Hydration Characteristics and Mechanical Properties of Cement-Based Materials Modified by Calcined Zeolite and Montmorillonite

Montmorillonite and clinoptilolite zeolite were used as representative materials to prepare calcined clay-cement binary cementitious materials in order to study the effect of calcination treatment on the activation of clay minerals and the activity difference between layered and framed clays in this research.The influence of different calcined clay content(2%,4%,6%,8%,10%)on the fluidity,compressive strength,microstructure,phase change,and hydration heat of cement-based materials were analyzed.The calcined clay improves the fluidity of cement-based materials as compared with the uncalcined group.The addition of calcined montmorillonite(CMT)improves the development of mechanical strength,and the optimal compressive strength reaches 85 MPa at 28 days with 8%CMT.However,the activity of calcined clinoptilolite zeolite(CZL)is weak with few reaction sites,which slightly reduced the mechanical strength as compared to the blank sample.The addition of CMT changes the microscopic morphology of hydration products such as C-S-H and C-A-H,leading to the formation and transformation of ettringite in the early stage.It promotes the gradual polymerization of Si-O bonds into Si-O-Si bonds simultaneously,which accelerates the early hydration process.However,CZL acts mainly as a filling function in the cementitious system.In brief,CMT as an admixture can improve the mechanical properties of cement,but CZL has little effect.This work provides a guideline for the applications of calcined clay in cement,considering the influence of clay type on workability and mechanical strength.

Growing-season soil microbial respiration response to long-term no tillage and spring ridge tillage

No tillage(NT)and spring ridge tillage(SRT)are two common applications of conservation tillage.Although conservation tillage is known to exert major control over soil microbial respiration(SMR),the growing-season SMR response to these two applications remains elusive.In order to better understand the influence of conservation tillage practices,this experiment was conducted in an experimental field using NT and SRT for 17 years.In situ measurements of SMR,soil temperature and soil water content(SWC)were performed.Soil samples were collected to analyze soil porosity,soil microbial biomass(SMB)and soil enzymatic activities.Results show that the two conservation tillage systems had a significant difference(p<0.05)in terms of SMR;the SMR of NT was 14.7 mg∙C/m^(2)∙h higher than that of SRT.In terms of soil temperature and soil enzymatic activities,the two treatments were not significantly different(p>0.05).Despite SRT increasing the proportion of micro-porosities and meso-porosities,the soil macro-porosities for NT were 7.37%higher than that of SRT,which resulted in higher bacteria and fungi in NT.Owing to SRT damaged the hypha,which had disadvantage in soil microbe protection.Inversely,less soil disturbance was a unique advantage in NT,which was in favor of improving soil macro-pores and SWC.Redundancy analyses(RDA)showed SMR was positively correlated with soil macro-pores,SMB and SWC.Furthermore,the Pearson correlation test indicated that SMB and soil enzymatic activities did not have a significant correlation(p>0.05).This study results suggest that SRT is more conducive to carbon sequestration compared with NT in cropland.

Application of machine learning to assess people' s perception of household energy in the developing world: A case of Nepal

Research on social aspects of energy and those applying machine learning(ML)is limited compared to the‘hard’disciplines such as science and engineering.We aim to contribute to this niche through this multidisciplinary study integrating energy,social science and ML.Specifically,we aim:(i)to compare the applicability of different ML models in household(HH)energy;and(ii)to explain people’s perception of HH energy using the most appropriate model.We carried out cross-sectional survey of 323 HHs in a developing country(Nepal)and extracted 14 predictor variables and one response variable.We tested the performance of seven ML models:K-Nearest Neighbors(KNN),Multi-Layer Perceptron(MLP),Extra Trees Classifier(ETC),Random Forest(RF),Ridge Classifier(RC),Multinomial Regression–Logit(MR-L)and Probit(MR-P)in classifying people’s responses.The models were evaluated against six metrics(confusion matrix,precision,f1 score,recall,balanced accuracy and overall accuracy).In this study,ETC outperformed all other models demonstrating a balanced accuracy of 0.79,0.95 and 0.68 respectively for the Agree,Neutral and Disagree response categories.Results showed that,compared to conventional statistical models,data driven ML models are better in classifying people’s perceptions.It was seen that the majority of the surveyed people from rural(68%)and semi-urban areas(67%)tend to resist energy changes due to economic constraints and lack of awareness.Interestingly,most(73%)of the urban residents are open to changes,but still resort to fuel-stacking because of distrust in the state.These grass-root level responses have strong policy implications.