Computational screening of MN4 (M = Ti-Cu) based metal organic frameworks for CO2 reduction using the d-band centre as a descriptor.

Electrocatalytic reduction is considered to be a promising way for the green and sustainable conversion of CO2 into fuels and chemicals. Transition metals, copper particularly, are the most popular catalysts for this process and a wide range of reduced carbon compounds can be obtained. In previous studies, the binding energies of *CO and *OH were adopted as descriptors to screen out the best catalyst. However, this approach is not effective for those catalysts that have a weak interaction with CO molecules. Herein, we present a theoretical work by using the d-band centre as a descriptor to predict the best catalyst for CO2 reduction to CH4 based on newly synthesized metal organic frameworks, namely porous M3 (HITP)2 (HITP, 2,3,6,7,10,11-hexaiminotriphenylene) two-dimensional metal organic frameworks (MN4-MOFs). The limiting potentials of MN4-MOFs (M = Ti to Cu) for CO2 reduction, determined by the formation energy of *OCHOH and *OCH2OH species, are closely correlated with the d-band centre from the TiN4-MOF to CuN4-MOF. Among the eight catalysts examined, the FeN4-MOF turns out to be the most active one for the selective conversion of CO2 to CH4 with an ultralow limiting potential of only -0.41 V, which is comparable or even lower than that of other reported CO2 reduction catalysts.

Design and Development of a Novel Oral Care Simulator for the Training of Nurses.

OBJECTIVE: A Novel Oral Care Simulator was designed and developed to measure and visualise the facial and lingual forces exerted on teeth by the action of tooth brushing, considering the irregular geometry and structural composition of human dentition and the emulation of the realistic biomechanical deflection of the teeth. METHOD: FEA simulations were carried out on a central incisor under facial loading and an appropriate force sensing mechanism was designed. An anatomically accurate mandibular jaw and 16 teeth were 3D printed, on which 16 force sensing structures were embedded. The signals from the sensors were amplified using a multichannel signal amplifier built using instrumentation amplifiers which were then visualised through a GUI. RESULTS: The developed simulator is capable of indicating the magnitude of a force upto 15 N exerted on to the facial and lingual surfaces of teeth at a frequency of 60 Hz and above and it is capable of alerting the user if the force exceeds a pre-specified threshold. CONCLUSION: The designed force sensing mechanism considers the irregular geometry and structural composition of human dentition in measuring the facial and lingual forces. It provides a reliable feedback by indicating the force and emulating the realistic biomechanical deflection of teeth. SIGNIFICANCE: Nurses who care for the disabled, elderly and sick have explicitly stated the requirement for a simulator to train themselves on brushing the teeth of their subjects as their incorrect technique can cause longterm dental damage, for which a device has not been developed to date.