Tuning the electronic conductance of REH_(x)(RE=Nd,Ce,Pr)by structural deformation

Hydride ion(H-)conductors have drawn much attention due to their potential applications in hydrideion-based devices.Rare earth metal hydrides(REH_(x))have fast H-conduction which,unfortunately,is accompanied by detrimental electron conduction preventing their application as ion conductors.Here,REH_(x)(RE=Nd,Ce,and Pr)with varied grain sizes,rich grain boundaries,and defects have been prepared by ball milling and subsequent sintering.The electronic conductivity of the ball-milled REH_(x)samples can be reduced by 2-4 orders of magnitude compared with the non-ball-milled samples.The relationship of electron conduction and miscrostructures in REH_(x)is studied and discussed based on experimental data and previously-proposed classical and quantum theories.The H-conductivity of all REH_(x)is about 10^(-4)to 10^(-3)S cm^(-1)at room temperature,showing promise for the development of H-conductors and their applications in clean energy storage and conversion.

Effect of BaNH,CaNH,Mg3N2 on the activity of Co in NH3 decomposition catalysis

Development of active and non-noble metal-based catalyst for H2 production via NH3 decomposition is crucial for the implementation of NH3 as a H2 carrier.Co-based catalysts have received increasing attention because of its high intrinsic activity and moderate cost.In this work,we examined the effect of BaNH,CaNH and Mg3 N2 on the catalytic activity of Co in the NH3 decomposition reaction.The H2 formation rate ranks the order as Co-BaNH>Co-CaNH>Co-Mg3 N2≈Co/CNTs within a reaction temperature range of 300-550℃.It is worth pointing out that the H2 formation rate of Co-BaNH at 500℃reaches20 mmolH2 gcat-1 min-1,which is comparable to those of the active Ru/Al2 O3(ca.17 mmolH2 gcat-1 min1)and Ru/AC(21 mmolH2 gcat-1 min-1)catalysts under the similar reaction conditions.In-depth research shows that Co-BaNH exhibits an obviously higher intrinsic activity and much lower Ea(46.2 kJ mol-1)than other Co-based catalysts,suggesting that BaNH may play a different role from CaNH,Mg3 N2 and CNTs during the catalytic process.Combined results of XRD,Ar-TPD and XAS show that a[Co-N-Ba]-like intermediate species is likely formed at the interface of Co metal and BaNH,which may lead to a more energy-efficient reaction pathway than that of neat Co metal for NH3 decomposition.

The Estimation of the Higher Heating Value of Biochar by Data-Driven Modeling

Biomass is a carbon-neutral renewable energy resource.Biochar produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel substitution.For time-and cost-saving,it is vital to establish predictive models to predict biochar properties.However,limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various biochar.Therefore,the multi-linear regression(MLR)and the machine learning(ML)models were developed to predict the measured HHV of biochar from the experiment data of this study.In detail,52 types of biochars were produced by pyrolysis from rice straw,pig manure,soybean straw,wood sawdust,sewage sludge,Chlorella Vulgaris,and their mixtures at the temperature ranging from 300 to 800℃.The results showed that the co-pyrolysis of the mixed biomass provided an alternative method to increase the yield of biochar production.The contents of ash,fixed carbon(FC),and C increased as the incremental pyrolysis temperature for most biochars.The Pearson correlation(r)and relative importance analysis between HHV values and the indicators derived from the proximate and ultimate analysis were carried out,and the measured HHV was used to train and test the MLR and the ML models.Besides,ML algorithms,including gradient boosted regression,random forest,and support vector machine,were also employed to develop more widely applicable models for predicting HHV of biochar from an expanded dataset(total 149 data points,including 97 data collected from the published literature).Results showed HHV had strong correlations(|r|>0.9,p<0.05)with ash,FC,and C.The MLR correlations based on either proximate or ultimate analysis showed acceptable prediction performance with test R2>0.90.The ML models showed better performance with test R^(2)around 0.95(random forest)and 0.97–0.98 before and after adding extra data for model construction,respectively.Feature importance analysis of the ML models showed that ash and C were the most important inputs to predict biochar HHV.

Dinitrogen fixation mediated by lanthanum hydride

Dinitrogen fixation is one of the key reactions in chemistry, which is closely associated with food, environment, and energy. It has been recently recognized that the hydride materials containing negatively charged hydrogen(H~-) show promises for Nfixation and hydrogenation to ammonia. Herein, we report that rare earth metal hydrides such as lanthanum hydride can also fix Neither by heating to 200 °C or ball milling under ambient Npressure and temperature. The Nfixation by lanthanum hydride may proceed via an intermediate lanthanum hydride-nitride(La-H-N) structure to form the final lanthanum nitride product. The hydride ion functions as an electron donor, which provides electrons for Nactivation possibly mediated by the lanthanum atoms. It is observed that N–H bond is not formed during the Nfixation process, which is distinctly different from the alkali or alkaline earth metal hydrides. The hydrolysis of La-H-N to ammonia is feasible using water as the hydrogen source. These results provide new insights into the nitrogen fixation by hydride materials and more efforts are needed for the development of rare earth metal-based catalysts and/or nitrogen carriers for ammonia synthesis processes.

Mild-condition synthesis of A2ZnH4(A = K, Rb, Cs) and their effects on the hydrogen storage properties of 2LiH-Mg(NH2)2

In this paper, A2ZnH4(A = K, Rb and Cs) have been synthesized for the first time by a new approach involving in two-step reactions, in which the target samples can be produced under mild conditions(160 ℃ for 4 h). What’s more, the additive effects of A2ZnH4 on the hydrogen storage properties of 2LiH-Mg(NH2)2 composite have been investigated systematically. Experimental results show that K2ZnH4 has the best comprehensive modification effects among these hydrides. The 2LiH-Mg(NH2)2-0.1 K2ZnH4 sample shifts dehydrogenation peak temperature downwards by ca. 30 ℃ as compared to the pristine sample. In addition, about 70% extent of the theoretical hydrogen is able to desorb from the 0.1 K2ZnH4 doped sample at 140 ℃ within 2 h, however, only 20% extent of hydrogen is liberated from the pure sample under the same conditions. The improved desorption kinetics is indicated by the reduced dehydrogenation activation energy(Ea), the Ea of the 0.1 K2ZnH4 doped sample is around 68 ± 1.0 kJ mol-1 which is 28% lower than that of the pristine one. Furthermore, the dehydrogenation mechanism of the K2ZnH4 doped sample has been proposed.

Effect of TRX Suspension Training on Physical Function in Elderly Men with Sarcopenia

Elderly people have reduced muscle mass,decreased physical function and an increased hospitalization probability,which affect their quality of life and even lead to death in severe cases.Scholars at home and abroad have studied progressive body muscle loss associated with increase of age,decreased muscle strength and muscle function impairment syndrome and how to prevent them.Much research have been done preventing diseases and improving muscle strength through exercising,nutrition,and drug intervention,and these measures have been proven effective.This article describes the effect of exercising in reducing muscle disease,improving muscle mass,and physical abilities of the elderly.This study can not only effectively enrich the research results related to exercise therapy of sarcopenia,but is also in line with national strategies and policies such as the“national fitness campaign,”“Healthy China 2030 Plan Outline,”and“Healthy China Action 2019-2030”depending on the health conditions of the elderly population.Besides,this paper aims to develop individualized exercise programs,provide scientific basis for the prevention and treatment of sarcopenia in the elderly,reduce the risk of fall damage in the elderly,and promote the physical health level of the elderly.

Machine learning predicting and engineering the yield,N content,and specific surface area of biochar derived from pyrolysis of biomass

Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications.The specific surface area(SSA)and functionalities such as N-containing functional groups of biochar are the most significant properties determining the application performance of biochar as a carbon material in various areas,such as removal of pollutants,adsorption of CO_(2)and H2,catalysis,and energy storage.Producing biochar with preferable SSA and N functional groups is among the frontiers to engineer biochar materials.This study attempted to build machine learning models to predict and optimize specific surface area of biochar(SSA-char),N content of biochar(N-char),and yield of biochar(Yield-char)individually or simultaneously,by using elemental,proximate,and biochemical compositions of biomass and pyrolysis conditions as input variables.The predictions of Yield-char,N-char,and SSA-char were compared by using random forest(RF)and gradient boosting regression(GBR)models.GBR outperformed RF for most predictions.When input parameters included elemental and proximate compositions as well as pyrolysis conditions,the test R^(2) values for the single-target and multi-target GBR models were 0.90-0.95 except for the two-target prediction of Yield-char and SSA-char which had a test R^(2) of 0.84 and the three-target prediction model which had a test R^(2) of 0.81.As indicated by the Pearson correlation coefficient between variables and the feature importance of these GBR models,the top influencing factors toward predicting three targets were specified as follows:pyrolysis temperature,residence time,and fixed carbon for Yield-char;N and ash for N-char;ash and pyrolysis temperature for SSA-char.The effects of these parameters on three targets were different,but the trade-offs of these three were balanced during multi-target ML prediction and optimization.The optimum solutions were then experimentally verified,which opens a new way for designing smart biochar with target properties and oriented application potential.