
ny
Solid State NMR
fra 1 369,-
Tilgjengelig i 1 butikker
Frakt og levering
Produktinformasjon
<p>Nuclear Magnetic Resonance (NMR) has proved to be a uniquely powerful and versatile tool for analyzing and characterizing chemicals and materials of all kinds. This book focuses on the latest developments and applications for "solid-state" NMR, which has found new uses from archaeology to crystallography to biomaterials and pharmaceutical science research. The book will provide materials engineers, analytical chemists, and physicists, in and out of lab, a survey of the techniques and the essential tools of solid-state NMR, together with a practical guide on applications. In this concise introduction to the growing field of solid-state nuclear magnetic resonance spectroscopy the reader will find: <p><ul><li>Basic NMR concepts for solids, including guidance on the spin-1/2 nuclei concept <br><li>Coverage of the quantum mechanics aspects of solid state NMR and an introduction to the concept of quadrupolar nuclei <br><li>An understanding relaxation, exchange and quantitation in NMR <br><li>An analysis and interpretation of NMR data, with examples from crystallography studies <br><li>Appendices covering spin properties of spin-1/2 nuclides as well as NMR simulation procedures</ul>
Topplisten: Other Brand Matematikk og naturfag
Spesifikasjon
Produkt
| Produktnavn | Solid State NMR |
| Merke | Other Brand |
Pris og prishistorikk
Akkurat nå er 1 369,- den billigste prisen for Solid State NMR blant 1 butikker hos Prisradar. Sjekk også vår topp 5-rangering av beste matematikk og naturfag for å være sikker på at du gjør det beste kjøpet.
Mushroom Forager's ChoiceEinfuhrung in Die BiologieEntlegene Spuren GoethesDie Grundwasser Mit Besonderer Berucksichtigung Der Grundwasser Schwedens
Datenanalyse und Modellierung mit STATISTICADie Stromung in Rohren Und Die Berechnung Weitverzweigter Leitungen Und Kanale Mit Rucksicht Auf BeUnd Entluftungsanlagen Grubenbewetterung GastraHeat Resistant Microorganisms in Food Safety and SpoilageMinimizing Data Movement and Parameter Count Across the Machine Learning Stack









