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Innovation networks and knowledge clusterisation depends to a great extent on the capacity of companies to acquire and assimilate external knowledge. The capacity of business to absorb scientific knowledge is associated with the disciplinary profile of its human resources. To explore the relationship between disciplinary absorption capacity, networks and innovation products, we suggest the concept of critical mass in science and technology-oriented human resources and compare two types of companies distinguished by differences in the profile of their critical masses. The results show that a relation does exist between the discipline-related makeup of the critical mass, the types of products obtained and the types of partners in innovation networks.

The selection of the lithium-ion battery chemistry is a crucial step when designing a certain application that includes an energy storage device, as it could limit the lifetime of the system. This paper presents two empirical cycling degradation models designed for NMC and LFP lithium-ion battery chemistries. The novel contribution of the models consists on representing the effect of the degradation stress factors as function of battery chemistries, rather than single cell references as typically approached in the literature. This know-how is claimed to be potentially valuable in order to compare characteristics of different lithium-ion battery chemistries and therefore help in the selection of the definite cell reference to be integrated in a certain application. A methodology is proposed for the development of such empirical degradation models, which are built upon large sets of degradation data collected from the literature and include information of diverse cell references and stress factors. A review of the state-of-art reveals that the data of NMC and LFP cathode chemistries is enough rich to develop degradation models that consider the main cycling stress factors accelerating battery ageing – number of full equivalent cycles, operation temperature, depth-of-discharge, charge and discharge current rate, and state-of-charge. Eventually, the cycling degradation models are constructed based on data from 232 degradation tests for NMC and 85 degradation tests for LFP. The obtained models are validated and mean absolute errors of 2.49% (NMC model) and 2.18% (LFP model) are achieved. An analysis of the models is conducted focusing on the effect that each stress factor has on the overall degradation of both lithium-ion battery chemistries. Finally, some case studies based on typical applications are proposed in order to calculate the lifetimes of each battery chemistry and evaluate the most appropriate option for each application.