Development of a highly sensitive ultra-small ratiometric fluorescence nanosphere probe for Sunset Yellow detection.

A highly sensitive ultra-small ratiometric fluorescence nanosphere probe was successfully manufactured to detect Sunset Yellow (SY). The probe, CMCS@N, S-CDs/Rh6G, was formed through the encapsulation of N, S-CDs and Rh6G within carboxymethyl chitosan (CMCS) through in situ cross-linking. Remarkably, our nanosphere probe had an average grain diameter of 6.80 nm and exhibited excellent dispersibility without the need for additional solvents. The probe exhibited a strong linear relationship with SY concentration in the range of 0.26-100 µM, with a low detection limit of 0.078 µM. Furthermore, SY demonstrated strong fluorescence quenching capability on our nanosphere probe, with the fluorescence quenching mechanism involving a combined effects of inner filter effect (IFE) and static quenching. Notably, our nanosphere probe retained the bacteriostatic properties of CMCS, with a substantial bacteriostasis rate of 77.58 %, introducing novel potential applications.

Characteristics of arsenic speciation in mainly cultured shellfish from Sanmen Bay, Zhejiang Province, China.

Sanmen Bay plays a crucial role in economic shellfish aquaculture in China, yet few studies exist on the arsenic speciation of shellfish from this area. In this study, arsenic speciation of 11 cultured shellfish species from Sanmen Bay were analyzed by HPLC/ICP-MS. The results showed that organic arsenic particularly AsB, was the dominant arsenic species, constituting 21 %-71 % of the total arsenic. Conversely, the levels of inorganic arsenic were relatively low, ranging from 0.007 to 0.093 mg/kg, only accounted for 0.2 %-5.7 % of the total arsenic. There was no significant level correlation between inorganic arsenic and total arsenic in Sanmen Bay shellfish, so the concentration of inorganic arsenic did not increase with the total arsenic. Overall, the present study firstly revealed the arsenic speciation of shellfish from Sanmen Bay and also suggested that the proportion of inorganic arsenic should be considered in the revision of arsenic limit values.

A baseline study on the distribution characteristics and health risk assessment of cadmium in edible tissues of the swimming crabs (Portunus trituberculatus) from Shanghai, China.

This study analyzed the cadmium accumulation differences in edible tissues of the swimming crabs (Portunus trituberculatus) from Shanghai markets, which were mostly caught in the East China Sea, and the human health risk of cadmium from crabs consumption was evaluated. A total of 78 swimming crabs were collected, and the white meat and brown meat were separated for the cadmium analysis by Inductively coupled plasma mass spectrometry. The results revealed that there was difference in cadmium content in brown meat (1.260-16.303 mg/kg) and white meat (0.005-0.542 mg/kg). Furthermore, pollution index (Pi) results showed that only the claw muscle was at low contamination levels, while other edible tissues had varying degrees of contamination. Based on the health risk assessment by estimated daily intake (EDI), target hazard quotient (THQ) and target cancer risk (TCR), the consumption of the swimming crabs in Shanghai is considered safe, however, the accumulation of cadmium in the brown meat of swimming crabs deserves further attention and evaluation.

Transforming waste into value: pomelo-peel-based nitrogen-doped carbon dots for the highly selective detection of tetracycline.

Tetracycline (TC) is widely used as a veterinary drug, and its residue in livestock products could enter the human body and cause damage. In this study, we developed an eco-friendly approach that utilized pomelo peel as a carbon source to synthesize new water-soluble N-doped carbon dots (P-NCDs) with blue fluorescence, obtaining a high quantum yield of up to 76.47% and achieving the goal of turning waste into value. Our prepared P-NCDs can selectively recognized TC, and their fluorescence was quenched based on the IFE. P-NCDs could measure the TC concentration in the linear range of 0-100 µmol L-1 with a detection limit (LOD, S/N = 3) as low as 0.045 µmol L-1. Furthermore, we have successfully applied our P-NCDs to the detection of TC in milk samples with convincing results within 90 s. Overall, our newly synthesized fluorescent sensor, P-NCDs, demonstrated huge potential to become an alternative way to detect TC in a simple, efficient, sensitive way without using any special instruments.

Attenuation of Diabetic Nephropathy in Diabetic Mice by Fasudil through Regulation of Macrophage Polarization.

