Control over charge transport in molecular–scale devices requires a deep understanding of how minute structural changes influence electronic properties.Here,we demonstrate dual transport regimes in tunnel junctions o...Control over charge transport in molecular–scale devices requires a deep understanding of how minute structural changes influence electronic properties.Here,we demonstrate dual transport regimes in tunnel junctions of n-alk-1-yne(CnA)molecules with gold electrodes driven by conformational bifurcation—the emergence of two nearly isoenergetic(planar and skewed)molecular conformers(dihedral anglesα=180°andα≈65°at the alkyne terminus in the gas phase).Although the energy differences are small,these subtle conformational differences manifest as distinct transport behaviors,uncovered through unsupervised machine learning,which identified two junction groups:“short”and“long”chains,with distinct attenuation factors(β_(short)≈1.0 vs.β_(long)≈0.74)and contact conductances(G_(c,short)≈200μS vs.G_(c,long)≈8μS).This dramatic impact of the dihedral angle exceeds the impact of the inter-ring twist angle in biphenyl-based junctions and rivals changes induced by switching from gold to platinum electrodes or from monothiol to dithiol anchors in oligoacene and oligophenylene junctions.X-ray photoelectron spectroscopy(XPS)confirmed this bifurcation,linking the“short”and“long”groups to planar and skewed conformers,with dihedrals remarkably agreeing with the gas-phase values.This work establishes conformational bifurcation as a promising route for designing programmable nanotransport properties through anchor-group control.展开更多
This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal s...This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.展开更多
We incorporate a non-Markovian feedback mechanism into the simulated bifurcation method for dynamical solvers addressing combinatorial optimization problems.By reinjecting a portion of dissipated kinetic energy into e...We incorporate a non-Markovian feedback mechanism into the simulated bifurcation method for dynamical solvers addressing combinatorial optimization problems.By reinjecting a portion of dissipated kinetic energy into each spin in a history-dependent and trajectory-informed manner,the method effectively suppresses early freezing induced by inelastic boundaries and enhances the system's ability to explore complex energy landscapes.Numerical results on the maximum cut(MAX-CUT)instances of fully connected Sherrington–Kirkpatrick(SK)spin glass models,including the 2000-spin K_(2000)benchmark,demonstrate that the non-Markovian algorithm significantly improves both solution quality and convergence speed.Tests on randomly generated SK instances with 100 to 1000 spins further indicate favorable scalability and substantial gains in computational efficiency.Moreover,the proposed scheme is well suited for massively parallel hardware implementations,such as field-programmable gate arrays,providing a practical and scalable approach for solving large-scale combinatorial optimization problems.展开更多
基金financial support from the National Key R&D Program of China(2023YFA1407100)the National Natural Science Foundation of China(22373026)+1 种基金Guangdong Science and Technology Department(2021B0301030005,STKJ2023072,GDZX2304005,GDZX2504001,and 2021QN02X538)Ioan Bâldea gratefully acknowledges computational support by the state of Baden-Württemberg through bwHPC and the German Research Foundation through Grant Nos.INST 40/575-1,35/1597-1,and 35/1134-1(JUSTUS 2,bwUniCluster 2/3,and bwForCluster/MLS&WISO/HELIX 2).
文摘Control over charge transport in molecular–scale devices requires a deep understanding of how minute structural changes influence electronic properties.Here,we demonstrate dual transport regimes in tunnel junctions of n-alk-1-yne(CnA)molecules with gold electrodes driven by conformational bifurcation—the emergence of two nearly isoenergetic(planar and skewed)molecular conformers(dihedral anglesα=180°andα≈65°at the alkyne terminus in the gas phase).Although the energy differences are small,these subtle conformational differences manifest as distinct transport behaviors,uncovered through unsupervised machine learning,which identified two junction groups:“short”and“long”chains,with distinct attenuation factors(β_(short)≈1.0 vs.β_(long)≈0.74)and contact conductances(G_(c,short)≈200μS vs.G_(c,long)≈8μS).This dramatic impact of the dihedral angle exceeds the impact of the inter-ring twist angle in biphenyl-based junctions and rivals changes induced by switching from gold to platinum electrodes or from monothiol to dithiol anchors in oligoacene and oligophenylene junctions.X-ray photoelectron spectroscopy(XPS)confirmed this bifurcation,linking the“short”and“long”groups to planar and skewed conformers,with dihedrals remarkably agreeing with the gas-phase values.This work establishes conformational bifurcation as a promising route for designing programmable nanotransport properties through anchor-group control.
基金supported by the Natural Science Founda tion of Chongqing(Grant No.CSTB2024NSCQ-MSX0944)。
文摘This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.
基金supported by the National Key Research and Development Program of China(Grant No.2024YFA1408500)the National Natural Science Foundation of China(Grant Nos.12174028 and 12574115)the Open Fund of the State Key Laboratory of Spintronics Devices and Technologies(Grant No.SPL-2408)。
文摘We incorporate a non-Markovian feedback mechanism into the simulated bifurcation method for dynamical solvers addressing combinatorial optimization problems.By reinjecting a portion of dissipated kinetic energy into each spin in a history-dependent and trajectory-informed manner,the method effectively suppresses early freezing induced by inelastic boundaries and enhances the system's ability to explore complex energy landscapes.Numerical results on the maximum cut(MAX-CUT)instances of fully connected Sherrington–Kirkpatrick(SK)spin glass models,including the 2000-spin K_(2000)benchmark,demonstrate that the non-Markovian algorithm significantly improves both solution quality and convergence speed.Tests on randomly generated SK instances with 100 to 1000 spins further indicate favorable scalability and substantial gains in computational efficiency.Moreover,the proposed scheme is well suited for massively parallel hardware implementations,such as field-programmable gate arrays,providing a practical and scalable approach for solving large-scale combinatorial optimization problems.
