Pith. sign in

REVIEW 2 major objections 5 minor 79 references

Heat Transport Hysteresis Generated through Frequency Switching of a Time-Dependent Temperature Gradient

T0 review · 2 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Pith's one-line read The paper derives exact analytical energy-flux formulas for a Langevin particle coupled to two heat baths with oscillating temperatures, where one bath's oscillation frequency is periodically switched, and shows that this frequency…

desk verdict A clean analytic extension to frequency-switched baths whose control claim needs an explicit commensurability condition before publication. read the letter →

arxiv 2501.12649 v1 pith:7R32I5W7 submitted 2025-01-22 cond-mat.mes-hall cond-mat.stat-mech

classification cond-mat.mes-hallcond-mat.stat-mech
keywords heattransporthysteresisfrequencyswitchingstochasticenergeticsLangevindynamicstime-dependenttemperaturegradientthermalmemristorpinchedloop
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper derives exact analytical formulas for the time-dependent heat fluxes in a minimal nanoscale model: a single particle coupled to two heat baths whose temperatures oscillate, with the oscillation frequency of one bath periodically switching between a fast and a slow value. Using a stochastic-energetics treatment of the Langevin equation, it shows that frequency switching reshapes the heat-transport hysteresis loop -- the curve of energy flux versus temperature difference -- producing pinched loops and multi-loop patterns that do not appear without switching. The closed-form expressions match nonequilibrium molecular dynamics simulations, and the authors argue this gives a control handle for nanoscale thermal memory devices such as thermal memristors and memcapacitors. If correct, the paper establishes frequency switching as a design tool for shaping heat-transport memory in molecular junctions.

What carries the argument

The central object is the time-dependent nonequilibrium state (TDNES) of a Langevin particle, and the method is the Sekimoto stochastic-energetics decomposition of the Langevin equation into system and bath energy fluxes. The load-bearing identities are the exact flux formulas Eqs. (24)-(26) (free particle) and Eqs. (46)-(47) (harmonic oscillator), built from the piecewise integrals $I(t,\gamma)$ and $P(t,\gamma)$ that sum the right bath's contribution over completed $\omega_{R1}$ and $\omega_{R2}$ segments. The phase factor $\phi = T_{R1}(\omega_{R1}-\omega_{R2})$ keeps $T_R(t)$ continuous at each switch, and the overall period $T_R = T_{R1}+T_{R2}$ makes the long-time state periodic, which is what turns the flux into a closed hysteresis loop in the $J$-$\Delta T$ plane.

What would settle it

Measure the time-resolved heat flux through a single-molecule junction while oscillating both electrode temperatures and periodically switching one electrode's oscillation frequency between a fast and a slow value; if the observed J-versus-$\Delta$-T loops lack the predicted knee at the switching time, the pinched multi-loop topology, or the quantitative flux values of Eqs. (46)-(47) for parameters like those of Figs. 3-5, the central claim would be refuted.

Watch

Extended reading notes

Core claim

The central discovery is that periodically switching the oscillation frequency of one bath's temperature turns the heat-transport hysteresis curve into a piecewise-smooth, multi-valued loop whose shape is exactly computable. For a free particle and for a harmonic oscillator, the long-time energy fluxes $J_L(t)$, $J_R(t)$, and $J_{\rm sys}(t)$ are given in closed form by Eqs. (24)-(26) and (46)-(47), which incorporate the frequency-switched right-bath temperature through the piecewise integrals $I(t,\gamma)$ and $P(t,\gamma)$. These expressions show that when the right-bath frequency switches between $\omega_{R1}$ and $\omega_{R2}$, the hysteresis curve jumps from one loop to another at the switching time, yielding kneed, pinched, and multi-loop structures; the sharpness of the knee decreases as the harmonic force constant $k$ increases. Excellent agreement with molecular dynamics simulation supports the claim that these exact expressions capture the transport physics.

Load-bearing premise

Each bath is a memoryless Markovian reservoir whose temperature enters only through the instantaneous noise strength, and the frequency switch is instantaneous, so the bath dynamics itself is unchanged except for the prescribed temperature.

