Abstract: A leaf inspired biomimetic light trapping scheme for ultrathin flexible graphene silicon Schottky junction solar cell. An all-dielectric approach comprising of lossless silica and titania nanoparticles is used for mimicking the two essential light trapping mechanisms of a leaf: (1) focusing and waveguiding and (2) scattering. The light trapping scheme uses two optically tuned layers and does not require any nano-structuring of the active silicon substrate, thereby ensuring that the optical gain is not offset due to recombination losses.

Abstract: A phototransistor device to act as an artificial photonic synapse includes a substrate and a graphene source-drain channel patterned on the substrate. A perovskite quantum dot layer is formed on the graphene source-drain channel. The perovskite quantum dot layer is methylammonium lead bromide material. A method of operating the phototransistor device as an artificial photonic synapse includes applying a first fixed voltage to a gate of the phototransistor and a second fixed voltage across the graphene source-drain channel. A presynaptic signal is applied as stimuli across the graphene source-drain channel. The presynaptic signal includes one or more pulses of light or electrical voltage. A current across the graphene source-drain channel is measured to represent a postsynaptic signal.

Abstract: A leaf inspired biomimetic light trapping scheme for ultrathin flexible graphene silicon Schottky junction solar cell. An all-dielectric approach comprising of lossless silica and titania nanoparticles is used for mimicking the two essential light trapping mechanisms of a leaf: (1) focusing and waveguiding and (2) scattering. The light trapping scheme uses two optically tuned layers and does not require any nano-structuring of the active silicon substrate, thereby ensuring that the optical gain is not offset due to recombination losses.

Abstract: The attached disclosure provides a systems approach based on mathematical modelling of the kinetics of essential biochemical pathways involved in organ homeostasis. When this in silico model is coupled with in-vitro and/or in-vivo measurements to quantify drug-induced perturbations, a powerful platform that allows accurate and mechanistic-level prediction of drug-induced organ injury can be generated. The method described in this disclosure demonstrates that several physiological situations can also be accurately modelled in addition to the effect of perturbations induced by drugs. It can also be used along with high-throughput “omics” data to generate testable hypotheses leading to informed decision-making in drug development.

Abstract: The present disclosure relates to a methodology that identifies the underlying mechanisms that lead to hepatotoxicity. This is done by using the alterations in cellular metabolite profiles obtained before and after therapy/drug treatment in combination with a model of liver metabolism. Subsequently, by using a covariance matrix adaptation followed by evolutionary selection, it compares the drug-induced metabolite profiles obtained experimentally with those generated using the in silico model, automatically shortlists potential parameters whose alterations could have produced the drug-treated metabolite profile. Values of these parameters are estimated by formulating an optimal control problem to minimize the differences between the model-generated metabolite values and experimentally observed data. The estimated parameters are given as an input to the homeostatic liver model and simulations are carried out, thereby the results providing a mechanistic explanation for the development of toxicity.

Abstract: The attached disclosure provides a systems approach based on mathematical modelling of the kinetics of essential biochemical pathways involved in organ homeostasis. When this in silico model is coupled with in-vitro and/or in-vivo measurements to quantify drug-induced perturbations, a powerful platform that allows accurate and mechanistic-level prediction of drug-induced organ injury can be generated. The method described in this disclosure demonstrates that several physiological situations can also be accurately modelled in addition to the effect of perturbations induced by drugs. It can also be used along with high-throughput “omics” data to generate testable hypotheses leading to informed decision-making in drug development.