The Power List 2015
Frantisek (Frank) Svec
Lead Scientist; Facility director at the Molecular Foundry, Lawrence Berkeley National Laboratory, California, USA; Short-term Principal Researcher, Beijing University of Chemical Technology, International Research Center for Soft Matter, China.
Most important lesson Be patient. Good results do not occur overnight. Also, talk with people – both in one’s own group and outside; wide ranging teamwork is imperative.
Encounters with serendipity I do not believe too much in serendipity. The Romans had a proverb: Audacem fortuna iuvat (Fortune favors the bold). One has to be prepared to discover something new. For example, our monoliths did not fall from the blue skies. We worked with porous beads for almost two decades before the idea of monoliths occurred to us.
Eye on the horizon I am currently building my new group in the Beijing University of Chemical Technology, China. It is a new challenge for me. However, with the generous support from the university and sufficient funding, the group is shaping up and some interesting results are emerging.
Being involved in the chromatographic columns technologies, I expect that a significant progress can be expected from moving from today’s typical columns to columns that will be manufactured entirely by 3D printing. They will all be the same, computer designed to have optimal morphology and the highest possible efficiency. We are probably far away from this target since we need 3D printing with a submicrometer resolution and high speed. Also, the materials used for this technique still needs to be selected. Another direction might be further miniaturization of chromatographic systems although we may hit the physical barriers to do so.
Finally, I am sure that separation science will thrive in the future. The ever more strict regulations in testing pharmaceuticals, food, environment, and in many other areas of our everyday life, as well as the research concerned, for example, with life sciences will require more precise and faster methods using ever smaller samples.