Proton exchange membrane fuel cells are promising candidates for a clean and efficient energy conversion in the future, the development of carbon based inexpensive non-precious metal ORR catalyst has becoming one of the most attractive topics in fuel cell field. Herein we report a Fe- and N- doped carbon catalyst Fe-PANI/C-Mela with graphene structure and the surface area up to 702 m2 g−1. In 0.1 M HClO4 electrolyte, the ORR onset potential for the catalyst is high up to 0.98 V, and the half-wave potential is only 60 mV less than that of the Pt/C catalyst (Loadings: 51 μg Pt cm−2). The catalyst shows high stability after 10,000 cyclic voltammetry cycles. A membrane electrode assembly made with the catalyst as a cathode is tested in a H2-air single cell, the maximum power density reached ~0.33 W cm2 at 0.47 V.

BackgroundAbove-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.ResultsIn this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.ConclusionsThese results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.Electronic supplementary materialThe online version of this article (10.1186/s13007-019-0394-z) contains supplementary material, which is available to authorized users.