Editorial extensions

If this is right

  • A single nanoscale junction can be switched between distinct hysteresis-loop shapes, giving multiple memory states in the flux response.
  • The exact formulas predict where pinched loops and multi-loop structures appear as functions of the two switching frequencies, temperature amplitudes, and harmonic force constant.
  • Increasing the harmonic force constant smooths the switching knee, so the two frequency regimes blend into a single loop.
  • The derivation extends the stochastic-energetics toolkit to piecewise time-dependent temperatures, enabling systematic study of other switching protocols.
  • The results support the feasibility of thermal memristors and memcapacitors that store information in hysteretic heat flux.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the switching frequencies are incommensurate, the strict periodicity underlying the closed loops would break, likely producing quasiperiodic or slowly drifting flux trajectories rather than exact closed hysteresis loops.
  • The flux has derivative discontinuities at each switch, so tools from piecewise-smooth dynamical systems could characterize the knee, pinching, and loop-bifurcation behavior beyond the examples shown.
  • The phase factor that keeps the right-bath temperature continuous at each switch could be tuned independently, offering an extra design parameter for reshaping loops.
  • Real molecular junctions with non-Markovian baths or anharmonic potentials would deviate quantitatively from these exact formulas, but the qualitative pinching and multi-loop features should survive if the Markovian assumption holds approximately.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper develops a stochastic-energetics description of heat transport in a single-particle model coupled to two Langevin baths, where one bath temperature oscillates sinusoidally and the other is piecewise frequency-switched between two oscillation frequencies. Analytical expressions for the energy fluxes JL, JR, and Jsys are derived for a free particle and a harmonic oscillator in the long-time time-dependent nonequilibrium state (TDNES), and the expressions are used to construct heat-flux-versus-temperature-difference hysteresis loops. The authors report agreement with nonequilibrium molecular dynamics simulations in a free-particle case and a harmonic-oscillator case, and they show that frequency switching produces pinched and multi-loop hysteresis structures, which they argue are relevant to thermal memristors and memcapacitors.

Significance. The model is exactly solvable within its stated assumptions, and the derivation from the Langevin equation and Sekimoto energy balance is transparent and contains no fitted parameters. The validation against independent simulation in the free-particle and harmonic-oscillator cases is a genuine strength. If the periodicity restriction discussed in the major comments is made explicit, the paper would provide a clean exact illustration of how frequency switching can reshape hysteresis and generate memory-like response; the connection to thermal neuromorphic devices is qualitative but plausible. The main novelty is the piecewise frequency-switching mechanism and the resulting pinched and multi-loop hysteresis structures.

major comments (2)
  1. [Section II (Eqs. (5)-(6)) and Section IV (Figures 3-5)] The combined left/right driving is T_R-periodic only if ω_L T_R/(2π) is rational, yet the paper never states this compatibility condition. All of the TDNES derivation and the closed hysteresis curves in Figures 3-5 assume a common period T_R. For incommensurate ω_L and 2π/T_R, the long-time state is quasiperiodic, the expressions in Eqs. (24)-(26) and (46)-(47) do not describe a closed curve over one T_R, and the claimed pinched loops, multi-loop structures, and thermal-memory behavior are not well-defined. The authors should state the commensurability requirement explicitly and restrict the hysteresis/memory claims to that case, or analyze the quasiperiodic case separately.
  2. [Figure 4 and Section IV] The central demonstration of frequency-switching-induced multi-loop and pinched hysteresis is presented only through analytical curves, without simulation data for that parameter set. Since Figures 2 and 5 show that such validation is feasible, providing a simulation comparison for Figure 4 would substantially strengthen the claim that the predicted multi-loop structures are a real feature of the model rather than an artifact of the analytical expressions.
minor comments (5)
  1. [Eq. (4)] The second interval in the definition of ω_R(t) is written as 'TR1 ≤ tmod < TR2'; it should be 'TR1 ≤ tmod < TR1 + TR2' (i.e., the full period TR).
  2. [Eq. (38)] The right-bath noise-velocity correlation is written with the left-bath coupling γ_L on the right-hand side; it should be γ_R.
  3. [Section II] The sentence introducing the coupling strengths says 'γL and γL parameterize'; this should presumably read 'γL and γR parameterize'.
  4. [Figure 5 caption] The caption labels the k=0 panel as a harmonic oscillator, though k=0 is the free-particle case; consider clarifying that the k=0 limit recovers the free-particle result.
  5. [Section III.A] The text says that the t→∞ limit yields the TDNES while retaining terms containing e^{-2γt}; it would be clearer to state explicitly that the combination e^{-2γt} I(t,γ) is what remains finite and periodic in this limit.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the energy-flux expressions are derived from the stated Langevin model and validated against independent NEMD simulations; self-citations provide only the starting formalism.

