Rational design of Bi2Sn2O7/Bi5O7I S-scheme heterojunction for visible photocatalytic oxidation of emerging pollutants.

The construction of an S-scheme heterostructure is considered as a promising strategy for enhancing photocatalytic performance. Herein, a three-dimensional Bi5O7I (BOI) microsphere decorated with Bi2Sn2O7 (BSO) nanoparticles was prepared for the first time via a simple ultrasonic-assisted electrostatic self-assembly strategy and used for the degradation of 2,4-dinitrophenylhydrazine. 3 wt% Bi2Sn2O7/Bi5O7I has the highest degradation activity (93.7 %), with an apparent rate constant of 0.0848 min-1, which is 2.55 times that of the original Bi5O7I (0.0333 min-1). Moreover, the optimal binary heterojunction photocatalyst has good reusability and universal applicability. The results of cyclic voltammetry tests clarify that the optimal photocatalyst can provide more surface reactive sites. The results of radical trapping experiments and electron spin resonance indicate that holes (h+) and superoxide radicals are the main active radicals in the degradation process of 2,4-dinitrophenylhydrazine. Photoelectrochemical and photoluminescence confirm that 3 wt% Bi2Sn2O7/Bi5O7I composites exhibit the highest separation rate of photogenerated carriers. Finally, based on the results of experimental studies and theoretical calculations, the S-scheme charge transfer path on Bi2Sn2O7/Bi5O7I composite is determined. This work provides a new perspective on how to design high-performance S-scheme bismuth oxyhalide-based heterojunction photocatalysts for solar energy conversion.

Biochemical and Structural Insights into a Thiamine Diphosphate-Dependent α-Ketoglutarate Decarboxylase from Cyanobacterium Microcystis aeruginosa NIES-843.

α-Ketoglutarate decarboxylase is a crucial enzyme in the tricarboxylic acid cycle of cyanobacteria, catalyzing the non-oxidative decarboxylation of α-ketoglutarate to produce succinate semialdehyde and CO2. The decarboxylation process is reliant on the cofactor of thiamine diphosphate. However, this enzyme's biochemical and structural properties have not been well characterized. In this work, two α-ketoglutarate decarboxylases encoded by MAE_06010 and MiAbw_01735 genes from Microcystis aeruginosa NIES-843 (MaKGD) and NIES-4325 (MiKGD), respectively, were overexpressed and purified by using an Escherichia coli expression system. It was found that MaKGD exhibited 9.2-fold higher catalytic efficiency than MiKGD, which may be attributed to the absence of glutamate decarboxylase in Microcystis aeruginosa NIES-843. Further biochemical investigation of MaKGD demonstrated that it displayed optimum activity at pH 6.5-7.0 and was most activated by Mg2+. Additionally, MaKGD showed substrate specificity towards α-ketoglutarate. Structural modeling and autodocking results revealed that the active site of MaKGD contained a distinct binding pocket where α-ketoglutarate and thiamine diphosphate interacted with specific amino acid residues via hydrophobic interactions, hydrogen bonds and salt bridges. Furthermore, the mutagenesis study provided strong evidence supporting the importance of certain residues in the catalysis of MaKGD. These findings provide new insights into the structure-function relationships of α-ketoglutarate decarboxylases from cyanobacteria.

Effect of Bi2WO6/g-C3N4 composite on the combustion and catalytic decomposition of energetic materials: An efficient catalyst with g-C3N4 carrier.

An effective strategy involving a suitable carrier is needed to improve the dispersion, combustion and catalytic performances of catalyst nanoparticles. Herein, a Bi2WO6/g-C3N4 composite employing g-C3N4 as the catalyst carrier was prepared by a one-step in situ hydrothermal method, which was used as the combustion catalyst of solid propellants. The catalyst's structure, morphology and its catalytic decomposition on several energetic materials were characterized by a series of analyses. The optimal ratio of g-C3N4 and Bi2WO6 was systematically determined. The results demonstrate that Bi2WO6/g-C3N4 (4:6) composite can diminish the decomposition temperatures of ammonium perchlorate (AP), cyclotrimethylenetrinitramine (RDX), dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50) and cyclotrimethylenetrinitramine + nitrocellulose (RDX + NC) by 25.0, 5.2, 24.0 and 1.2 (4.9) ° C, and reduce their apparent activation energy by 59.5, 116.7, 11.6 kJ mol-1, respectively. Moreover, the laser ignition tests indicate that Bi2WO6/g-C3N4 can effectively promote the ignition performance of RDX and RDX + NC. A possible mechanism of Bi2WO6/g-C3N4 on AP was proposed. The g-C3N4 catalyst carrier is superior to GO carrier due to its low cost, simple synthesis process, improved combustion and catalytic performances, as well as high N content. These make it have broad engineering application prospects in solid propulsion and other energetic materials.

Efficient activation of peroxydisulfate by g-C3N4/Bi2MoO6 nanocomposite for enhanced organic pollutants degradation through non-radical dominated oxidation processes.

Persulfate-assisted photocatalysis technology is considered to be a promising method for the rapid and efficient degradation of organic pollutants in water environment remediation. In this study, a novel g-C3N4/Bi2MoO6/PDS (CN/BMO/PDS) system is constructed and applied in 2,4-dinitrophenylhydrazine (2,4-DPH) degradation under visible light irradiation. Compared with the CN/BMO system, the degradation rate of 2,4-DPH is significantly improved from 59.7% to 90.2% within 60 min in the combined CN/BMO/PDS system. The enhanced performance can be attributed to the superior synergetic effects of CN/BMO, PDS and visible light irradiation. More importantly, singlet oxygen (1O2) is determined as the main reactive species based on the radical scavenging experiments and electron paramagnetic resonance (EPR), which indicates that the combined system can achieve non-radical oxidative degradation of pollutants, instead of the traditional radical oxidation process. In addition, the active sites of the reaction during the non-radical 1O2 oxidation are calculated by density functional theory (DFT), and the stability and reusability of catalyst are also investigated. In brief, the CN/BMO/PDS system has great application potential for removing organic pollutants from wastewater.

The rapid H2 release from AlH3 dehydrogenation forming porous layer in AlH3/hydroxyl-terminated polybutadiene (HTPB) fuels during combustion.

Although the motivation of AlH3 enhancing combustion were recognized in many research, the promotion mechanism have been rarely explored. Herein, a previously unreported porous layer mechanism when combustion were determined in HTPB/AlH3 fuels by SEM, thermo-analysis and a new simplified calculation method, owing to rapidly released gas phase H2 from AlH3 dehydrogenation exposing in melting layer. 5/10% 40-80 µm and 10% 80-200 µm AlH3-HTPB formulas show the regression rate increase by, 25.7%, 29.0% and 43.0% at Gox = 350 kg/m2·s, while by 57.2%, 42.0% and 44.2% enhancement at Gox = 150 kg/m2·s. The low AlH3 content (≤ 10%) promotes the regression rate obviously, while excess AlH3 content (≥ 20%) promotes slightly as a result of comprehensive factors combined by energy release, a certain porous layer mechanism, aggregated Al2O3 attached on the burning surface and the blocking effect of the gaseous released H2. A new model predicting the overlapping process of AlH3 dehydrogenation and Al oxidation in air atmosphere was developed by superimposing AlH3 dehydrogenation simulation and corresponding separated Al oxidation simulation. A 1.5th Avrami-Erofeev (A-E) simulation was proposed for Al passivation weight gain between 420 and 520 K with an activation energy of 124.92 kJ/mol and the pre-exponential of 10^12.35.

SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy.

Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.