The new approach to the development of thin-layer chromatograms is presented. For the first time we show flexible mobile phase dosage onto the surface of the adsorbent layer by moving pipette combined with precise syringe pumps. The pipette is driven into movement by computer controlled 3D machine (modified 3D printer mechanism). Delivery of the mobile phase to the adsorbent layer is equal to or lower than that of conventional development. Therefore chromatograms can be developed with optimal mobile phase velocity, adjusted to its absorption rate by the adsorbent layer. Under such conditions there is no excess of eluent on the surface of the adsorbent layer so higher performance of the chromatographic system can be obtained. Moreover chromatograms can be developed with constant linear mobile phase velocity and therefore the relationships the plate height vs. mobile phase linear velocity obtained with planar chromatography driven by capillary forces are investigated and reported. In addition the contribution of starting spot variance in total peak variance and the influence of narrowing of starting spots on performance of the chromatographic system have been studied. The results confirm a very significant starting spot variance contribution to total peak variance and consequently considerable influence of starting spot size on plate height of the separation system, when chromatogram is developed on a short distance. In the paper the advantages and disadvantages of the prototype device and its possible application are discussed.
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http://dx.doi.org/10.1016/j.chroma.2018.08.003 | DOI Listing |
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
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View Article and Find Full Text PDFPhys Rev Lett
December 2024
The Australian National University, Department of Quantum Science and Technology, Canberra, Australian Capital Territory 2601, Australia.
We demonstrate an atom interferometer measurement protocol compatible with operation on a dynamic platform. Our method employs two open interferometers, derived from the same atomic source, with different interrogation times to eliminate initial velocity dependence while retaining precision, accuracy, and long term stability. We validate the protocol by measuring gravitational tides, achieving a precision of 4.
View Article and Find Full Text PDFJ Sep Sci
January 2025
Chair of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Toruń, Poland.
Oligonucleotides (ONs) are an increasingly popular category of molecules in the pharmaceutical landscape, particularly attractive for the treatment of genetic and rare diseases. However, analyzing these molecules presents significant challenges, due to their highly hydrophilic nature, multiple negative charges, and the presence of closely related impurities resulting from the complex solid-phase synthesis process. Ion pairing reverse-phase liquid chromatography (IP-RPLC) is the preferred technique for ONs analysis but is not ideal for mass spectrometry (MS) coupling.
View Article and Find Full Text PDFSci Rep
January 2025
School of Environmental Health, Institute of Public Health, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
The objective of this study is to enhance the capacity of struvite-phosphate forming reactor utilized in the production of phosphorus fertilizer from wastewater collected from mobile toilets, characterized by phosphorus (P) concentrations of 5.0 ± 1.1 g/l.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Background And Objective: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions.
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