full rationale

The paper's central result, Eqs. (24)-(26) and (46)-(47), is obtained by directly evaluating the noise-velocity and velocity correlation functions (Eqs. (15)-(17) and (37)-(39)) from the specified Langevin dynamics in Eqs. (1)-(2), with no parameter fitted to the hysteresis data. The only recourse to prior work by the authors (Refs. 3, 27, and 60) is to justify the standard Sekimoto flux decomposition and to set the general form of the calculation; the frequency-switched temperature TR(t) in Eq. (6) and the piecewise integrals I(t, gamma) and P(t, gamma) are new to this paper and are derived, not assumed. The analytical expressions are then checked against independent stochastic molecular dynamics simulations (Figs. 2 and 5), so the claimed prediction is not equivalent to an input by construction. The paper does not explicitly state the commensurability condition (omega_L T_R/(2 pi) rational) needed for the combined driving to be strictly T_R-periodic, and this is a genuine scope/validity caveat for incommensurate parameters, but it does not make the derivation circular. Consequently there are no circularity steps to report.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No fitted free parameters are introduced: gamma_L, gamma_R, T0, DeltaT, and frequencies are chosen model inputs. The central claim rests on Markovian white-noise baths, linear potentials, and the existence of a periodic TDNES. No new particles, forces, or conserved quantities are postulated.

assumptions (4)
  • standard math Gaussian white-noise Langevin dynamics: formal solutions (14) and (34) and the delta-correlation integrations are treated as standard.
    The velocity and noise-velocity correlation functions are evaluated by integrating delta-correlated noise against exponential kernels, a standard linear Langevin calculation.
  • domain assumption The thermal baths are memoryless Markovian reservoirs: noise correlations are delta-correlated with instantaneous temperatures TL(t) and TR(t), Eq. (2).
    This is the central modeling premise. Real molecular junctions have non-Markovian noise and frequency-dependent bath response, so quantitative transfer to experiments is not guaranteed.
  • domain assumption A time-periodic nonequilibrium state with overall period TR exists in the long-time limit.
    The formulas and hysteresis loops assume a periodic TDNES. The analyzed examples use commensurate frequencies (omega_L=5, omega_R1=5 or omega_L=0), but arbitrary incommensurate driving would produce a quasiperiodic state not covered by the closed-form expressions.
  • domain assumption The system is linear: only free or harmonic potentials are considered, so the exact Gaussian response follows from the Langevin equation.
    Anharmonic potentials would introduce nonlinear corrections that are not in the derived expressions; the central claim is scoped to this paradigmatic model.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Heat Transport Hysteresis Generated through Frequency Switching of a Time-Dependent Temperature Gradient." pith.science (2026). https://pith.science/paper/7R32I5W7

@misc{pith2026250112649,
  author       = {Pith},
  title        = {Pith review of: Heat Transport Hysteresis Generated through Frequency Switching of a Time-Dependent Temperature Gradient},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7R32I5W7}},
  note         = {Machine review of arXiv:2501.12649}
}
read the original abstract

A stochastic energetics framework is applied to examine how periodically shifting the frequency of a time-dependent oscillating temperature gradient affects heat transport in a nanoscale molecular model. We specifically examine the effects that frequency switching, i.e., instantaneously changing the oscillation frequency of the temperature gradient, has on the shape of the heat transport hysteresis curves generated by a particle connected to two thermal baths, each with a temperature that is oscillating in time. Analytical expressions are derived for the energy fluxes in/out of the system and the baths, with excellent agreement observed between the analytical expressions and the results from nonequilibrium molecular dynamics simulations. We find that the shape of the heat transport hysteresis curves can be significantly altered by shifting the frequency between fast and slow oscillation regimes. We also observe the emergence of features in the hysteresis curves such as pinched loops and complex multi-loop patterns due to the frequency shifting. The presented results have implications in the design of thermal neuromorphic devices such as thermal memristors and thermal memcapacitors.

