Sumneryoung5059

Understanding mechanisms of materials deterioration during service life is fundamental for their confident use in the building sector. This work presents analysis of time series of data related to wood weathering acquired at three scales (molecular, microscopic, macroscopic) with different sensors. By using several complementary techniques, the material description is precise and complete; however, the data provided by multiple equipment are often not directly comparable due to different resolution, sensitivity and/or data format. This paper presents an alternative approach for multi-sensor data fusion and modelling of the deterioration processes by means of PARAFAC model. Time series data generated within this research were arranged in a data cube of dimensions samples × sensors × measuring time. The original protocol for data fusion as well as novel meta parameters, such as cumulative nested biplot, was proposed and tested. It was possible to successfully differentiate weathering trends of diverse materials on the basis of the NIR spectra and selected surface appearance indicators. A unique advantage for such visualization of the PARAFAC model output is the possibility of straightforward comparison of the degradation kinetics and deterioration trends simultaneously for all tested materials.The molybdenum blue method is the American Public Health Association (APHA) approved method for the detection and quantification of phosphate in water. The standard molybdenum blue method, APHA 4500 PE has a detection limit of 30 μgL-1 phosphate (10 μgL-1 phosphorus) in freshwater with a 5 cm cuvette. To further lower the detection limit to sub μgL-1 levels, we have developed a simple, fast, and solventless method for conversion of phosphate present in solution to a solid for quantification by Visible spectroscopy. The process converts the anionic heteropolymolybdate ions into a solid colloidal precipitate by charge neutralization with the cationic surfactant cetyltrimethylammonium bromide (CTAB), and the precipitate is then captured on a Visible transparent membrane. A Visible spectrum is then recorded in transmission mode through the membrane and the concentration of the phosphate is determined from the intensity of a band cantered at 700 nm. Using this method, the detection limit for phosphate in water is lowered to 0.64 μgL-1. The approach has also been extended to detect arsenate in water with a detection limit of 4.8 μgL-1 arsenate. . The method is also used to investigate real matrices with accuracy that matches the standard APHA method for detection of phosphate in water.Metabolites in the body fluid are becoming a rich source of disease biomarkers. Developing an effective and high throughput detection and analysis platform of metabolites is of great importance for potential biomarker discovery and validation. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has been successfully applied in rapid biomolecules detection in large scale. However, non-negligible background interference in low molecule-weight region still constitutes a main challenge even though various nanomaterials have been developed as an alternative to traditional organic matrix. In this work, a novel composite chip, silicon nanowires loaded with fluorinated ethylene propylene (FEP@SiNWs) was fabricated. It can serve as an excellent substrate for nanostructure-initiator mass spectrometry (NIMS) detection with ultra-low background noise in low molecular weight region ( less then 500 Da). Ion desorption efficiency and internal energy transfer of FEP@SiNWs were studied using benzylpyridinium salt and tetraphenylboron salt as thermometer chemicals. The results indicated that a non-thermal desorption mechanism might be involved in the LDI process on FEP@SiNWs. Owing to the higher LDI efficiency and low background interference of this novel substrate, the metabolic fingerprint of complex bio-fluids, such as human saliva, can be sensitively and stably acquired. As a proof of concept, FEP@SiNWs chip was successfully used in the detection of salivary metabolites. With the assistance of multivariate analysis, 22 metabolic candidates (p less then 0.05) which can discriminate type 2 diabetes mellitus (2-DM) and healthy volunteers were found and identified. The role of these feature metabolites in the metabolic pathway involved in 2-DM was confirmed by literature mining. This work demonstrates that FEP@SiNWs-based NIMS might be served as an efficient and high throughput platform for metabolic biomarker exploration and clinical diagnosis.Frequent on-line and automated monitoring of multiple protein biomarkers level secreted in the culture media during tissue growth is essential for the successful development of Tissue Engineering and Regenerative Medicine (TERM) products. Here, we present a low-cost, rapid, reliable, and integrable anion-exchange membrane-(AEM) based multiplexed sensing platform for this application. Unlike the gold-standard manual ELISA test, incubation/wash steps are optimized for each target and precisely metered in microfluidic chips to enhance selectivity. Unlike optical detection and unreliable visual detection for the ELISA test, which require standardization for every usage, the AEM ion current signal also offers robustness, endowed by the pH and ionic strength control capability of the ion-selective membrane, such that a universal standard curve can be used to calibrate all runs. The electrical signal is enhanced by highly charged silica nanoparticle reporters, which also act as hydrodynamic shear amplifiers to enhance selectivity during wash. This AEM-based sensing platform is tested with vascular protein biomarkers, Endothelin-1 (ET-1), Angiogenin (ANG) and Placental Growth Factor (PlGF). Gefitinib The limit of detection and three-decade dynamic range are comparable to ELISA assay but with a significantly reduced assay time of 1 h vs 7 h, due to the elimination of calibration and blocking steps. Optimized protocol for each target renders the detection highly reliable with more than 98% confidence. The multiplexed detection capability of the platform is also demonstrated by simultaneous detection of ET-1, ANG and PlGF in 40 μl of the vascular endothelial cell culture supernatants using three-membrane AEM sensor and the performance is validated against ELISA.