Computational Approaches to Zeolite-Based Adsorption Processes

Computational methods to calculate the properties of zeolites in gas adsorption and separation have proven to be a valuable complement to experimental work. Molecular simulation provides a molecular understanding of the mechanisms involved in the adsorption, desorption, and transport. The accuracy and reliability of the predictions depend on the models used for adsorbates and adsorbents, the force fields that describe the interaction, and the computational methods to calculate the properties. The selection of force fields and methods depends on the properties of the systems and on characteristics such as the flexibility of the framework, the hydrophobicity/hydrophilicity of the zeolite, the chirality, the silicon atom substitutions, the nature and concentration of extra framework cations, the composition of the guest gases, the measured property, etc. In this chapter, a brief description of the state of the art of molecular simulation applied to porous materials is provided, as well as a discussion of current challenges in the field.</p

Identification of common variants associated with human hippocampal and intracranial volumes

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease1, 2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6, 7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7)