Figures

Figures reproduced from arXiv: 2501.12649 by the authors.

Figure 1
Figure 1. FIG. 1. Time-dependence of the left and right bath temperatures and the temperature difference [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Energy flux as a function of time for the left bath (red) and right bath (blue) for the case [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Energy flux as a function of temperature difference for the left bath (top), right bath [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Energy flux as a function of temperature difference for the left bath (top), right bath [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Energy flux as a function of temperature difference for a harmonic oscillator with force [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

79 extracted references · 59 canonical work pages

  1. [1]

    Jsys is the energy flux in/out of the system

  2. [2]

    JL is the energy flux associated with the left bath

  3. [3]

    JR is the energy flux associated with the right bath A sum over all the energy fluxes obeys conservation of energy: JL(t) + JR(t) + Jsys(t) = 0. (10) The expectation values for the energy fluxes can be expressed as [3, 4, 66, 70, 71]: JL(t) = mγL v2(t) − m ξL(t)v(t) , (11) JR(t) = mγR v2(t) − m ξR(t)v(t) , (12) Jsys(t) = d E(t) dt , (13) where E(t) is the...

  4. [4]

    Ben-Abdallah, AIP Adv

    P. Ben-Abdallah, AIP Adv. 7, 065002 (2017), doi:10.1063/1.4985055

  5. [5]

    Ordonez-Miranda, Y

    J. Ordonez-Miranda, Y. Ezzahri, J. A. Tiburcio-Moreno, K. Joulain, and J. Drevillon, Phys. Rev. Lett. 123, 025901 (2019), doi:10.1103/PhysRevLett.123.025901

  6. [6]

    R. Chen, T. Gibson, and G. T. Craven, Phys. Rev. E 108, 024148 (2023), doi:10.1103/PhysRevE.108.024148

  7. [7]

    J. L. Lebowitz, Phys. Rev. 114, 1192 (1959), doi:10.1103/PhysRev.114.1192

  8. [8]

    Rieder, J

    Z. Rieder, J. L. Lebowitz, and E. Lieb, J. Math. Phys. 8, 1073 (1967), doi:10.1063/1.1705319

Show all 79 references
  1. [9]

    Casher and J

    A. Casher and J. L. Lebowitz, J. Math. Phys. 12, 1701 (1971), doi:10.1063/1.1665794

  2. [10]

    D. G. Cahill, W. K. Ford, K. E. Goodson, G. D. Mahan, A. Majumdar, H. J. Maris, R. Merlin, and S. R. Phillpot, J. Appl. Phys. 93, 793 (2003), doi:10.1063/1.1524305

  3. [11]

    Segal and A

    D. Segal and A. Nitzan, Phys. Rev. Lett. 94, 034301 (2005), doi:10.1103/PhysRevLett.94.034301

  4. [12]

    Segal and B

    D. Segal and B. K. Agarwalla, Annu. Rev. Phys. Chem. 67, 185 (2016), doi:10.1146/annurev- physchem-040215-112103

  5. [13]

    Galperin, A

    M. Galperin, A. Nitzan, and M. A. Ratner, Phys. Rev. B 75, 155312 (2007), doi:10.1103/PhysRevB.75.155312

  6. [14]

    Narayana and Y

    S. Narayana and Y. Sato, Phys. Rev. Lett. 108, 214303 (2012), doi:10.1103/PhysRevLett.108.214303

  7. [15]

    Maldovan, Nature 503, 209 (2013), doi:10.1038/nature12608

    M. Maldovan, Nature 503, 209 (2013), doi:10.1038/nature12608

  8. [16]

    D. M. Leitner, Annu. Rev. Phys. Chem. 59, 233 (2008), doi:10.1146/annurev.physchem.59.032607.093606

  9. [17]

    D. M. Leitner, J. Phys. Chem. B 117, 12820 (2013), doi:10.1021/jp402012z

  10. [18]

