Novel antibacterial strategies such as antibacterial photodynamic therapy(aPDT)and photothermal therapy(PTT)have gained significant attention,however,relying on a single-treatment approach still faces challenges of in...Novel antibacterial strategies such as antibacterial photodynamic therapy(aPDT)and photothermal therapy(PTT)have gained significant attention,however,relying on a single-treatment approach still faces challenges of insufficient therapeutic efficiency and the potential for drug resistance.In this study,a multimodal synergistic antibacterial nanoplatform by coupling a carbon monoxide(CO)donor(4-(3-hydroxy-4-oxo-4H-chromen-2-yl)benzoic acid(4-BA))with carbon dots(CDs)is developed,referred to as CDs-CO,which integrates multiple antibacterial modes of aPDT,PTT,and gas therapy.This nanoplatform is designed for highly efficient antibacterial action with a low risk of inducing drug resistance.CDs are engineered to possess tailored functions,including deep-red light-triggered heat and singlet oxygen(^(1)O_(2))production.After modification with 4-BA and exposure to 660 nm laser irradiation,CDs-CO exhibits favorable photothermal conversion efficiency(η=52.7%),robust ^(1)O_(2) generation,and ^(1)O_(2)-activated CO release.Antibacterial experiments demonstrated the excellent sterilization effects of CDs-CO against both Escherichia coli(E.coli)and Staphylococcus aureus(S.aureus),underscoring the enhanced antibacterial efficiency of this multimodal nanoplatform.This study offers a rational approach for designing multimodal synergistic antibacterial platforms,highlighting their potential for effectively treating bacterial infections.展开更多
Quantum dot(QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance.However,the stochastic nature of QD structures results in th...Quantum dot(QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance.However,the stochastic nature of QD structures results in the poor reliability of resistive switching in neuromorphic computing,limiting its practical applications.Here,we present a data-driven QD synthesis optimization loop to precisely engineer QD structures for reliable neuromorphic computing.By deeply integrating high-throughput density functional theory with machine learning,we establish a cross-scale screening platform for precise synthesis of QDs,enabling multi-dimension predictions from atomic-level structures to macroscopic electrical synaptic behaviors.Through the minimization of structural disorder,achieved by pure phase,uniform size distribution,and highly preferred orientation,QD-based memristors demonstrate a 57%reduction in switching voltage,a two-order-of-magnitude increase in the ON/OFF ratio,and endurance and retention degradation as low as 0.1%over 8.4×10^(7)s of continuous operation and 10^(5)rapid read cycles.Furthermore,the dynamic learning range and neuromorphic computing accuracy are improved by 477%and 27.8%(reaching 92.23%),respectively.These findings establish a scalable,data-driven strategy for rational design of QD-based memristors,advancing the development of next-generation reliable neuromorphic computing systems.展开更多
基金supported by the National Natural Science Foundation of China(No.52173126)China Postdoctoral Science Foundation(No.2024M751152).
文摘Novel antibacterial strategies such as antibacterial photodynamic therapy(aPDT)and photothermal therapy(PTT)have gained significant attention,however,relying on a single-treatment approach still faces challenges of insufficient therapeutic efficiency and the potential for drug resistance.In this study,a multimodal synergistic antibacterial nanoplatform by coupling a carbon monoxide(CO)donor(4-(3-hydroxy-4-oxo-4H-chromen-2-yl)benzoic acid(4-BA))with carbon dots(CDs)is developed,referred to as CDs-CO,which integrates multiple antibacterial modes of aPDT,PTT,and gas therapy.This nanoplatform is designed for highly efficient antibacterial action with a low risk of inducing drug resistance.CDs are engineered to possess tailored functions,including deep-red light-triggered heat and singlet oxygen(^(1)O_(2))production.After modification with 4-BA and exposure to 660 nm laser irradiation,CDs-CO exhibits favorable photothermal conversion efficiency(η=52.7%),robust ^(1)O_(2) generation,and ^(1)O_(2)-activated CO release.Antibacterial experiments demonstrated the excellent sterilization effects of CDs-CO against both Escherichia coli(E.coli)and Staphylococcus aureus(S.aureus),underscoring the enhanced antibacterial efficiency of this multimodal nanoplatform.This study offers a rational approach for designing multimodal synergistic antibacterial platforms,highlighting their potential for effectively treating bacterial infections.
基金supported by the National Natural Science Foundation of China(51572205,52372159)the Natural Science Foundation Innovation Research Team of Hainan Province(524CXTD431)+1 种基金the National Science Fund for Distinguished Young Scholars of Hubei Province(201CFA067)the National Innovation and Entrepreneurship Training Program for College Students(S202510497020,202510497003,and S202510497010)。
文摘Quantum dot(QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance.However,the stochastic nature of QD structures results in the poor reliability of resistive switching in neuromorphic computing,limiting its practical applications.Here,we present a data-driven QD synthesis optimization loop to precisely engineer QD structures for reliable neuromorphic computing.By deeply integrating high-throughput density functional theory with machine learning,we establish a cross-scale screening platform for precise synthesis of QDs,enabling multi-dimension predictions from atomic-level structures to macroscopic electrical synaptic behaviors.Through the minimization of structural disorder,achieved by pure phase,uniform size distribution,and highly preferred orientation,QD-based memristors demonstrate a 57%reduction in switching voltage,a two-order-of-magnitude increase in the ON/OFF ratio,and endurance and retention degradation as low as 0.1%over 8.4×10^(7)s of continuous operation and 10^(5)rapid read cycles.Furthermore,the dynamic learning range and neuromorphic computing accuracy are improved by 477%and 27.8%(reaching 92.23%),respectively.These findings establish a scalable,data-driven strategy for rational design of QD-based memristors,advancing the development of next-generation reliable neuromorphic computing systems.
