Developing an efficient artificial photosynthetic system for transforming carbon dioxide and storing solar energy in the form of chemical bonds is one of the greatest challenges in modern chemistry.However,the limited...Developing an efficient artificial photosynthetic system for transforming carbon dioxide and storing solar energy in the form of chemical bonds is one of the greatest challenges in modern chemistry.However,the limited choice of catalysts with wide light absorption range,long-term stability and excellent selectivity for CO_(2) reduction makes the process sluggish.Here,a core-shell-structured nonnoble-metal Ni@In co-catalyst loaded p-type silicon nanowire arrays(SiNWs)for efficient CO_(2) reduction to formate is demonstrated.The formation rate and Faradaic efficiency of formate over the Ni@In/SiNWs catalyst reach 58μmol h^(-1) cm^(-2) and 87% under the irradiation of one simulated sunlight(AM 1.5 G,100 mW cm^(-2)),respectively,which are about 24 and 12 times those over the pristine SiNWs.The enhanced photoelectrocatalytic performance for CO_(2) reduction is attributed to the rational combination of Ni capable of effectively extracting the photogenerated electrons and In responsible for the selective activation of CO_(2).展开更多
The discrete preparation of functional layers followed by lamination for all-organic active-matrix organic light-emitting diodes enables an ultrahigh aperture ratio and reliable conformability,promising significant po...The discrete preparation of functional layers followed by lamination for all-organic active-matrix organic light-emitting diodes enables an ultrahigh aperture ratio and reliable conformability,promising significant potential for nextgeneration skin-like displays.展开更多
With the burgeoning developments in artificial intelligence,hardware implementation of artificial neural network is also gaining pace.In this pursuit,ferroelectric devices(i.e.,tunneling junctions and transistors)with...With the burgeoning developments in artificial intelligence,hardware implementation of artificial neural network is also gaining pace.In this pursuit,ferroelectric devices(i.e.,tunneling junctions and transistors)with voltage thresholds were recently proposed as suitable candidates.However,their development is hindered by the inherent integration issues of inorganic ferroelectrics,as well as poor properties of conventional organic ferroelectrics.In contrast to the conventional ferroelectric synapses,here we demonstrated a two-terminal ferroelectric synaptic device using a molecular ferroelectric(MF)/semiconductor interface.The interfacial resistance can be tuned via the polarization-controlled blocking effect of the semiconductor,owing to the high ferroelectricity and field amplification effect of the MF.Typical synaptic features including spike timing-dependent plasticity are substantiated.The introduction of the semiconductor also enables the attributes of optoelectronic synapse and in-sensor computing with high image recognition accuracies.Such interfaces may pave the way for the hardware implementation of multifunctional neuromorphic devices.展开更多
Despite that in-sensor processing has been proposed to remove the latency and energy consumption during the inevitable data transfer between spatial-separated sensors,memories and processors in traditional computer vi...Despite that in-sensor processing has been proposed to remove the latency and energy consumption during the inevitable data transfer between spatial-separated sensors,memories and processors in traditional computer vision,its hardware implementation for artificial neural networks(ANNs)with all-in-one device arrays remains a challenge,especially for organic-based ANNs.With the advantages of biocompatibility,low cost,easy fabrication and flexibility,here we implement a self-powered in-sensor ANN using molecular ferroelectric(MF)-based photomemristor arrays.Tunable ferroelectric depolarization was intentionally introduced into the ANN,which enables reconfigurable conductance and photoresponse.Treating photoresponsivity as synaptic weight,the MFbased in-sensor ANN can operate analog convolutional computation,and successfully conduct perception and recognition of white-light letter images in experiments,with low processing energy consumption.Handwritten Chinese digits are also recognized and regressed by a large-scale array,demonstrating its scalability and potential for low-power processing and the applications in MF-based in-situ artificial retina.展开更多
基金supported by the National Natural Science Foundation of China(Nos.21972115,91945301,21690082 and 21503176)the China Postdoctoral Science Foundation(Nos.2015M570555,2016T90597)。
文摘Developing an efficient artificial photosynthetic system for transforming carbon dioxide and storing solar energy in the form of chemical bonds is one of the greatest challenges in modern chemistry.However,the limited choice of catalysts with wide light absorption range,long-term stability and excellent selectivity for CO_(2) reduction makes the process sluggish.Here,a core-shell-structured nonnoble-metal Ni@In co-catalyst loaded p-type silicon nanowire arrays(SiNWs)for efficient CO_(2) reduction to formate is demonstrated.The formation rate and Faradaic efficiency of formate over the Ni@In/SiNWs catalyst reach 58μmol h^(-1) cm^(-2) and 87% under the irradiation of one simulated sunlight(AM 1.5 G,100 mW cm^(-2)),respectively,which are about 24 and 12 times those over the pristine SiNWs.The enhanced photoelectrocatalytic performance for CO_(2) reduction is attributed to the rational combination of Ni capable of effectively extracting the photogenerated electrons and In responsible for the selective activation of CO_(2).
