672 research outputs found

    Memory Effect, Rejuvenation and Chaos Effect in the Multi-layer Random Energy Model

    Full text link
    We introduce magnetization to the Multi-layer Random Energy Model which has a hierarchical structure, and perform Monte Carlo simulation to observe the behavior of ac-susceptibility. We find that this model is able to reproduce three prominent features of spin glasses, i.e., memory effect, rejuvenation and chaos effect, which were found recently by various experiments on aging phenomena with temperature variations.Comment: 10 pages, 14 figures, to be submitted to J. Phys. Soc. Jp

    Scaling Law and Aging Phenomena in the Random Energy Model

    Full text link
    We study the effect of temperature shift on aging phenomena in the Random Energy Model (REM). From calculation on the correlation function and simulation on the Zero-Field-Cooled magnetization, we find that the REM satisfies a scaling relation even if temperature is shifted. Furthermore, this scaling property naturally leads to results obtained in experiment and the droplet theory.Comment: 8 pages, 7 figures, to be submitted to J. Phys. Soc. Jp

    Numerical Study of Aging in the Generalized Random Energy Model

    Full text link
    Magnetizations are introduced to the Generalized Random Energy Model (GREM) and numerical simulations on ac susceptibility is made for direct comparison with experiments in glassy materials. Prominent dynamical natures of spin glasses, {\it i.e.}, {\em memory} effect and {\em reinitialization}, are reproduced well in the GREM. The existence of many layers causing continuous transitions is very important for the two natures. Results of experiments in other glassy materials such as polymers, supercooled glycerol and orientational glasses, which are contrast to those in spin glasses, are interpreted well by the Single-layer Random Energy Model.Comment: 8 pages, 9 figures, to be submitted to J. Phys. Soc. Jp

    Aging, rejuvenation, and memory effects in short-range Ising spin glass: Cu0.5_{0.5}Co0.5_{0.5}Cl2_{2}-FeCl3_{3} graphite bi-intercalation compound

    Full text link
    Non-equilibrium aging dynamics in 3D Ising spin glass Cu0.5_{0.5}Co0.5_{0.5}Cl2_{2}-FeCl3_{3} GBIC has been studied by zero-field cooled (ZFC) magnetization and low frequency AC magnetic susceptibility (f=0.05f = 0.05 Hz), where Tg=3.92±0.11T_{g} = 3.92 \pm 0.11 K. The time dependence of the relaxation rate S(t)=(1/H)S(t) = (1/H)dMZFC/M_{ZFC}/dlnt\ln t for the ZFC magnetization after the ZFC aging protocol, shows a peak at a characteristic time tcrt_{cr} near a wait time twt_{w} (aging behavior), corresponding to a crossover from quasi equilibrium dynamics to non-equilibrium. The time tcrt_{cr} strongly depends on twt_{w}, temperature (TT), magnetic field (HH), and the temperature shift (ΔT\Delta T). The rejuvenation effect is observed in both χ\chi^{\prime} and χ\chi^{\prime\prime} under the TT-shift and HH-shift procedures. The memory of the specific spin configurations imprinted during the ZFC aging protocol can be recalled when the system is re-heated at a constant heating rate. The aging, rejuvenation, and memory effects observed in the present system are discussed in terms of the scaling concepts derived from numerical studies on 3D Edwards-Anderson spin glass model.Comment: 14 pages, 14 figures; Eur. Phys. J. B accepted for publicatio

    Relaxation of the field-cooled magnetization of an Ising spin glass

    Full text link
    The time and temperature dependence of the field-cooled magnetization of a three dimensional Ising spin glass, Fe_{0.5}Mn_{0.5}TiO_{3}, has been investigated. The temperature and cooling rate dependence is found to exhibit memory phenomena that can be related to the memory behavior of the low frequency ac-susceptibility. The results add some further understanding on how to model the three dimensional Ising spin glass in real space.Comment: 8 pages RevTEX, 5 figure

