Label free colorimetric sensing of thiocyanate based on inducing aggregation of Tween 20-stabilized gold nanoparticles

Based on inducing the aggregation of gold nanoparticles (AuNPs), a simple colorimetric method with high sensitivity and selectivity was developed for the sensing of thiocyanate (SCN-) in aqueous solutions. Citrate-capped AuNPs were prepared following a classic method and Tween 20 was subsequently added as a stabilizer. With the addition of SCN-, citrate ions on AuNPs surfaces were replaced due to the high affinity between SCN- and Au. As a result, Tween 20 molecules adsorbed on the AuNPs surfaces were separated and the AuNPs aggregated. The process was accompanied by a visible color change from red to blue within 5 min. The sensing of SCN- can therefore be easily achieved by a UV-vis spectrophotometer or even by the naked eye. The potential effects of relevant experimental conditions, including concentration of Tween 20, pH, incubation temperature and time, were evaluated to optimize the method. Under optimized conditions, this method yields excellent sensitivity (LOD = 0.2 mu M or 11.6 ppb) and selectivity toward SCN-. Our attempt may provide a cost-effective, rapid and simple solution to the inspection of SCN- ions in saliva and environmental aqueous samples

Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy

Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its surprising effectiveness in improving generalization performance.However, training neural networks with SAM can be highly unstable since the loss does not decrease along the direction of the exact gradient at the current point, but instead follows the direction of a surrogate gradient evaluated at another point nearby. To address this issue, we propose a simple renormalization strategy, dubbed StableSAM, so that the norm of the surrogate gradient maintains the same as that of the exact gradient. Our strategy is easy to implement and flexible enough to integrate with SAM and its variants, almost at no computational cost. With elementary tools from convex optimization and learning theory, we also conduct a theoretical analysis of sharpness-aware training, revealing that compared to stochastic gradient descent (SGD), the effectiveness of SAM is only assured in a limited regime of learning rate. In contrast, we show how StableSAM extends this regime of learning rate and when it can consistently perform better than SAM with minor modification. Finally, we demonstrate the improved performance of StableSAM on several representative data sets and tasks.Comment: 31 page

Highly sensitive label-free colorimetric sensing of nitrite based on etching of gold nanorods

A simple colorimetric method with high sensitivity and selectivity was developed for sensing of nitrite as low as 4.0 mu M by naked eyes, which is based on etching of gold nanorods accompanied by shape changes in aspect ratios (length/width) and a visible color change from bluish green to red and then to colorless with the increase of nitrite

Growth, chemical components and ensiling characteristics of king grass at different cuttings

In order to effectively use and ensile king grass (Pennisetum purpureum × Pennisetum americanum), the present research investigated growth rate, yield, chemical components and silage fermentation quality of different cuttings. King grass was harvested four times, and the 1st and 3rd cuttings were ensiled directly or after wilting for 12 and 24 h. The results showed that the dry matter daily growth of 2nd cutting was significantly higher than that of other cuttings, and the 4th cutting was the lowest (P < 0.05). The contents of crude protein (CP), crude fat and water-soluble carbohydrates (WSC) tended to reduce, and crude ash tended to increase with the increase of cutting times. All four cuttings of king grass had higher WSC content, lower buffer capacity and much lactic acid bacteria, the silages made from unwilted 1st cutting and 3rd cutting were of good fermentation quality, indicated by low pH values and high V-scores. Wilting had different effects on the 1st cutting and 3rd cutting silages in pH value and NH3-N content, the 1st cutting silage tended to increase the pH values and NH3-N content, with moisture content reduction, while the 3rd cutting silage tended to reduce NH3-N content and its pH value was not affected by wilting (P > 0.05). Although the 3rd cutting silage had better aerobic stability than the 1st cutting silage, they all were not stable within 6 days of aerobic exposure. Considering the contents of CP, crude fat, crude fiber, crude ash and WSC, the 1st cutting of king grass might have best nutrient value, while the 4th cutting was contrary. Different cuttings of king grass could be well preserved by natural fermentation, but their aerobic stability was poor.Keywords: Cuttings, ensiling, king grass, nutrient component, wiltin

The Optimal Linear Secret Sharing Scheme for Any Given Access Structure

Any linear code can be used to construct a linear secret sharing scheme. In this paper, it is shown how to decide optimal linear codes (i.e., with the biggest information rate) realizing a given access structure over finite fields. It amounts to solving a system of quadratic equations constructed from the given access structure and the corresponding adversary structure. The system becomes a linear system for binary codes. An algorithm is also given for finding the adversary structure for any given access structure

Fluorescent sensing of mercury(II) based on formation of catalytic gold nanoparticles

A fluorescence assay for the highly sensitive and selective detection of Hg2+ using a gold nanoparticle (AuNP)-based probewas proposed. The assay was based on the formation of Hg-Au alloys, which accelerated the oxidization of o-phenylenediamine by dissolved oxygen to produce 2,3-diaminophenazine, a fluorescent product.;A fluorescence assay for the highly sensitive and selective detection of Hg2+ using a gold nanoparticle (AuNP)-based probewas proposed. The assay was based on the formation of Hg-Au alloys, which accelerated the oxidization of o-phenylenediamine by dissolved oxygen to produce 2,3-diaminophenazine, a fluorescent product

Experimental Study on Bending Performance of Composite Sandwich Panel with New Mixed Core

This paper presents an experimental investigation of bending performance of composite sandwich panels with new mixed core, sandwich panels were tested by four-point bending test. Parametric study was conducted to investigate the influence of different core materials on the failure mode, ultimate bearing capacity, stiffness and ductility of composite sandwich panels. The results of the experimental investigation showed that the mixed core can change the failure mode of sandwich panels. The failure mode of wooden panels is characterized by tensile failure of bottom wood, and the failure mode of composite sandwich panels with wood core is that the surface layer and core are stripped and the webs are damaged by shear, while the failure mode of composite sandwich panels with wood and polyurethane foam mixed core is the shear failure of the web. Composite sandwich panels with GFRP-wood-polyurethane foam core have better bending performance and can effectively reduce the weight of panels

Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling

The incompleteness of the seismic data caused by missing traces along the spatial extension is a common issue in seismic acquisition due to the existence of obstacles and economic constraints, which severely impairs the imaging quality of subsurface geological structures. Recently, deep learning-based seismic interpolation methods have attained promising progress, while achieving stable training of generative adversarial networks is not easy, and performance degradation is usually notable if the missing patterns in the testing and training do not match. In this paper, we propose a novel seismic denoising diffusion implicit model with resampling. The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step. The cosine noise schedule, serving as the global noise configuration, promotes the high utilization of known trace information by accelerating the passage of the excessive noise stages. The model inference utilizes the denoising diffusion implicit model, conditioning on the known traces, to enable high-quality interpolation with fewer diffusion steps. To enhance the coherency between the known traces and the missing traces within each reverse step, the inference process integrates a resampling strategy to achieve an information recap on the former interpolated traces. Extensive experiments conducted on synthetic and field seismic data validate the superiority of our model and its robustness on various missing patterns. In addition, uncertainty quantification and ablation studies are also investigated.Comment: 14 pages, 13 figure