    Brandner, K

    K. Brandner, K. Saito, and U. Seifert, Phys. Rev. X 5, 031019 (2015), doi:10.1103/PhysRevX.5.031019

  11. [19]

    N. Li, J. Ren, L. Wang, G. Zhang, P. H¨ anggi, and B. Li, Rev. Mod. Phys. 84, 1045 (2012), doi:10.1103/RevModPhys.84.1045

  12. [20]

    Dubi and M

    Y. Dubi and M. Di Ventra, Rev. Mod. Phys. 83, 131 (2011), doi:10.1103/RevModPhys.83.131. 21

  13. [21]

    J. S. Lim, R. L´ opez, and D. S´ anchez, Phys. Rev. B 88, 201304 (2013), doi:10.1103/PhysRevB.88.201304

  14. [22]

    G. T. Craven and A. Nitzan, Proc. Natl. Acad. Sci. 113, 9421 (2016), doi:10.1073/pnas.1609141113

  15. [23]

    J. Zhu, Y. Liu, and D. He, Phys. Rev. E 103, 062121 (2021), doi:10.1103/PhysRevE.103.062121

  16. [24]

    Ordonez-Miranda, R

    J. Ordonez-Miranda, R. Anufriev, M. Nomura, and S. Volz, Phys. Rev. B 106, L100102 (2022), doi:10.1103/PhysRevB.106.L100102

  17. [26]

    Sharony, R

    I. Sharony, R. Chen, and A. Nitzan, J. Chem. Phys. 153, 144113 (2020), doi:10.1063/5.0022423

  18. [27]

    S. V. Dmitriev, V. A. Kuzkin, and A. M. Krivtsov, Phys. Rev. E 108, 054221 (2023), doi:10.1103/PhysRevE.108.054221

  19. [28]

    Dastgeer, Z

    G. Dastgeer, Z. M. Shahzad, H. Chae, Y. H. Kim, B. M. Ko, and J. Eom, Adv. Funct. Mater. 32, 2204781 (2022), doi:10.1002/adfm.202204781

  20. [29]

    Dastgeer, S

    G. Dastgeer, S. Nisar, A. Rasheed, K. Akbar, V. D. Chavan, D. kee Kim, S. M. Wabaidur, M. W. Zulfiqar, and J. Eom, Nano Energy 119, 109106 (2024), doi:10.1016/j.nanoen.2023.109106

  21. [30]

    R. Chen, T. Gibson, and G. T. Craven, J. Chem. Phys. 160, 194305 (2024), doi:10.1063/5.0204819

  22. [31]

    G. T. Craven and A. Nitzan, J. Chem. Phys. 158 (2023), doi:10.1063/5.0144248

  23. [32]

    Segal and A

    D. Segal and A. Nitzan, J. Chem. Phys. 122, 194704 (2005), doi:10.1063/1.1900063

  24. [33]

    Wu and B

    G. Wu and B. Li, Phys. Rev. B 76, 085424 (2007), doi:10.1103/PhysRevB.76.085424

  25. [34]

    Segal, Phys

    D. Segal, Phys. Rev. Lett. 100, 105901 (2008), doi:10.1103/PhysRevLett.100.105901

  26. [35]

    Wu and D

    L.-A. Wu and D. Segal, Phys. Rev. Lett. 102, 095503 (2009), doi:10.1103/PhysRevLett.102.095503

  27. [36]

    G. T. Craven, D. He, and A. Nitzan, Phys. Rev. Lett. 121, 247704 (2018), doi:10.1103/PhysRevLett.121.247704

  28. [37]

    M. A. Sim´ on, A. Ala˜ na, M. Pons, A. Ruiz-Garc ´ ıa, and J. G. Muga, Phys. Rev. E103, 012134 (2021), doi:10.1103/PhysRevE.103.012134. 22

  29. [38]

    D. M.-T. Kuo and Y.-C. Chang, Phys. Rev. B 81, 205321 (2010), doi:10.1103/PhysRevB.81.205321

  30. [39]

    B. Li, L. Wang, and G. Casati, Phys. Rev. Lett. 93, 184301 (2004), doi:10.1103/PhysRevLett.93.184301

  31. [41]