Inflammation and fibrosis induced by hyperglycemia are considered to play a critical role in the pathogenesis of diabetic nephropathy. As macrophage polarization may determine the severity and progression of inflammation, regulation of macrophage polarization may be an effective method to treat diabetic complications. Fasudil, a potent Rho-kinase inhibitor, reportedly exhibits anti-inflammatory activity. However, whether fasudil reduces hyperglycemia-induced diabetic nephropathy via regulation of macrophage polarization remains unclear. In this study, we investigate the effect of fasudil on diabetic nephropathy in streptozotocin-induced type 1 diabetic mice. Our data showed that fasudil significantly decreased urinary protein and serum creatinine in diabetic mice, whereas it had no effect on the body weight and blood glucose. We also found increased M1-type macrophages and related proinflammatory cytokines, adverse fibrosis in renal tissue of diabetic mice. Interestingly, treatment of diabetic mice with fasudil increased the number of M2-type macrophages and related anti-inflammatory cytokines, which attenuated renal injury in diabetic mice. Taken together, the results of this study suggest that fasudil could slow the progression of diabetic nephropathy. The possible mechanism might be associated with its induction of M2 macrophage polarization and the reduction of M1 macrophage polarization and inflammation.

Zinc ions modified InP quantum dots for enhanced photocatalytic hydrogen evolution from hydrogen sulfide.

Through direct addition of inorganic zinc ions into the solution of indium phosphide quantum dots (InP QDs) at ambient environment, we here present a facile but effective method to modify InP QDs for photocatalytic hydrogen evolution from hydrogen sulfide (H2S). X-ray diffraction patterns and transmission electron microscopic images demonstrate that zinc ions have no significant influence on the crystal structure and morphology of InP QDs, while X-ray photoemission spectra and UV-Vis diffuse and reflectance spectra indicate that zinc ions mainly adsorbed on the surface of InP QDs. Photocatalytic results show the average hydrogen evolution rate has been enhanced to 2.9 times after modification and H2S has indeed involves in the hydrogen evolution process. Steady-state and transient photoluminescence spectra prove that zinc ions could effectively eliminate the surface traps on InP QDs, which is crucial to suppress the recombination of charge carriers. In addition, the electrostatic interaction between zinc ions and the surface sulfide from InP QDs could mitigate the repulsion between QDs and sulfide/hydrosulfide, which may promote the surface oxidative reaction during photocatalysis. This work avoids the traditional harsh and complicated operations required for surface passivation of QDs, which offers a convenient way for optimization of QDs in photocatalysis.

A novel intelligent fault identification method based on random forests for HVDC transmission lines.

In order to remedy the current problem of having been buffeted by competing requirements for both protection sensitivity and quick reaction of High Voltage Direct Current (HVDC) transmission lines simultaneously, a new intelligent fault identification method based on Random Forests (RF) for HVDC transmission lines is proposed. S transform is implemented to extract fault current traveling wave of 8 frequencies and calculate the fluctuation index and energy sum ratio, in which the wave index is used to identify internal and external faults, and energy sum ratio is used to identify the positive and negative pole faults occurred on the transmission line. The intelligent fault identification model of RF is established, and the fault characteristic sample set of HVDC transmission lines is constructed by using multi-scale S transform fluctuation index and multi-scale S-transform energy sum ratio. Training and testing have been carried out to identify HVDC transmission line faults. According to theoretical researches and a large number of results of simulation experiments, the proposed intelligent fault identification method based on RF for HVDC transmission lines can effectively solve the problem of protection failure caused by inaccurate identification of traditional traveling wave wavefront or wavefront data loss. It can accurately and quickly realize the identification of internal and external faults and the selection of fault poles under different fault distances and transitional resistances, and has a strong ability to withstand transitional resistance and a strong ability to resist interference.

Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search.

An accurate, rapid signal analysis is crucial in the acoustic-based detection for internal defects in arc magnets. Benefiting from the adaptive decomposition without the mode mixing, variational mode decomposition (VMD), has emerged as a promising technology for processing and analyzing acoustic signals. However, improper parameter settings are the root cause of inaccurate VMD results, while existing optimization methods for VMD parameters are only applicable to a single signal with exclusive signal characteristics, rather than different signals with similar features. Therefore, we developed a new acoustic signal analysis method combining VMD, beetle antennae search (BAS), and naive Bayes classification (NBC), and then applied it for detecting internal defects of arc magnets. In this method, multiple optimizations for different signals are simplified to a one-time optimization for the whole signal group by a specially designed parameter-related fitness function. Since the coordinates of the function maximum value in a parameter space correspond to the unified parameter setting generating the overall optimal processing effect for all signals, BAS is introduced to achieve a rapid search of coordinates. With the obtained unified parameter setting, each acoustic signal of arc magnets can be consistently processed by VMD. Next, two modes stemmed from VMD are screened out by an energy threshold, and their specific frequency information is extracted as features representing the internal defects. NBC is carried out to learn and identify the extracted features. The experimental validation of the proposed method was conducted by detecting various arc magnets. Experimental results indicate that the identification accuracy reaches 100% and the detection speed per a single arc magnet approximately ranges between 1.7 and 4.5 s. This work provides not only a new strategy for the parameter optimization of VMD, but also a practical solution for the internal defect detection of arc magnets.