No abstract

No abstract

No abstract

Zn–air batteries (ZABs) are one of the promising candidates of future energy storage technology owing to their advantages of high theoretical energy density, high safety, and low cost. However, high voltage polarization and low energy efficiency hinder their practical applications. Herein, we show that the charging voltage of a ZAB can be reduced to ∼1.6 V with a high energy efficiency of ∼70% by adding a redox radical, 2,2,6,6-tetramethylpiperidinyloxyl (TEMPO), and a biomass, glucose, into the electrolyte. Upon charging, the oxidation of TEMPO forming the oxoammonium cation at the cathode catalyzes the oxidation of glucose to generate value-added derivatives. Operando differential electrochemical mass spectrometry, first principle calculations, and ex situ spectroscopic characterizations demonstrate the significant effects of TEMPO and glucose on inhibiting side reactions and dendrite growth of the Zn anode, which endow the TEMPO-mediated ZABs with long-term charging/discharging cycles over 400 h.

Extensive studies have focused on assessing leaf chlorophyll content through spectral indices; however, the accuracy is weakened by limited wavebands and coarse resolution. With hundreds of wavebands, hyperspectral data can substantially capture the essential absorption features of leaf chlorophyll; however, few such studies have been conducted on same species in various degraded vegetations. In this investigation, complete combinations of either original reflectance or first‐order derivative spectra we conducted a complete combination on either original reflectance or its first‐order derivative value from 350 to 1000 nm to quantify leaf total chlorophyll (Chll), chlorophyll‐a (Chla), and chlorophyll‐b (Chlb) contents. This was performed using three hyperspectral datasets collected in situ from lightly, moderately, and severely degraded vegetations in temperate Helin County, China. Suitable combinations were selected by comparing the numbers of significant correlation coefficients with leaf Chll, Chla, and Chlb contents. The combinations of reflectance difference (D ij), normalized differences (ND), first‐order derivative (FD), and first‐order derivative difference (FD(D)) were found to be the most effective. These sensitive band‐based combinations were further optimized by means of a stepwise linear regression analysis and were compared with 43 empirical spectral indices, frequently used in the literature. These sensitive band‐based combinations on hyperspectral data proved to be the most effective indices for quantifying leaf chlorophyll content (R 2 > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.