Emergence, Breaking Symmetry and Neurophenomenology as Pillars of Chemical Tenets

Conference paper
First Online:
Part of the NATO Science for Peace and Security Series B: Physics and Biophysics book series (NAPSB)

Abstract

Since Heraclitus and Parmenides human thought was based on the research of the first principles governing the world. This necessity requires the adoption of an invariance concept which in turn is described by laws and theories defined by symmetry properties. Chemistry does not follow this paradigm, because of its intrinsic interest in inducing a break in the order towards the emergence of a new order through a symmetry breaking process. Indeed chemistry is basically the study of matter and its transformations. The manipulation of the matter always requires the adoption of a realistic approach, which is strongly contrasting with the definition of an absolute truth. The minds of chemists are continuously addressing the verification of the potentialities of Nature and these potentialities are always referred to a reference context defined by other chemical compounds. When these properties are considered from another point of view, they lose a part of their significance. Therefore chemists adopt a divergent pragmatism, which is rather clear from the neurophenomenological approach they use in the interaction with quantum world. In fact the approach discards the study of the essence of the real things, since only the knowledge of the relationships between the things is necessary. In this sense the answer the chemists obtain from their investigation of the microscopic properties of matter must be always considered as the resultant of the interactions of the microscopic object with its environment. A few examples concerning the decay behavior of magnetic systems in metastable states are discussed.

Keywords

Quantum Mechanic Absolute Truth Quantum Object Downward Causality Schroedinger Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

I am strongly indebted with Professor Dante Gatteschi for discussions and the work he made in revising this manuscript. A significant acknowledgment is also due to Professor Roberta Sessoli for the elegant competence she used in simplifying the ideas contained in the first draft of the manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.LAMM Laboratory, Dipartimento di ChimicaUniversità di Firenze, UdR INSTMSesto Fiorentino (Firenze)Italy

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