Esa Stenberg

This report describes a system for real-time biospecific interaction analysis, using biosensor technology based on the optical phenomenon surface plasmon resonance. The biospecific interface is a sensor chip consisting of a thin gold film deposited on a glass support and covered with a hydrogel matrix. One component of the interaction being studied is attached covalently to the hydrogel, and other interactants are passed over the chip in solution. The interaction is followed in real time in terms of changes in the mass concentration of biomolecules at the sensor surface. Surface concentrations down to 10 pg/mm2 can be measured. The technique does not require molecular labels such as isotopes or spectroscopic markers, and purification of interacting components can often be avoided. Repeated analyses can be performed on the same sensor chip. With this system, the same general procedure can be used for a wide range of different applications, including concentration determination, kinetic measurements and multi-site binding studies. The sensitivity of the technique can be adjusted by choice of reagents and experimental procedure: determination of specific proteins in serum down to 20 ng/ml and macromolecular association constants from 10(7) M-1 up to 4 x 10(11) M-1 are documentated examples. No other single analytical system has the same versatility and general applicability to biospecific interaction analysis. The system is developed and marketed by Pharmacia Biosensor AB, Sweden.

In this bibliometric study, we analyze two of the six battery research subfields identified in the BATTERY 2030+ roadmap: Materials Acceleration Platform and Smart functionalities: Sensing. In addition, we analyze the entire research field related to BATTERY 2030+ as a whole. We (a) evaluate the European standing in the two subfields/the BATTERY 2030+ field in comparison to the rest of the world, and (b) identify strongholds of the two subfields/the BATTERY 2030+ field across Europe. For each subfield and the field as a whole, we used seed articles, i. e. articles listed in the BATTERY 2030+ roadmap or cited by such articles, in order to generate additional, similar articles located in an algorithmically obtained classification system. The output of the analysis is publication volumes, field normalized citation impact values with comparisons between country/country aggregates and between organizations, co-publishing networks between countries and organizations, and keyword co-occurrence networks.