Predicting substituent effects on activation energy changes by static catalytic fields
نویسندگان
چکیده
منابع مشابه
Predicting substituent effects on activation energy changes by static catalytic fields
Catalytic fields illustrate topology of the optimal charge distribution of a molecular environment reducing the activation energy for any process involving barrier crossing, like chemical reaction, bond rotation etc. Until now, this technique has been successfully applied to predict catalytic effects resulting from intermolecular interactions with individual water molecules constituting the fir...
متن کاملEffects of static and electromagnetic fields on human serum paraoxonase-1 activity in vitro
Introduction: In recent years the relationship between electromagnetic fields and coronary artery disease is attracted a considerable attention. Low density lipoprotein (LDL) oxidation is the initial step in the development of atherosclerosis. Paraoxonase1 (PON1) protects LDL and High density lipoprotein (HDL) against oxidative processes, thus preventing the formation of atherogenic (oxidized-L...
متن کاملEffects of weak static magnetic fields on endothelial cells.
Pulsed electromagnetic fields (PEMFs) have been used extensively in bone fracture repairs and wound healing. It is accepted that the induced electric field is the dose metric. The mechanisms of interaction between weak magnetic fields and biological systems present more ambiguity than that of PEMFs since weak electric currents induced by PEMFs are believed to mediate the healing process, which ...
متن کاملBehavioral effects of high-strength static magnetic fields on rats.
Advances in magnetic resonance imaging are driving the development of more powerful and higher-resolution machines with high-strength static magnetic fields. The behavioral effects of high-strength magnetic fields are largely uncharacterized, although restraint within a 9.4 T magnetic field is sufficient to induce a conditioned taste aversion (CTA) and induce brainstem expression of c-Fos in ra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Molecular Modeling
سال: 2017
ISSN: 1610-2940,0948-5023
DOI: 10.1007/s00894-017-3559-6