Manipulating the Solvation Structure and Interface via a Bio-Based Green Additive for Highly Stable Zn Metal Anode.

The practical application of aqueous zinc-ion batteries (AZIBs) is limited by serious side reactions, such as the hydrogen evolution reaction and Zn dendrite growth. Here, the study proposes a novel adoption of a biodegradable electrolyte additive, γ-Valerolactone (GVL), with only 1 vol.% addition (GVL-to-H2 O volume ratio) to enable a stable Zn metal anode. The combination of experimental characterizations and theoretical calculations verifies that the green GVL additive can competitively engage the solvated structure of Zn2+ via replacing a H2 O molecule from [Zn(H2 O)6 ]2+ , which can efficiently reduce the reactivity of water and inhibit the subsequent side reactions. Additionally, GVL molecules are preferentially adsorbed on the surface of Zn to regulate the uniform Zn deposition and suppress the Zn dendrite growth. Consequently, the Zn anode exhibits boosted stability with ultralong cycle lifespan (over 3500 h) and high reversibility with 99.69% Coulombic efficiency. The Zn||MnO2 full batteries with ZnSO4 -GVL electrolyte show a high capacity of 219 mAh g-1 at 0.5 A g-1 and improved capacity retention of 78% after 550 cycles. This work provides inspiration on bio-based electrolyte additives for aqueous battery chemistry and promotes the practical application of AZIBs.

Electrospinning Preparation of Nanostructured g-C3N4/BiVO4 Composite Films with an Enhanced Photoelectrochemical Performance.

Nanostructured g-C3N4/BiVO4 composite films with an enhanced photoelectrochemical (PEC) performance have been fabricated via the facile electrospinning technique. The g-C3N4 nanosheets can not only form heterojunctions with BiVO4 but also prevent the agglomeration of BiVO4, helping the formation of nanostructures. The as-prepared g-C3N4/BiVO4 films exhibit good coverage and stability. The PEC performance of the g-C3N4/BiVO4 films is much more enhanced compared with that for individual BiVO4 films because of the enhanced electron-hole separation. The photocurrent density is 0.44 mA/cm2 for g-C3N4/BiVO4 films at 0.56 V in the linear sweep current-voltage test, over 10 times higher than that of individual BiVO4 films (0.18 mA/cm2). The effects of the preparation conditions including the g-C3N4 content, collector temperature, calcination temperature, and electrospinning time on the PEC performance were investigated, and the reasons for the effects were proposed. The optimal preparation condition was with 3.9 wt % g-C3N4 content in the electrospinning precursor, 185 °C collector temperature, 450 °C calcination temperature, and 40 min electrospinning time. The excellent PEC performance and the facile preparation method suggest that the g-C3N4/BiVO4 films are good candidates in energy and environmental remediation area.

Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction.

Raman spectra have been widely used in biology, physics, and chemistry and have become an essential tool for the studies of macromolecules. Nevertheless, the raw Raman signal is often obscured by a broad background curve (or baseline) due to the intrinsic fluorescence of the organic molecules, which leads to unpredictable negative effects in quantitative analysis of Raman spectra. Therefore, it is essential to correct this baseline before analyzing raw Raman spectra. Polynomial fitting has proven to be the most convenient and simplest method and has high accuracy. In polynomial fitting, the cost function used and its parameters are crucial. This article proposes a novel iterative algorithm named Goldindec, freely available for noncommercial use as noted in text, with a new cost function that not only conquers the influence of great peaks but also solves the problem of low correction accuracy when there is a high peak number. Goldindec automatically generates parameters from the raw data rather than by empirical choice, as in previous methods. Comparisons with other algorithms on the benchmark data show that Goldindec has a higher accuracy and computational efficiency, and is hardly affected by great peaks, peak number, and wavenumber.

Bi-factor analysis based on noise-reduction (BIFANR): a new algorithm for detecting coevolving amino acid sites in proteins.

Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.