    Extraction of the Spin Glass Correlation Length

    Full text link
    The peak of the spin glass relaxation rate, S(t)=d{-M_{TRM}(t,t_w)}/H/{d ln t}, is directly related to the typical value of the free energy barrier which can be explored over experimental time scales. A change in magnetic field H generates an energy E_z={N_s}{X_fc}{H^2} by which the barrier heights are reduced, where X_{fc} is the field cooled susceptibility per spin, and N_s is the number of correlated spins. The shift of the peak of S(t) gives E_z, generating the correlation length, Ksi(t,T), for Cu:Mn 6at.% and CdCr_{1.7}In_{0.3}S_4. Fits to power law dynamics, Ksi(t,T)\propto {t}^{\alpha(T)} and activated dynamics Ksi(t,T) \propto {ln t}^{1/psi} compare well with simulation fits, but possess too small a prefactor for activated dynamics.Comment: 4 pages, 4 figures. Department of Physics, University of California, Riverside, California, and Service de Physique de l'Etat Condense, CEA Saclay, Gif sur Yvette, France. To appear in Phys. Rev. Lett. January 4, 199

    Microplastic in wild populations of the omnivorous crab Carcinus aestuarii: A review and a regional-scale test of extraction methods, including microfibres

    Get PDF
    Microplastic (MP) has become ubiquitous in the marine environment. Its threat to marine organisms has been demonstrated under laboratory conditions, yet studies on wild populations still face methodological difficulties. We reviewed the methods used to separate MP from soft animal tissues and highlighted a lack of standardised methodologies, particularly critical for synthetic microfibres. We further compared enzymatic and a potassium hydroxide (KOH)-based alkaline digestion protocols on wild crabs (Carcinus aestuarii) collected from three coastal lagoons in the north Adriatic Sea and on laboratory-prepared synthetic polyester (PES) of different colour and polypropylene (PP). We compared the cost-effectiveness of the two methods, together with the potential for adverse quantitative or qualitative effects on MP that could alter the capability of the polymers to be recognised via microscopic or spectroscopic techniques. Only 5.5% of the 180 examined crabs contained MP in their gastrointestinal tracts, with a notably high quantitative variability between individuals (from 1 to 117 particles per individual). All MP found was exclusively microfibres, mainly PES, with a mean length (\ub1SE) of 0.5\u202f\ub1\u202f0.03\u202fmm. The two digestion methods provided comparable estimates on wild crabs and did not cause any visible physical or chemical alterations on laboratory-prepared microfibres treated for up to 4 days. KOH solution was faster and cheaper compared to the enzymatic extraction, involving fewer procedural steps and therefore reducing the risk of airborne contamination. With digestion times longer than 4 days, KOH caused morphological alterations of some of the PES microfibres, which did not occur with the enzymatic digestion. This suggests that KOH is effective for the digestion of small marine invertebrates or biological samples for which shorter digestion time is required, while enzymatic extraction should be considered as alternative for larger organisms or sample sizes requiring longer digestion times

    Off-Equilibrium Dynamics in Finite-Dimensional Spin Glass Models

    Full text link
    The low temperature dynamics of the two- and three-dimensional Ising spin glass model with Gaussian couplings is investigated via extensive Monte Carlo simulations. We find an algebraic decay of the remanent magnetization. For the autocorrelation function C(t,tw)=[]avC(t,t_w)=[]_{av} a typical aging scenario with a t/twt/t_w scaling is established. Investigating spatial correlations we find an algebraic growth law ξ(tw)twα(T)\xi(t_w)\sim t_w^{\alpha(T)} of the average domain size. The spatial correlation function G(r,tw)=[<Si(tw)Si+r(tw)>2]avG(r,t_w)=[< S_i(t_w)S_{i+r}(t_w)>^2]_{av} scales with r/ξ(tw)r/\xi(t_w). The sensitivity of the correlations in the spin glass phase with respect to temperature changes is examined by calculating a time dependent overlap length. In the two dimensional model we examine domain growth with a new method: First we determine the exact ground states of the various samples (of system sizes up to 100×100100\times 100) and then we calculate the correlations between this state and the states generated during a Monte Carlo simulation.Comment: 38 pages, RevTeX, 14 postscript figure
    corecore