Image stack of Phymolepis cuifengshanensis

Image stack of Phymolepis cuifengshanensis (IVPP V4425.2) for virtual restoration

3D model of the head and trunk shields of Phymolepis cuifengshanensis

3D model of the head and trunk shields of Phymolepis cuifengshanensis (IVPP V4425.2)

Breaking the Linear Scaling Relationship of the Reverse Water–Gas–Shift Reaction via Construction of Dual-Atom Pt–Ni Pairs

Oxide-supported single-atom catalysts hold great potential for reverse water–gas–shift (RWGS) reactions. Nevertheless, it remains challenging to break the linear scaling relationships between the adsorption and desorption capability of catalysts. Herein, we report the design of ZrO2-anchored dual-atom Pt–Ni pairs for the RWGS reaction. The dual-atom material delivers a CO selectivity as high as 99.8% and a space-time yield of 157.2 μmolCO gcat–1 s–1 at atmospheric pressure. Theoretical calculations reveal that the dual-atom Pt–Ni pairs could direct the dual electronic transfer paths (dxz and dyz) to the 2π* orbitals of CO2 in the RWGS reaction, which achieve strong hybridization between them to enable efficient activation of CO2. Moreover, the delocalized charge in dual-atom Pt–Ni may lead to a facile desorption of the CO product

Breaking the Linear Scaling Relationship of the Reverse Water–Gas–Shift Reaction via Construction of Dual-Atom Pt–Ni Pairs

Oxide-supported single-atom catalysts hold great potential for reverse water–gas–shift (RWGS) reactions. Nevertheless, it remains challenging to break the linear scaling relationships between the adsorption and desorption capability of catalysts. Herein, we report the design of ZrO2-anchored dual-atom Pt–Ni pairs for the RWGS reaction. The dual-atom material delivers a CO selectivity as high as 99.8% and a space-time yield of 157.2 μmolCO gcat–1 s–1 at atmospheric pressure. Theoretical calculations reveal that the dual-atom Pt–Ni pairs could direct the dual electronic transfer paths (dxz and dyz) to the 2π* orbitals of CO2 in the RWGS reaction, which achieve strong hybridization between them to enable efficient activation of CO2. Moreover, the delocalized charge in dual-atom Pt–Ni may lead to a facile desorption of the CO product

Additional files 1: of N-carbamylglutamate restores nitric oxide synthesis and attenuates high altitude-induced pulmonary hypertension in Holstein heifers ascended to high altitude

Table S1. Ingredients and chemical composition of the total mixed ration. (DOCX 14 kb

S2 Data -

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.</div

UV-Filtering Cellulose Nanocrystal/Carbon Quantum Dot Composite Films for Light Conversion in Glass Windows

The development of energy-saving materials in buildings based on biomass materials is a general consensus building construction of low-carbon cities. Herein, we reported an effective ultraviolet (UV)-filtered film, composed of carbon quantum dots (CDs) and cellulose nanocrystals (CNC). The results showed that CNC had nanoscale dimensions with a diameter of 20–50 nm and length of 250–450 nm, a high crystallinity index of 84.7%, and enrichment of hydrogen bonds on the surface. The photoluminescence spectra showed that lignin-based carbon quantum dots (CDs) exhibited a long-wavelength red emission (623 nm) and an uncommon narrow emission bandwidth (fwhm <30 nm). In addition, the prepared UV-filtered film had strong mechanical tensile properties, good UV light absorbing capability, and water resistance. The optical test showed that the film also had highly optical transparency (94%) and haze (70%). The excellent light management and conversion function of this film provides a new user experience for soft, uniform, healthy, and comfortable indoor sunlight lighting

Fig 6 -

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.</div

S1 Data -

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.</div

Performance and mechanism of FeS₂/FeS_xO_y as highly effective Fenton-like catalyst for phenol degradation

Developing a highly efficient Fenton-like catalyst working in a wide pH range is imperative to accomplish its practical wastewater treatment. Herein, FeS2/FeSxOy catalyst was synthesized by hydrothermal-solvothermal vulcanization with thioacetamide as a sulfur source. Characterization results confirmed FeS2/FeSxOy consisted of pyrite, kornelite, and szomolnokite. FeS2/FeSxOy exhibited superior catalytic activity toward H2O2 activation with more than 96% phenol removal within 5 min in pH 3.0 ∼ 8.0 at 30°C. Radical scavenging experiment and EPR analysis revealed both hydroxyl radicals (·OH) and superoxide anion radicals (O2·-) anticipated in phenol elimination, but ·OH played a dominant role. The detailed degradation experiments and density functional theory (DFT) calculation confirmed the vital role of FeS2 in enhancing phenol abatement. This study not only developed a highly active catalyst for H2O2 activation but also theoretically analyzed the FeS2 function in depth, which provided a guide for designing a highly efficient Fenton-like catalyst.</p