    Roberts and D

    N. Roberts and D. Walker, Int. J. Therm. Sci. 50, 648 (2011), doi:10.1016/j.ijthermalsci.2010.12.004

  32. [42]

    M. J. Mart ´ ınez-P´ erez, A. Fornieri, and F. Giazotto, Nature Nanotech. 10, 303 (2015), doi:10.1038/nnano.2015.11

  33. [43]

    H. Zhao, X. Yang, C. Wang, R. Lu, T. Zhang, H. Chen, and X. Zheng, Mater. Today Phys. 30, 100941 (2023), doi:10.1016/j.mtphys.2022.100941

  34. [44]

    Romero-Bastida and B

    M. Romero-Bastida and B. A. Mart ´ ınez-Torres, J. Phys.: Condens. Matter36, 025302 (2023), doi:10.1088/1361-648X/acff32

  35. [45]

    B. Li, L. Wang, and G. Casati, Appl. Phys. Lett. 88, 143501 (2006), doi:10.1063/1.2191730

  36. [46]

    Ben-Abdallah and S.-A

    P. Ben-Abdallah and S.-A. Biehs, Phys. Rev. Lett. 112, 044301 (2014), doi:10.1103/PhysRevLett.112.044301

  37. [47]

    Joulain, J

    K. Joulain, J. Drevillon, Y. Ezzahri, and J. Ordonez-Miranda, Phys. Rev. Lett. 116, 200601 (2016), doi:10.1103/PhysRevLett.116.200601

  38. [48]

    S. Wang, A. L. Cottrill, Y. Kunai, A. R. Toland, P. Liu, W.-J. Wang, and M. S. Strano, Phys. Chem. Chem. Phys. 19, 13172 (2017), doi:10.1039/C7CP02445B

  39. [49]

    G. T. Craven and A. Nitzan, J. Chem. Phys. 146, 092305 (2017), doi:10.1063/1.4971293

  40. [50]

    A. A. Odebowale, A. M. Berhe, H. T. Hattori, and A. E. Miroshnichenko, Applied Sciences 14 (2024), 10.3390/app14062633

  41. [51]

    Seifert, Rep

    U. Seifert, Rep. Prog. Phys. 75, 126001 (2012), http://stacks.iop.org/0034- 4885/75/i=12/a=126001

  42. [52]

    Van den Broeck, in Physics of Complex Colloids, Vol

    C. Van den Broeck, in Physics of Complex Colloids, Vol. 184 (IOS Phys. Rev. Ess, 2013) pp. 155–193

  43. [53]

    Reimann, Phys

    P. Reimann, Phys. Rev. 361, 57 (2002), doi:10.1016/S0370-1573(01)00081-3

  44. [54]

    Brey and J

    J. Brey and J. Casado, J. Stat. Phys. 61, 713 (1990)

  45. [55]

    A. V. Popov and R. Hernandez, J. Chem. Phys. 126, 244506 (2007), doi:10.1063/1.2743032. 23

  46. [56]

    A. V. Popov and R. Hernandez, Phys. Rev. E 88, 032145 (2013), 10.1103/Phys- RevE.88.032145

  47. [57]

    I. J. Ford, Z. P. L. Laker, and H. J. Charlesworth, Phys. Rev. E 92, 042108 (2015), doi:10.1103/PhysRevE.92.042108

  48. [58]

    Brandner and U

    K. Brandner and U. Seifert, Phys. Rev. E 93, 062134 (2016), doi:10.1103/PhysRevE.93.062134

  49. [59]

    Awasthi and S

    S. Awasthi and S. B. Dutta, Phys. Rev. E 103, 062143 (2021), doi:10.1103/PhysRevE.103.062143

  50. [60]

    Portugal, F

    P. Portugal, F. Brange, and C. Flindt, Phys. Rev. Res. 4, 043112 (2022), doi:10.1103/PhysRevResearch.4.043112

  51. [61]

    Lanoisel´ ee, A

    Y. Lanoisel´ ee, A. Stanislavsky, D. Calebiro, and A. Weron, Phys. Rev. E106, 064127 (2022), doi:10.1103/PhysRevE.106.064127

  52. [62]

