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doi:10.22028/D291-32434
Titel: | Novel approaches for quantitative analysis of small biomolecules in MALDI-MS and SALDI-MS |
VerfasserIn: | Zhen, Liu |
Sprache: | Englisch |
Erscheinungsjahr: | 2020 |
DDC-Sachgruppe: | 540 Chemie |
Dokumenttyp: | Dissertation |
Abstract: | The aim of this work is to develop novel approaches to improve signal reproducibility and sensitivity in matrix-assisted laser desorption/ionization (MALDI) and surface-assisted laser desorption/ionization (SALDI) mass spectrometry (MS), for quantitative analysis of small biomolecules including endogenous metabolites and small lipids. Firstly, regular channels were designed in the target plate to inhibit the inhomogeneous deposition of the samples during solvent evaporation, to improve the signal reproducibility in MALDI-MS. Secondly, a series of ultra-thin and homogeneous AuNP substrates ([AuNP]n) were prepared at the air/water interface by using a Langmuir-Blodgett inspired approach. The optimized [AuNP]n substrates exhibited not only high SALDI-MS signal intensity but also excellent signal reproducibility, both of which benefits the quantitative analyses in SALDI-MS. Thirdly, influences of gold core size and surface ligands on the MS signal were systematically studied to further improve the function of AuNP substrates in SALDI-MS. The results indicated that the AuNPs with bigger core size and hydrophobic surface ligands showed higher signal intensity. Moreover, removing the organic ligand of the as-deposited AuNP substrates could further increase the signal intensity. |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-324341 hdl:20.500.11880/29869 http://dx.doi.org/10.22028/D291-32434 |
Erstgutachter: | Volmer, Dietrich A. |
Tag der mündlichen Prüfung: | 24-Sep-2020 |
Datum des Eintrags: | 16-Okt-2020 |
Drittmittel / Förderung: | Chinese Council scholarship |
Fördernummer: | 201608080213 |
Fakultät: | NT - Naturwissenschaftlich- Technische Fakultät |
Fachrichtung: | NT - Chemie |
Professur: | NT - Keiner Professur zugeordnet |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
PhD thesis-Zhen Liu - 05.10.2020.pdf | PhD dissertation | 29,36 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons