Transfer learning guided discovery of efficient perovskite oxide for alkaline water oxidation.

Perovskite oxides show promise for the oxygen evolution reaction. However, numerical chemical compositions remain unexplored due to inefficient trial-and-error methods for material discovery. Here, we develop a transfer learning paradigm incorporating a pre-trained model, ensemble learning, and active learning, enabling the prediction of undiscovered perovskite oxides with enhanced generalizability for this reaction. Screening 16,050 compositions leads to the identification and synthesis of 36 new perovskite oxides, including 13 pure perovskite structures. Pr0.1Sr0.9Co0.5Fe0.5O3 and Pr0.1Sr0.9Co0.5Fe0.3Mn0.2O3 exhibit low overpotentials of 327 mV and 315 mV at 10 mA cm-2, respectively. Electrochemical measurements reveal coexistence of absorbate evolution and lattice oxygen mechanisms for O-O coupling in both materials. Pr0.1Sr0.9Co0.5Fe0.3Mn0.2O3 demonstrates enhanced OH- affinity compared to Pr0.1Sr0.9Co0.5Fe0.5O3, with the emergence of oxo-bridged Mn-Co conjugate facilitating charge redistribution and dynamic reversibility of Olattice/VO, thereby slowing down Co dissolution. This work paves the way for accelerated discovery and development of high-performance perovskite oxide electrocatalysts for this reaction.

A novel 3D-fluorescence sensing strategy based on HN-chitosan polymer probe for rapid identification and quantification of potential adulteration in saffron.

Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry.

[Research on the Method of Eliminating Noise and Background in the Meantime in Detecting Ethanol Contention Based on Raman Spectra].

In the process of detecting ethanol content by Raman spectra, the precision of correction model prediction is affected by noise and baseline drift, which is caused by the spectral fluorescence and sample pool's background. Use ensemble empirical mode decomposition to decompose spectrum into several intrinsic mode functions, which are without aliasing. The permutation entropy is employed to judge the intrinsic mode functions. Set the intrinsic mode functions which are on behalf of noise and background to zero, and then the signal is without noise and background. In this paper combine ensemble empirical mode decomposition and permutation entropy, and apply to the Raman spectrum, which are used to detect ethanol content. At the same time compare with wavelet transform and average smoothing filter. The experimental result shows that the application of empirical mode decomposition and permutation entropy can effectively eliminate the noise and background. The precision of correction model prediction is improved. This method simply employs and doesn't need to set parameters, which has great value of application in the process of detecting ethanol content by Raman spectra.

[The method of removing breathing baseline in pulse wave signal].

A method which combines empirical mode decomposition with wavelet transform is employed to remove breathing baseline draft from pulse wave signal. First of all, this method decomposes pulse wave signal into several intrinsic mode functions and judges the intrinsic mode function which contains the information of breathing baseline draft. And then wavelet transform is used to decompose these intrinsic mode functions, and the detail coefficients representing breathing baseline draft are set into zero. At last, the signal is rebuilt. This can realize removing breathing baseline draft. A self-developed measurement device was used to obtain the pulse wave signal for validating, and AC-DC modulation ratio value was adopted to evaluate the effect. The results showed that this method could effectively remove breathing baseline draft from pulse wave signal.

[The method of removing high-frequency noise in pulse wave signal in detecting oxygen saturation of human].

Photoplethysmography can be used to noninvasively detect oxygen saturation of human. When detecting by photoplethysmography, because of the disturbance of random noise in the process of signal acquisition, there is high-frequency noise, which affects the final prediction accuracy of oxygen saturation. Therefore empirical mode decomposition(EMD) method based on consecutive mean square error(CMSE) criterion is employed, which can remove high-frequency noise from pulse wave signal. The present paper used a self-developed photoplethysmography acquiring device to obtain the pulse wave signal, employed the above mentioned method to remove high-frequency noise, and adopted frequency spectrum of the signal to evaluate the effect. The results showed that: this method could effectively remove high-frequency noise from pulse wave signal. This would be beneficial for improving the prediction accuracy of oxygen saturation of human.