    V. A. Kuzkin and A. M. Krivtsov, Phys. Rev. E 101, 042209 (2020), doi:10.1103/PhysRevE.101.042209

  53. [63]

    Chen and G

    R. Chen and G. T. Craven, Journal of Physics: Condensed Matter 36, 405201 (2024), doi:10.1088/1361-648X/ad5d40

  54. [64]

    Bernardo, C

    M. Bernardo, C. Budd, A. R. Champneys, and P. Kowalczyk, Piecewise-smooth dynamical systems: theory and applications, Vol. 163 (Springer Science & Business Media, 2008)

  55. [65]

    Bonet, M

    C. Bonet, M. R. Jeffrey, P. Mart ´ ın, and J. M. Olm, Communications in Nonlinear Science and Numerical Simulation 102, 105950 (2021)

  56. [66]

    Bonet, M

    C. Bonet, M. R. Jeffrey, P. Mart ´ ın, and J. M. Olm, Communications in Nonlinear Science and Numerical Simulation 118, 107032 (2023)

  57. [67]

    Han and Q

    X. Han and Q. Bi, Chaos, Solitons & Fractals 169, 113270 (2023)

  58. [68]

    Jiang, X

    S. Jiang, X. Han, and H. Yu, Nonlinear Dynamics 112, 19013 (2024)

  59. [69]

    Sekimoto, Prog

    K. Sekimoto, Prog. Theor. Phys. Supp. 130, 17 (1998), doi:10.1143/PTPS.130.17

  60. [71]

    G. T. Craven and A. Nitzan, J. Chem. Phys. 148, 044101 (2018), doi:10.1063/1.5007854

  61. [72]

    A. C. Barato, E. Rold´ an, I. A. Mart ´ ınez, and S. Pigolotti, Phys. Rev. Lett. 121, 090601 (2018), doi:10.1103/PhysRevLett.121.090601

  62. [73]

    Sabhapandit, Phys

    S. Sabhapandit, Phys. Rev. E 85, 021108 (2012), doi:10.1103/PhysRevE.85.021108

  63. [74]

    Dhar and R

    A. Dhar and R. Dandekar, Physica A 418, 49 (2015), doi:10.1016/j.physa.2014.06.002. 24

  64. [75]

    Reddy, S.-Y

    P. Reddy, S.-Y. Jang, R. A. Segalman, and A. Majumdar, Science 315, 1568 (2007), doi:10.1126/science.1137149

  65. [76]

    A. Tan, J. Balachandran, S. Sadat, V. Gavini, B. D. Dunietz, S.-Y. Jang, and P. Reddy, J. Am. Chem. Soc. 133, 8838 (2011), doi:10.1021/ja202178k

  66. [77]

    W. Lee, K. Kim, W. Jeong, L. A. Zotti, F. Pauly, J. C. Cuevas, and P. Reddy, Nature 498, 209 (2013), doi:10.1038/nature12183

  67. [78]

    Y. Kim, W. Jeong, K. Kim, W. Lee, and P. Reddy, Nature Nanotech. 9, 881 (2014), doi:10.1038/nnano.2014.209

  68. [79]

    Capozzi, J

    B. Capozzi, J. Xia, O. Adak, E. J. Dell, Z.-F. Liu, J. C. Taylor, J. B. Neaton, L. M. Campos, and L. Venkataraman, Nature Nanotech. 10, 522 (2015), doi:10.1038/nnano.2015.97

  69. [80]

    L. Cui, S. Hur, Z. A. Akbar, J. C. Kl¨ ockner, W. Jeong, F. Pauly, S.-Y. Jang, P. Reddy, and E. Meyhofer, Nature 572, 628 (2019), doi:10.1038/s41586-019-1420-z

  70. [81]

    Mosso, H

    N. Mosso, H. Sadeghi, A. Gemma, S. Sangtarash, U. Drechsler, C. Lambert, and B. Gotsmann, Nano Letters 19, 7614 (2019), doi:10.1021/acs.nanolett.9b02089

  71. [82]

    N. A. Zimbovskaya and A. Nitzan, J. Phys. Chem. B 124, 2632 (2020), doi:10.1021/acs.J. Phys. Chem. B.0c00059. 25

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.