Controlling the dominant magnetic relaxation mechanisms for magnetic hyperthermia in bimagnetic core-shell nanoparticles

We report a simple and effective way to control the heat generation of a magnetic colloid under alternate magnetic fields by changing the shell composition of bimagnetic core-shell Fe 3 O 4 /Zn x Co 1-x Fe 2 O 4 nanoparticles. The core-shell structure constitutes a magnetically-coupled biphase system, with an effective anisotropy that can be tuned by the substitution of Co 2+ by Zn 2+ ions in the shell. Magnetic hyperthermia experiments of nanoparticles dispersed in hexane and butter oil showed that the magnetic relaxation is dominated by Brown relaxation mechanism in samples with higher anisotropy (i.e., larger concentration of Co within the shell) yielding high specific power absorption values in low viscosity media as hexane. Increasing the Zn concentration of the shell, diminishes the magnetic anisotropy, which results in a change to a Néel relaxation that dominates the process when the nanoparticles are dispersed in a high-viscosity medium. We demonstrate that tuning the Zn contents at the shell of these exchange-coupled core/shell nanoparticles provides a way to control the magnetic anisotropy without loss of saturation magnetization. This ability is an essential prerequisite for most biomedical applications, where high viscosities and capturing mechanisms are present. This journal i

Magnetic hyperthermia experiments with magnetic nanoparticles in clarified butter oil and paraffin: A thermodynamic analysis

In specific power absorption models for magnetic fluid hyperthermia (MFH) experiments, the magnetic relaxation time of nanoparticles (NPs) is known to be a fundamental descriptor of the heating mechanisms. The relaxation time is mainly determined by the interplay between the magnetic properties of NPs and the rheological properties of NPs’ environment. Although the role of magnetism in MFH has been extensively studied, the thermal properties of the NP medium and their changes during MFH experiments have been underrated so far. Herein, we show that ZnxFe3-xO4 NPs dispersed through different media with phase transition in the temperature range of experiment as clarified butter oil (CBO) and paraffin. These systems show nonlinear behavior of the heating rate within the temperature range of MFH experiments. For CBO, a fast increase at ~306 K is associated with changes in the viscosity (¿(T)) and specific heat (cp(T)) of the medium at its melting temperature. This increment in the heating rate takes place around 318 K for paraffin. The magnetic and morphological characterization of NPs together with the observed agglomeration of NPs above 306 and 318 K for CBO and paraffin, respectively, indicate that the fast increase in MFH curves could not be associated with the change in the magnetic relaxation mechanism, with Neél relaxation being dominant. In fact, successive experimental runs performed up to temperatures below and above the CBO and paraffin melting points resulted in different MFH curves due to agglomeration of NPs driven by magnetic field inhomogeneity during the experiments. Our results highlight the relevance of the thermodynamic properties of the system NP-medium for an accurate measurement of the heating efficiency for in vitro and in vivo environments, where the thermal properties are largely variable within the temperature window of MFH experiments

Electron Spin Decoherence in Bulk and Quantum Well Zincblende Semiconductors

A theory for longitudinal (T1) and transverse (T2) electron spin coherence times in zincblende semiconductor quantum wells is developed based on a non-perturbative nanostructure model solved in a fourteen-band restricted basis set. Distinctly different dependences of coherence times on mobility, quantization energy, and temperature are found from previous calculations. Quantitative agreement between our calculations and measurements is found for GaAs/AlGaAs, InGaAs/InP, and GaSb/AlSb quantum wells.Comment: 11 pages, 3 figure

Adjusting the Neel relaxation time of Fe3O4/ZnxCo1-xFe2O4 core/shell nanoparticles for optimal heat generation in magnetic hyperthermia

In this work it is shown a precise way to optimize the heat generation in high viscosity magnetic colloids, by adjusting the Neel relaxation time in core/shell bimagnetic nanoparticles, for magnetic fluid hyperthermia (MFH) applications. To pursue this goal, Fe3O4/ZnxCo1-xFe2O4 core/shell nanoparticles were synthesized with 8.5 nm mean core diameter, encapsulated in a shell of similar to 1.1 nm of thickness, where the Zn atomic ratio (Zn/(Zn + Co) at%) changes from 33 to 68 at%. The magnetic measurements are consistent with a rigid interface coupling between the core and shell phases, where the effective magnetic anisotropy systematically decreases when the Zn concentration increases, without a significant change of the saturation magnetization. Experiments of MFH of 0.1 wt% of these particles dispersed in water, in Dulbecco modified Eagles minimal essential medium, and a high viscosity butter oil, result in a large specific loss power (SLP), up to 150 W g(-1), when the experiments are performed at 571 kHz and 200 Oe. The SLP was optimized adjusting the shell composition, showing a maximum for intermediate Zn concentration. This study shows a way to maximize the heat generation in viscous media like cytosol, for those biomedical applications that require smaller particle sizes

Effects of Zn Substitution in the Magnetic and Morphological Properties of Fe-Oxide-Based Core-Shell Nanoparticles Produced in a Single Chemical Synthesis

