600 Technik, Technologie
Refine
Year of publication
- 2019 (3) (remove)
Language
- English (3)
Is part of the Bibliography
- yes (3)
Keywords
- biomaterial (1)
- biomedical (1)
- biomimetic (chemical reaction) (1)
- chemical synthesis (1)
Institute
- Institut für Chemie (3) (remove)
Defined chemical reactions in a physiological environment are a prerequisite for the in situ synthesis of implant materials potentially serving as matrix for drug delivery systems, tissue fillers or surgical glues. ‘Click’ reactions like thiol Michael-type reactions have been successfully employed as bioorthogonal reaction. However, due to the individual stereo-electronic and physical properties of specific substrates, an exact understanding their chemical reactivity is required if they are to be used for in-situ biomaterial synthesis. The chiral (S)-2-mercapto-carboxylic acid analogues of L-phenylalanine (SH-Phe) and L-leucine (SH-Leu) which are subunits of certain collagenase sensitive synthetic peptides, were explored for their potential for in-situ biomaterial formation via the thiol Michael-type reaction.
In model reactions were investigated the kinetics, the specificity and influence of stereochemistry of this reaction. We could show that only reactions involving SH-Leu yielded the expected thiol-Michael product. The inability of SH-Phe to react was attributed to the steric hindrance of the bulky phenyl group. In aqueous media, successful reaction using SH-Leu is thought to proceed via the sodium salt formed in-situ by the addition of NaOH solution, which was intented to aid the solubility of the mercapto-acid in water. Fast reaction rates and complete acrylate/maleimide conversion were only realized at pH 7.2 or higher suggesting the possible use of SH-Leu under physiological conditions for thiol Michael-type reactions. This method of in-situ formed alkali salts could be used as a fast approach to screen mercapto-acids for thio Michael-type reactions without the synthesis of their corresponding esters.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.