Bioelectrochemical cascade reaction for energy-saving hydrogen production and innovative Zn-air batteries.

The oxygen evolution reaction (OER) is an important half-reaction in electrochemical hydrogen production (EHP) and rechargeable metal-air batteries. However, the sluggish OER kinetics has seriously impeded their performance. Herein, we report a bioelectrochemical cascade system composed of glucose oxidase (GOx)-functionalized N-doped porous carbon nanofibers to replace OER in EHP and rechargeable Zn-air batteries (ZABs) applications. In this cascade system, GOx catalyzes oxidation of glucose to produce value-added gluconic acid accompanied with the generation of H2O2 under aerobic conditions. The subsequent electrocatalytic oxidation of H2O2 replacing the OER results in an onset voltage below 1.10 V for EHP, and a low charging voltage of 1.35 V as well as a small charging/discharging voltage gap of âˆ¼ 280 mV over 170 h for ZABs in neutral aqueous electrolytes. The advantages of employing the innovative bioelectrochemical cascade reaction are demonstrated in EHP and ZABs, achieving the full utilization of biomass energy in energy-saving electrochemical systems for energy storage and conversion.

Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation.

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 (Dij), 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 (R2 > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.