing, and phonation). In addition, due to its shape-memory characteristic, the sensing performance of the fibriform sensor is well retained after its shape recovers from 50% deformation and the fabric woven from the shape-memory coaxial fibers can be worn on the elbow joints in a reversible manner (original-enlarged-recovered) and fitted tightly. Thus, this sensor shows promising applications in wearable electronics

Emergence of a new designated clade 16 with significant antigenic drift in hemagglutinin gene of H9N2 subtype avian influenza virus in eastern China

H9N2 avian influenza viruses (AIVs) pose an increasing threat to the poultry industry worldwide and have pandemic potential. Vaccination has been principal prevention strategy to control H9N2 in China since 1998, but vaccine effectiveness is persistently challenged by the emergence of the genetic and/or antigenic variants. Here, we analysed the genetic and antigenic characteristics of H9N2 viruses in China, including 70 HA sequences of H9N2 isolates from poultry, 7358 from online databases during 2010-2020, and 15 from the early reference strains. Bayesian analyses based on hemagglutinin (HA) gene revealed that a new designated clade16 emerged in April 2012, and was prevalent and co-circulated with clade 15 since 2013 in China. Clade 16 viruses exhibited decreased cross-reactivity with those from clade 15. Antigenic Cartography analyses showed represent strains were classified into three antigenic groups named as Group1, Group2 and Group3, and most of the strains in Group 3 (15/17, 88.2%) were from Clade 16 while most of the strains in Group2 (26/29, 89.7%) were from Clade 15. The mean distance between Group 3 and Group 2 was 4.079 (95%CI 3.605 - 4.554), revealing that major switches to antigenic properties were observed over the emergence of clade 16. Genetic analysis indicated that 11 coevolving amino acid substitutions primarily at antigenic sites were associated with the antigenic differences between clade 15 and clade 16. These data highlight complexities of the genetic evolution and provide a framework for the genetic basis and antigenic characterization of emerging clade 16 of H9N2 subtype avian influenza virus