Blank sample denoising algorithm (BSDA): An effective spectral noise reduction in water sample LIBS detection.

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies.

Combined LIBS and Raman spectroscopy: an approach for salinity detection in the field of seawater investigation.

As salinity is an important indicator in marine geology, ecology, breeding, and other fields, accurate, rapid, and continuous measurement of salinity is of great significance in marine investigations. At present, the seawater salinity detection methods used in practice are mainly based on the principle that the conductivity and refractive index parameters of the water change with the concentration of elements, which are composed of salinity change. However, these methods quantitatively analyze salinity values by measuring other parameters (electrical or optical parameters) that may change depending on the salinity of the water, rather than the mass fraction of the components that make up the salinity. Moreover, when the salinity value of seawater water changes substantially or the proportion of various common components composing salinity changes significantly, the detection accuracy of the above methods is difficult to guarantee. Therefore, a spectral approach, LIBS, and the Raman spectroscopy combination method for salinity analyzation, LRSS, were proposed to provide a new option. The main idea of this approach is to use the two spectral detection methods, LIBS and Raman, to determine the mole values of cations and non-monatomic anions in per unit quality (1 kg) of water, respectively. Then the mole value of the chloride ion, which is the main monatomic anion in seawater, can be determined according to the electrically neutral principle. Based on all the obtained molar values and the molar mass of each ion, the salinity of the water sample can be determined. To demonstrate the performance of this new method, we compared it with LIBS under laboratory conditions and found that, when non-monatomic anions are present in the water, the accuracy of LRSS is significantly improved compared to using the LIBS method alone. Moreover, we also compared the LRSS with the other two traditional methods through the 11 water samples configured and found that the absolute value relative error of the LRSS is only 2.63% when the salinity and components concentration change is in the possible range, which is better than the conductivity method 0.53 times and better than the refractive index method 1.52 times.

Yeast encapsulation in nanofiber via electrospinning: Shape transformation, cell activity and immobilized efficiency.

To realize encapsulation of living microbial cells and easily evaluation of cell viability after immobilization, the yeast cells were encapsulated in water soluble PAAm nanofiber by a facile and effective electrospinning technology. Firstly, the conductivity, shear viscosity and surface tension of PAAm/yeast electrospinning solution as a function of mass ratios of yeast/PAAm were investigated to determine the optimum solution condition for electrospinning immobilization. After electrospinning, it is interesting to note that the original ellipsoidal structure of yeast cells turns to oblate spheroid structure. To distinguish immobilization structure from the bead appearing during general electrospinning process, immobilization structure and bead structure were compared and analyzed by FESEM and EDX. Free cell activity, the immediate cell activity after electrospinning and cell activity for seven days storage after immobilization were evaluated by dying methods of CTC and methylene blue, respectively. The results show that encapsulation efficiency maintained at about 40%, and immobilized yeast cells remain active even after seven days storage, which provides a promising application prospect for electrospinning immobilization.