基金the National Science Foundation of China(NSFC)under grant No 62274118the Singapore National Research Foundation Investigatorship under Grant No NRF-NRFI08-2022-0009+1 种基金Singapore Ministry of Education under its AcRF Tier 2 Grant No MOE-T2EP50220-0001the SUSTech-NUS Joint Research Program.
文摘The discrete preparation of functional layers followed by lamination for all-organic active-matrix organic light-emitting diodes enables an ultrahigh aperture ratio and reliable conformability,promising significant potential for nextgeneration skin-like displays.
基金supported by the Natural Science Foundation of China (Nos.62074040,62074045,61804055)the Natural Science Foundation of Shanghai (Nos.20ZR1404000,19JC1416700).
文摘With the burgeoning developments in artificial intelligence,hardware implementation of artificial neural network is also gaining pace.In this pursuit,ferroelectric devices(i.e.,tunneling junctions and transistors)with voltage thresholds were recently proposed as suitable candidates.However,their development is hindered by the inherent integration issues of inorganic ferroelectrics,as well as poor properties of conventional organic ferroelectrics.In contrast to the conventional ferroelectric synapses,here we demonstrated a two-terminal ferroelectric synaptic device using a molecular ferroelectric(MF)/semiconductor interface.The interfacial resistance can be tuned via the polarization-controlled blocking effect of the semiconductor,owing to the high ferroelectricity and field amplification effect of the MF.Typical synaptic features including spike timing-dependent plasticity are substantiated.The introduction of the semiconductor also enables the attributes of optoelectronic synapse and in-sensor computing with high image recognition accuracies.Such interfaces may pave the way for the hardware implementation of multifunctional neuromorphic devices.
基金supported by the National Key Research and Development Program of China for International Cooperation(2020YFE0191300)the National Natural Science Foundation of China(Nos.62074040,61804055,T2222025 and 62174053)+1 种基金the Natural Science Foundation of Shanghai(No.20ZR1404000)Open Research Projects of Zhejiang Lab(2021MD0AB03).
文摘Despite that in-sensor processing has been proposed to remove the latency and energy consumption during the inevitable data transfer between spatial-separated sensors,memories and processors in traditional computer vision,its hardware implementation for artificial neural networks(ANNs)with all-in-one device arrays remains a challenge,especially for organic-based ANNs.With the advantages of biocompatibility,low cost,easy fabrication and flexibility,here we implement a self-powered in-sensor ANN using molecular ferroelectric(MF)-based photomemristor arrays.Tunable ferroelectric depolarization was intentionally introduced into the ANN,which enables reconfigurable conductance and photoresponse.Treating photoresponsivity as synaptic weight,the MFbased in-sensor ANN can operate analog convolutional computation,and successfully conduct perception and recognition of white-light letter images in experiments,with low processing energy consumption.Handwritten Chinese digits are also recognized and regressed by a large-scale array,demonstrating its scalability and potential for low-power processing and the applications in MF-based in-situ artificial retina.
