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Changwen Du

Researcher at Chinese Academy of Sciences

Publications -  108
Citations -  3030

Changwen Du is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Partial least squares regression & Soil test. The author has an hindex of 21, co-authored 92 publications receiving 2146 citations.

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A global spectral library to characterize the world’s soil

R. A. Viscarra Rossel, +41 more
TL;DR: In this article, the authors developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library, which is currently the largest and most diverse database of its kind, and showed that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability.
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Risk assessment of potentially toxic element pollution in soils and rice (Oryza sativa) in a typical area of the Yangtze River Delta

TL;DR: The risk assessment of PTEs through rice consumption suggests that the concentrations of Cu, Pb, and Cd in some rice samples exceed their reference oral dose for adults and children.
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Removal of phosphate from aqueous solution by thermally treated natural palygorskite.

TL;DR: The dependence of the phosphate sorption capacity in the heating samples on thermal activation appears to be related to major changes in the crystal structure of palygorskite, and more calcium, iron and aluminum were released from the crystal matrix at 700 degrees C, which promoted phosphorus sorption.
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Release Characteristics of Nutrients from Polymer-coated Compound Controlled Release Fertilizers

TL;DR: In this paper, the energy of activation, EArel, estimated for the linear release, of the different nutrients, was narrow ranging between 37 to 46 KJmol−1 whereas the mean values obtained for the two coated compound CRF, differing by 50% in coating thickness, was non-significant.
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Evaluation of Soil Fertility Using Infrared Spectroscopy – A Review

TL;DR: Wang et al. as mentioned in this paper used IR-photoacoustic spectra to predict soil C and N contents using partial least square (PLS) and artificial neural network (ANN) models.