Synergistic cerium doping and MXene coupling in layered double hydroxides as efficient electrocatalysts for oxygen evolution

Oxygen evolution reaction(OER) is a bottle-neck process in many sustainable energy conversion systems due to its sluggish kinetics.The development of cost-effective yet efficient electrocatalysts towards OER is highly desirable but still a great challenge at current stage.Herein,a new type of hybrid nanostructure,consisting of two-dimensional(2D) Cerium-doped NiFe-layered double hydroxide nanoflakes directly grown on the 2D Ti3C2Tx MXene surface(denoted as NiFeCe-LDH/MXene),is designed using a facile insitu coprecipitation method.The resultant NiFeCe-LDH/MXene hybrid presents a hierarchical nanoporous structure,high electrical conductivity and strong interfacial junction because of the synergistic effect of Ce doping and MXene coupling.As a result,the hybrid catalyst exhibits an excellent catalytic activity for OER,delivering a low onset overpotential of 197 mV and an overpotential of 260 mV at a current density of 10 mA·cm-2 in the alkaline medium,much lower than its pure LDH counterparts and IrO2 catalyst.Besides,the hybrid catalyst also displays a fast reaction kinetics and a remarkable stable durability.Further theoretic studies using density function theory(DFT) methods reveal that Ce doping could effectively narrow the bandgap of NiFe-LDH and reduce the overpotential in OER process.This work may shed light on the exploration of advanced electrocatalysts for renewable energy conversion and storage systems.

Highly conductive dodecaborate/MXene composites for high performance supercapacitors

With the increasingly prominent energy and environmental issues,the supercapacitors,as a highly efficient and clean energy conversion and storage devices,meet the requirements well.However,it is still a challenge to enhance the capacitance and energy density of supercapacitors.A novel and highly conductive dodecaborate/MXene composites have been designed for high performance supercapacitors.The surface charge property of MXene was modified by a simple ultrasonic treatment with ammonium ion,and the dodecaborate ion can be inserted into the inner surface of MXene by electrostatic adsorption.Due to the unique icosahedral cage conjugate structure formed by the B-B bond and the highly delocalized three-dimensionalπbond structure of the electrons,the negative charge is delocalied on the whole dodecaborate ion,which reduces the ability to bind to cations.Therefore,the cations can move easily,and the dodecaborate can act as a“lubricant”for ion diffusion between the MXene layers,which significantly improves the ion transfer rate of supercapacitors.The dodecaborate/MXene composites can achieve an extremely high specific capacitance of 366 F.g^-1 at a scan rate of 2 mV.s^-1,which is more than eight times higher than that of MXene(43 F1-)at the same scan rate.Our finding provides a novel route on the fabrication of the high performance supercapacitors.

DrSim: Similarity Learning for Transcriptional Phenotypic Drug Discovery

Transcriptional phenotypic drug discovery has achieved great success,and various compound perturbation-based data resources,such as connectivity map(CMap)and library of integrated network-based cellular signatures(LINCS),have been presented.Computational strategies fully mining these resources for phenotypic drug discovery have been proposed.Among them,the fundamental issue is to define the proper similarity between transcriptional profiles.Traditionally,such similarity has been defined in an unsupervised way.However,due to the high dimensionality and the existence of high noise in high-throughput data,similarity defined in the traditional way lacks robustness and has limited performance.To this end,we present Dr Sim,which is a learning-based framework that automatically infers similarity rather than defining it.We evaluated Dr Sim on publicly available in vitro and in vivo datasets in drug annotation and repositioning.The results indicated that Dr Sim outperforms the existing methods.In conclusion,by learning transcriptional similarity,Dr Sim facilitates the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery.The source code and manual of Dr Sim are available at https://github.com/bm2-lab/Dr Sim/.