Enhanced Gas Adsorption on Graphitic Substrates via Defects and Local Curvature: A Density Functional Theory Study

Using van-der-Waals-corrected density functional theory calculations, we explore the possibility of engineering the local structure and morphology of high-surface-area graphene-derived materials to improve the uptake of methane and carbon dioxide for gas storage and sensing. We test the sensitivity of the gas adsorption energy to the introduction of native point defects, curvature, and the application of strain. The binding energy at topological point defect sites is inversely correlated with the number of missing carbon atoms, causing Stone-Wales defects to show the largest enhancement with respect to pristine graphene (similar to 20%). Improvements of similar magnitude are observed at concavely curved surfaces in buckled graphene sheets under compressive strain, whereas tensile strain tends to weaken gas binding. Trends for CO2 and CH4 are, similar, although CO2 binding is generally stronger by similar to 4 to 5 kJ mol(-1). However, the differential between the adsorption of CO2 and CH4 is much higher on folded graphene sheets and at concave curvatures; this could possibly be leveraged for CH4/CO2 flow separation and gasselective sensors.

Role of Vertex Index in Substructure Identification and Activity Prediction: A Study on Antitubercular Activity of a Series of Acid Alkyl Ester Derivatives

Tuberculosis (TB) is a life threatening disease caused due to infection from Mycobacterium tuberculosis (Mtb). That most of the TB strains have become resistant to various existing drugs, development of effective novel drug candidates to combat this disease is a need of the day. In spite of intensive research world-wide, the success rate of discovering a new anti-TB drug is very poor. Therefore, novel drug discovery methods have to be tried. We have used a rule based computational method that utilizes a vertex index, named `distance exponent index (D-x)' (taken x = -4 here) for predicting anti-TB activity of a series of acid alkyl ester derivatives. The method is meant to identify activity related substructures from a series a compounds and predict activity of a compound on that basis. The high degree of successful prediction in the present study suggests that the said method may be useful in discovering effective anti-TB compound. It is also apparent that substructural approaches may be leveraged for wide purposes in computer-aided drug design.