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您的检索式：作者名="M.F.Hossain"
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| 1 | Novel micro-ring structured ZnO photoelectrode for dye-sensitized solar cell显示文摘The micro-ring like structured zinc oxide(ZnO) film was deposited on SnO_2: F coated glass substrate by sol-gel dip-coating technique with 1.0 g polyethylene glycol(PEG) content. The surface morphology of micro-ring structured ZnO film has been confirmed by the scanning electron microscope. This ZnO film is used to fabricate the solar cell with the help of ruthenium based dye and carbon counter electrode. The photoelectric and incident photon-to-current conversion efficiency was 1.17% and 48.4%, respectively. The DSC results have been compared with ZnO films prepared without PEG contents. | M.F.Hossain Z.H.Zhang T.Takahashi | 2010 | Nano-Micro Letters2010，2，1： | 3 |
| 2 | Bulk density of mineral and organic soils in the Canada’s arctic and sub-arctic显示文摘Bulk density is an indicator of soil compaction subject to anthropogenic impact,essential to the interpretation of any nutrient budgets,especially to perform carbon inventories.It is so expensive to measure bulk density in arctic/sub-arctic and there are relatively very few field measurements are available.Therefore,to establish a bulk density and SOC empirical relationship in Canada’s arctic and sub-arctic ecosystems,compiled all the bulk density and SOC measurements that are available in Northern Canada.In addition an attempt has been made for bulk density and SOC field measurement in Yellowknife and Lupin,to develop an empirical relationship for Canada’s arctic and sub-arctic.Relationships between bulk density(BD)and soil organic carbon(SOC)for mineral soil and organic soils(0–100 cm depth)were described by exponential functions.The best fit model,predictive bulk density(BDp),for mineral soil,(BDp=0.701+0.952 exp(0.29 SOC),n=702,R2=0.99);for organic soil(BDp=0.074+2.632 exp(0.076 SOC),n=674,R2=0.93).Different soil horizons have different bulk densities and may require different predictive equations,therefore,developed predictive best fit exponential equation for both mineral and organic soils together(BDp=0.071+1.322 exp(0.071 SOC),n=1376,R2=0.984),where X is a dummy variable with a value of 0 for surface peat(0–25 cm depth)and 1 for subsurface peat(25–175 cm).We recommend using the soil organic carbon density approach to estimate BD from SOC because it allows BD to be predicted without significant bias. | M.F.Hossain W.Chen Yu Zhang | 2015 | Information Processing in Agriculture2015，2，3： | 1 |
| 3 | Uncertainty assessment of a polygon database of soil organic carbon for greenhouse gas reporting in Canada’s Arctic and sub-arctic显示文摘Canada’s Arctic and sub-arctic consist 46%of Canada’s landmass and contain 45%of the total soil organic carbon(SOC).Pronounced climate warming and increasing human disturbances could induce the release of this SOC to the atmosphere as greenhouse gases.Canada is committed to estimating and reporting the greenhouse gases emissions and removals induced by land use change in the Arctic and sub-arctic.To assess the uncertainty of the estimate,we compiled a site-measured SOC database for Canada’s north,and used it to compare with a polygon database,that will be used for estimating SOC for the UNFCCC reporting.In 10 polygons where 3 or more measured sites were well located in each polygon,the site-averaged SOC content agreed with the polygon data within±33%for the top 30 cm and within±50%for the top 1 m soil.If we directly compared the SOC of the 382 measured sites with the polygon mean SOC,there was poor agreement:The relative error was less than 50%at 40%of the sites,and less than 100%at 68%of the sites.The relative errors were more than 400%at 10%of the sites.These comparisons indicate that the polygon database is too coarse to represent the SOC conditions for individual sites.The difference is close to the uncertainty range for reporting.The spatial database could be improved by relating site and polygon SOC data with more easily observable surface features that can be identified and derived from remote sensing imagery. | M.F.Hossain Yu Zhang W.Chen | 2014 | Information Processing in Agriculture2014，1，1： | 1 |
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