LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Practical Application of Supercritical Fluid Chromatography for Pharmaceutical Research and Development provides a valuable “go-to” reference for many difficult-to-solve challenge… Read more
LIMITED OFFER
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Practical Application of Supercritical Fluid Chromatography for Pharmaceutical Research and Development provides a valuable “go-to” reference for many difficult-to-solve challenges using pertinent chromatographic theory, first-hand case studies, and examples provided from academic and industry experts. This text also enables professors teaching an analytical instrumental course to introduce and instruct students about one of the most sustainable and powerful separation methods currently available. While the text has broad applicability across industrial sectors, it focuses primarily on application in the pharmaceutical industry. The book is designed to allow readers to align current HPLC/UHPLC capabilities with SFC as an orthogonal tool for project specific methods in the pharmaceutical industry. It highlights where SFC falls on the spectrum of useful chromatographic tools for routine and challenging separative methods.
Experienced HPLC users who are interested in developing knowledge in orthogonal separation techniques, as well as newcomers to the field of separation science, will find this text particularly useful. Chapters address where SFC may fit the analytical needs of the pharmaceutical industry and alert the readers as to where the technique will not fit. Readers will gain an understanding of how and where SFC may be applied and adapted more routinely across the pharmaceutical industry as a ‘green’ way of undertaking separation opportunities and challenges. Areas within the pharmaceutical industry include early drug discovery, process chemistry, and late stage development and manufacturing.
MH
PF