Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to...Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.展开更多
Circulating microRNAs(miRNAs)play a pivotal role in the occurrence and development of acute myocardial infarction(AMI),and precise detection of them holds significant clinical implications.The development of luminol-b...Circulating microRNAs(miRNAs)play a pivotal role in the occurrence and development of acute myocardial infarction(AMI),and precise detection of them holds significant clinical implications.The development of luminol-based luminophores in the field of electrochemiluminescence(ECL)for miRNA detection has been significant,while their effectiveness is hindered by the instability of co-reactant hydrogen peroxide(H_(2)O_(2)).In this work,an iron single-atom catalyst(Fe-PNC)was employed for catalyzing the luminol-O_(2) ECL system to achieve ultra-sensitive detection of myocardial miRNA.Target miRNA triggers a hybridization chain reaction(HCR),resulting in the generation of a DNA product featuring multiple sticky ends that facilitate the attachment of Fe-PNC probes to the electrode surface.The Fe-PNC catalyst exhibits high promise and efficiency for the oxygen reduction reaction(ORR)in electrochemical energy conversion systems.The resulting ECL biosensor allowed ultrasensitive detection of myocardial miRNA with a low detection limit of 0.42 fM and a wide linear range from 1 fM to 1.0 nM.Additionally,it demonstrates exceptional performance when evaluated using serum samples collected from patients with AMI.This work expands the application of single-atom catalysis in ECL sensing and introduces novel perspectives for utilizing ECL in disease diagnosis.展开更多
Under the public spotlight,uranyl(UO22+)ions has attracted considerable attention for the extreme radioactive and chemical toxicity to ourselves and our environment.Herein,we present a simple and effective ratiometric...Under the public spotlight,uranyl(UO22+)ions has attracted considerable attention for the extreme radioactive and chemical toxicity to ourselves and our environment.Herein,we present a simple and effective ratiometric fluorescence imaging method for the visualizing and quantitative detection UO22+ions by cellphone-based optical platform.The sensing solution was prepared by mixing label-free red carbon dots(r-CDs)and blue carbon dots(b-CDs)together with a fixed photoluminescence intensity ratio of 4:1.When UO22+ions were added,the fluorescence of r-CDs can be selectively quenched,while the fluorescence of b-CDs remains stable without spectral cha nges.With the gradually increase the amounts of UO22+ions,the different response of dual-color CDs resulted in a signification color evolution from deep red to dark purple under the ultraviolet(UV)light illumination.Then,a cellphone-based optical platform was constructed for directly imaging the color change of the samples,and the built-in Colorpicker APP quickly output the red,green and blue(RGB)channel values of these images within one second.Interesting,there was a linear relationship between the ratio of red and blue(R/B)channel values and UO22+ions concentration from 0μmol/L to 30.0μmol/L(R^2=0.92804)with the detection limit of^8.15μmol/L(signal-to-noise ratio of 3).In addition,the optical platform has also been applied to the quantification of UO22+ions in tap water and river water sample.With the advantage of low-cost,portable,easy to operation,we anticipate that this method would greatly improve the accessibility of UO22+ions detection even in resource-limited areas.展开更多
基金the State Grid Liaoning Electric Power Supply Co.,Ltd.(Research on Scheduling Decision Technology Based on Interactive Reinforcement Learning for Adapting High Proportion of New Energy,No.2023YF-49).
文摘Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.
基金supported by the National Natural Science Foundation of China(No.22004003)the Natural Science Foundation of Anhui Province for Distinguished Young Scholars(No.2008085J11)+2 种基金the Open Project Program of Key Laboratory of Optic-electric Sensing and Analytical Chemistry for Life Science(No.M2024-5)MOE,the Open Project of Engineering Research Center of Biofilm Water Purification and Utilization Technology of Ministry of Education(No.BWPU2023KF06)the Natural Science Research Project of Anhui Province Education Department(No.2023AH051116).
文摘Circulating microRNAs(miRNAs)play a pivotal role in the occurrence and development of acute myocardial infarction(AMI),and precise detection of them holds significant clinical implications.The development of luminol-based luminophores in the field of electrochemiluminescence(ECL)for miRNA detection has been significant,while their effectiveness is hindered by the instability of co-reactant hydrogen peroxide(H_(2)O_(2)).In this work,an iron single-atom catalyst(Fe-PNC)was employed for catalyzing the luminol-O_(2) ECL system to achieve ultra-sensitive detection of myocardial miRNA.Target miRNA triggers a hybridization chain reaction(HCR),resulting in the generation of a DNA product featuring multiple sticky ends that facilitate the attachment of Fe-PNC probes to the electrode surface.The Fe-PNC catalyst exhibits high promise and efficiency for the oxygen reduction reaction(ORR)in electrochemical energy conversion systems.The resulting ECL biosensor allowed ultrasensitive detection of myocardial miRNA with a low detection limit of 0.42 fM and a wide linear range from 1 fM to 1.0 nM.Additionally,it demonstrates exceptional performance when evaluated using serum samples collected from patients with AMI.This work expands the application of single-atom catalysis in ECL sensing and introduces novel perspectives for utilizing ECL in disease diagnosis.
基金the National Natural Science Foundation of China(Nos.21976002,21675158,21507134,61603001,61705239 and 81773684)Natural Science Foundation of Anhui Province(Nos.1908085MB41,1908085QB75)+2 种基金Guangdong Natural Science Funds for Distinguished Young Scholars(No.2018B030306033)Pearl River S&T Nova Program of Guangzhou(No.201806010060)Pearl River Talent Program(No.2017GC010363)。
文摘Under the public spotlight,uranyl(UO22+)ions has attracted considerable attention for the extreme radioactive and chemical toxicity to ourselves and our environment.Herein,we present a simple and effective ratiometric fluorescence imaging method for the visualizing and quantitative detection UO22+ions by cellphone-based optical platform.The sensing solution was prepared by mixing label-free red carbon dots(r-CDs)and blue carbon dots(b-CDs)together with a fixed photoluminescence intensity ratio of 4:1.When UO22+ions were added,the fluorescence of r-CDs can be selectively quenched,while the fluorescence of b-CDs remains stable without spectral cha nges.With the gradually increase the amounts of UO22+ions,the different response of dual-color CDs resulted in a signification color evolution from deep red to dark purple under the ultraviolet(UV)light illumination.Then,a cellphone-based optical platform was constructed for directly imaging the color change of the samples,and the built-in Colorpicker APP quickly output the red,green and blue(RGB)channel values of these images within one second.Interesting,there was a linear relationship between the ratio of red and blue(R/B)channel values and UO22+ions concentration from 0μmol/L to 30.0μmol/L(R^2=0.92804)with the detection limit of^8.15μmol/L(signal-to-noise ratio of 3).In addition,the optical platform has also been applied to the quantification of UO22+ions in tap water and river water sample.With the advantage of low-cost,portable,easy to operation,we anticipate that this method would greatly improve the accessibility of UO22+ions detection even in resource-limited areas.
