One of the best approaches to date to obtain overall binding constants (Ko) for Al and dissolved organic matter (DOM) from acidic soil solutions is to collect 'free' Al data with diffusive gradients in thin films (DGT) and to infer the Ko values by fitting a continuous distribution model based on Scatchard plots. Although there is clear established literature demonstrating the usefulness of the Scatchard approach, relatively little attention has been given to a realistic assessment of the uncertainties associated with the final fitted Ko values. In this study we present an uncertainty analysis of the fitted Ko values using a synthetic dataset with different levels of random noise and a real data set using DGT data from an acidic soil solution. The parameters in the continuous distribution model and their corresponding upper and lower 95% uncertainty bounds were determined using the Shuffled Complex Evolution Metropolis (SCEM) algorithm. Although reasonable fits of the distribution model to the experimental data were obtained in all cases, an appreciable uncertainty in the resulting Ko values was found due to three main reasons. Firstly, obtaining 'free' Al data even with the DGT method is relatively difficult, leading to uncertainty in the data. Secondly, before Scatchard plots can be constructed, the maximum binding capacity (MBC) must be estimated. Any uncertainty in this MBC propagates into uncertainty associated with the final plots. Thirdly, as the final fitted Ko values are largely based on extrapolation, a small uncertainty in the fit of the binding data results in an appreciable uncertainty in the obtained Ko. Therefore, while trends in Ko for Al and DOM could easily be discerned and compared, the uncertainty in the Ko values hinders the application in quantitative speciation calculation. More comprehensive speciation models that avoid the use of Ko seem to fit better for this purpose.
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School of Health Sciences, Western Sydney University, Campbelltown, NSW 2560, Australia.
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Department of Applied Mathematics, College of Applied Sciences, Kyung Hee University, Yongin 17104, Republic of Korea.
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Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
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Department of Psychology, University of Liverpool, Liverpool L69 7ZA, United Kingdom.
Funding of curiosity-driven science is the lifeblood of scientific and technological innovation. Various models of funding allocation became institutionalized in the 20th century, shaping the present landscape of research funding. There are numerous reasons for scientists to be dissatisfied with current funding schemes, including the imbalance between funding for curiosity-driven and mission-directed research, regional and country disparities, path-dependency of who gets funded, gender and race disparities, low inter-reviewer reliability, and the trade-off between the effort and time spent on writing or reviewing proposals and doing research.
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College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, Hangzhou 310027, China.
Traditional drug-delivery methods are limited by low bioavailability and nonspecific drug distribution, resulting in poor therapeutic efficacy and potential risks of toxicity. Mesoporous silica nanoparticles (MSNs) have attracted wide attention as drug-delivery carriers due to their large specific surface area, adjustable pore size, good mechanical strength, good biocompatibility, and rich hydroxyl groups on their surface. In this paper, MSNs were synthesized by a template method, and the morphology and pore structure were regulated.
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