Magnetic, compositional, and morphological properties of Zn-Fe-oxide core-shell bimagnetic nanoparticles were studied for three samples with 0.00, 0.06, and 0.10 Zn/Fe ratios, as obtained from particle-induced X-ray emission analysis. The bimagnetic nanoparticles were produced in a one-step synthesis by the thermal decomposition of the respective acetylacetonates. The nanoparticles present an average particle size between 25 and 30 nm as inferred from transmission electron microscopy (TEM). High-resolution TEM images clearly show core-shell morphology for the particles in all samples. The core is composed by an antiferromagnetic (AFM) phase with a Wüstite (Fe1-yO) structure, whereas the shell is composed by a ZnxFe3-xO4 ferrimagnetic (FiM) spinel phase. Despite the low solubility of Zn in the Wüstite, electron energy-loss spectroscopy analysis indicates that Zn is distributed almost homogeneously in the whole nanoparticle. This result gives information on the formation mechanisms of the particle, indicating that the Wüstite is formed first, and the superficial oxidation results in the FiM ferrite phase with similar Zn concentration than the core. Magnetization and in-field Mössbauer spectroscopy of the Zn-richest nanoparticles indicate that the AFM phase is strongly coupled to the FiM structure of the ferrite shell, resulting in a bias field (HEB) appearing below TNFeO, with HEB values that depend on the core-shell relative proportion. Magnetic characterization also indicates a strong magnetic frustration for the samples with higher Zn concentration, even at low temperatures

General boundary conditions for the envelope function in multiband k.p model

We have derived general boundary conditions (BC) for the multiband envelope functions (which do not contain spurious solutions) in semiconductor heterostructures with abrupt heterointerfaces. These BC require the conservation of the probability flux density normal to the interface and guarantee that the multiband Hamiltonian be self--adjoint. The BC are energy independent and are characteristic properties of the interface. Calculations have been performed of the effect of the general BC on the electron energy levels in a potential well with infinite potential barriers using a coupled two band model. The connection with other approaches to determining BC for the envelope function and to the spurious solution problem in the multiband k.p model are discussed.Comment: 15 pages, 2 figures; to be published in Phys. Rev. B 65, March 15 issue 200

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Apolipoprotein E epsilon 4 (APOE-ε4) genotype is associated with decreased 6-month verbal memory performance after mild traumatic brain injury

Introduction: The apolipoprotein E (APOE) ε4 allele associates with memory impairment in neurodegenerative diseases. Its association with memory after mild traumatic brain injury (mTBI) is unclear. Methods: mTBI patients (Glasgow Coma Scale score 13–15, no neurosurgical intervention, extracranial Abbreviated Injury Scale score ≤1) aged ≥18 years with APOE genotyping results were extracted from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study. Cohorts determined by APOE-ε4(+/−) were assessed for associations with 6-month verbal memory, measured by California Verbal Learning Test, Second Edition (CVLT-II) subscales: Immediate Recall Trials 1–5 (IRT), Short-Delay Free Recall (SDFR), Short-Delay Cued Recall (SDCR), Long-Delay F

Pre-injury Comorbidities Are Associated With Functional Impairment and Post-concussive Symptoms at 3-and 6-Months After Mild Traumatic Brain Injury: A TRACK-TBI Study

Introduction: Over 70% of traumatic brain injuries (TBI) are classified as mild (mTBI), which present heterogeneously. Associations between pre-injury comorbidities and outcomes are not well-understood, and understanding their status as risk factors may improve mTBI management and prognostication. Methods: mTBI subjects (GCS 13–15) from TRACK-TBI Pilot completing 3- and 6-month functional [Glasgow Outcome Scale-Extended (GOSE)] and post-concussive outcomes [Acute Concussion Evaluation (ACE) physical/cognitive/sleep/emotional subdomains] were extracted. Pre-injury comorbidities >10% incidence were included in regressions for functional disability (GOSE ≤ 6) and post-concussive symptoms by subdomain. Odds ratios (OR) and mean differences (B) were reported. Significance was assessed at p < 0.0083 (Bonferroni correction). Results: In 260 subjects sustaining blunt mTBI, mean age was 44.0-years and 70.4% were male. Baseline comorbidities >10% incidence included psychiatric-30.0%, cardiac (hypertension)-23.8%, cardiac (structural/valvular/ischemic)-20.4%, gastrointestinal15.8%, pulmonary-15.0%, and headache/migraine-11.5%. At 3- and 6-months separately, 30.8% had GOSE ≤ 6. At 3-months, psychiatric (GOSE ≤ 6: OR = 2.75, 95% CI [1.44–5.27]; ACE-physical: B = 1.06 [0.38–1.73]; ACE-cognitive: B = 0.72 [0.26–1.17]; ACE-sleep: B = 0.46 [0.17–0.75]; ACE-emotional: B = 0.64 [0.25–1.03]), headache/migraine (GOSE ≤ 6: OR = 4.10 [1.67–10.07]; ACE-sleep: B = 0.57 [0.15–1.00]; ACE-emotional: B = 0.92 [0.35–1.49]), and gastrointestinal history (ACE-physical: B = 1.25 [0.41–2.10]) were multivariable predictors of worse outcomes. At 6-months, psychiatric (GOSE ≤ 6: OR = 2.57 [1.38–4.77]; ACE-physical: B = 1.38 [0.68–2.09]; ACE-cognitive: B = 0.74 [0.28–1.20]; ACE-sleep: B = 0.51 [0.20–0.83]; ACE-emotional: B = 0.93 [0.53–1.33]), and headache/migraine history (ACE-physical: B = 1.81 [0.79–2.84]) predicted worse outcomes. Conclusions: Pre-injury psychiat